SPATIO-TEMPORAL ANALYSIS OF PEDESTRIAN TRAFFIC IN IKEJA AREA, LAGOS BY BASHIRU ADISA RAJI MATRIC NO: 124061 B.Sc. (Hons) Geography and Regional Planning; M.Sc. Transport Studies (Ogun); M.Sc General Management (UNILAG); Advance Diploma Road Traffic Safety (Lund University, Sweden). BEING A THESIS SUBMITTED TO THE DEPARTMENT OF GEOGRAPHY, FACULTY OF THE SOCIAL SCIENCES, UNIVERSITY OF IBADAN, IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF DOCTOR OF PHILOSOPHY DEGREE IN GEOGRAPHY i UNIVERSITY OF IBADAN LIBRARY CERTIFICATION I certify that this work was carried out under my supervision by Mr. Bashiru Adisa RAJI, in the Department of Geography, University of Ibadan. Ibadan, Nigeria. ……………………………. …………………………… Date M. A. O. Ayeni B.Sc. (Hons), Ph.D. (Ibadan) Professor of Geography University of Ibadan, Ibadan, Nigeria. ii UNIVERSITY OF IBADAN LIBRARY DEDICATION This thesis is dedicated to: (i) Almighty God (Allah) (ii) My Mother (Matinée Odjounne Fonglo-RAJI) and late Father (Salami Olajuwon RAJI) whose investments in their children’s educational careers are outstanding. iii UNIVERSITY OF IBADAN LIBRARY ACKNOWLEDGEMENTS When Allah helps and Victory comes (Q110:1) But His Command, when He intended a thing, is only that He said Unto it: Be! And it is. Therefore, glory be to Him in whose hand Is the dominion over all things: Unto Him you will be brought Back: (Q36:82-83). I give all thanks and praises to Allah (Subhannah Wataallah) for counting me worthy of living to carry out this study, and as well enhancing all plans and trips that make up this thesis. I am grateful to the Head of Department of Geography, University of Ibadan Dr. I. O. Adelekan for her contributions towards successful completion of my Doctoral programme. I am highly indebted to my supervisor Professor M. A. O. Ayeni for his critical questioning and lucid comments in the course of writing this thesis. A professor of distinction, a father, a model, a mentor, a teacher, ……., whose fatherly role does not overlook laxity of any form, and whose interest in his students is to be creative and exhibit potentials in both qualitative and quantitative research world. The best we see in you is indelible and immeasurable. My special thanks and appreciation go to Professor A. S. Gbadegesin, Professor S. I. Okafor, Professor C. O. Ikporukpo, Professor A. O. Aweto, Professor C. O. Olatubara, Professor A. S. Aguda, Dr. D. D. Ajayi, Dr. F. O. A. Dada, Dr G. O. Ikwayatum, Dr. O. J. Taiwo, Dr. C. Y. Jaja, Dr. A. O. Fashae, Mr. H. D. Olaniran, Mrs. S. O. Adeleye, Mrs. M. A. Ubani, Mr. O. Olaitan, Mr. A. O. Owopetu, Mr. O. O. Ojo and other members of academic and non- academic staff of the Department of Geography, University of Ibadan, Ibadan, for their encouragement, motivation and inspirational counselling. Special thanks to Professor Serge Hoogendoorn of the Department of Civil Engineering and Geosciences Delft University, Stevinweg, Netherlands for permitting me to access his research site; Professor Sir Alan Wilson of Centre for Advanced Spatial Analysis, University College London for his scholarly publications he sent to me; and Professor Andras Varhelyi for valuable materials he gave to me during my stay in Lund University, Lund, Sweden. iv UNIVERSITY OF IBADAN LIBRARY I am also grateful to Professor O. O. Odugbemi, Professor O. O. Oyesiku, Professor B. A. Badejo and Dr. I. A. Ademiluyi and all my colleagues in the Department of Geography and Regional Planning, Olabisi Onabanjo University, Ago – Iwoye for their immeasurable support. My special thanks to Dr. M. O. Solanke, Dr. H.O. Adedeji, Dr. R. A Asiyanbola, Dr. K. T. Gbadamosi, Dr. C. O. Akanni, Dr. S. B. Osoba, Mr. W. O. Otun, Mr. O. H. Adebayo, Mr. K. Samuel, Mr. O Babatunde, Mr. A. M. Hassan, Mr. A. O Bakare, Mr. M. O. Rufai, Mr. D. Akinmade, Mr. O. A. Oladapo, Mrs. L. B. Bello, Pastor and Pastor (Mrs) O. N. Adesope, Mr. O. O. Ajani, Mr. I. K. Atiku, Mr. B. J. Adelaja and O. R. Adebanjo for their support at all times. I am indebted to Dr. K. A Obasan, Mr. B. I. Ilo, Mr. and Mrs S. O Ashiru. They deserve my appreciation because of their affinities, and moral support towards my educational programme. My appreciation equally goes to my mother, my sisters (Ayissatou, Salamat, Nousirat, Fariyat and Barikat), my brothers (Aliou and Abdul Rachidi) for their moral and financial support. My profound gratitude and sincere appreciation goes to my wife, Basirat Abiodun Temitayo, our daughters – Aisha, Rhadeeyah and Fareedah for their understanding and patience when I have to be away from home and for their marvellous support and for always being there for me. Bashiru Adisa RAJI v UNIVERSITY OF IBADAN LIBRARY TABLE OF CONTENTS Page Title i Certification ii Dedication iii Acknowledgements iv Table of Contents vi List of Tables xiii List of Figures xvi List of Appendices xxi Abstract xxii CHAPTER ONE: BACKGROUND TO THE STUDY 1.1 Introduction 1 1.2 The Research Problem 4 1.3 Aim and Objectives of the Study 7 1.4 Basis for the Choice of the Study Area 8 1.5 Limitations of the Study 9 vi UNIVERSITY OF IBADAN LIBRARY 1.6 Organisation of the Thesis 10 1.7 Summary 10 CHAPTER TWO: CONCEPTUAL FRAMEWORK AND LITERATURE REVIEW 2.1 Introduction 12 2.2 Conceptual Framework 12 2.2.1 Urban Spatial Structure 12 2.2.2 Urban Circulation and Ullman Triad 14 2.2.3 Urban Transport Models 16 2.2.3.1 Trip Generation 19 2.2.3.2 Trip Distribution 20 2.2.3.3 Modal Split Model 22 2.2.3.4 Traffic Assignment Model 23 2.2.4 Behavioural Modelling of Traffic 24 2.2.5 Modelling Pedestrian Traffic 27 2.2.5.1 Macroscopic Approach 27 2.2.5.2 Microscopic Approach 32 2.2.6 Road Networks in Graph Theory and Walking Distance Concept 34 vii UNIVERSITY OF IBADAN LIBRARY 2.2.6.1 Road Networks 34 2.2.6.2 Walking Distance 36 2.3 Empirical Findings of Pedestrian Movement 38 2.4 Gaps in the Literature 41 2.5 Hypotheses of the Study 42 2.6 Summary 43 CHAPTER THREE: METHODOLOGY 3.0 Introduction 45 3.1 Research Design 45 3.2 Types of Data and Method of Data Collections 51 3.2.1 Secondary Sources 51 3.2.2 Primary Sources 52 3.2.2.1 Questionnaire Survey 52 3.2.2.2 On-street Interview and Observational Survey 54 3.2.2.3 Field Measurement 54 3.2.3 Sample Size 54 3.2.4 Questionnaire Administration and Sampling Procedure 57 viii UNIVERSITY OF IBADAN LIBRARY 3.3 Measurement of Variables and Techniques of Analysis 57 3.3.1 Measurement of Variables 57 3.3.2 Techniques of Analysis 58 3.4 Summary 60 CHAPTER FOUR: PHYSICAL, SOCIO-ECONOMIC CHARACTERISTICS AND MOVEMENT PATTERNS OF PEDESTRIANS 4.1 Introduction 61 4.2 Ikeja and the Study Area 61 4.3 Land Use Pattern in the Study Area 67 4.3.1 Residential Landuse 67 4.3.2 Industrial Landuse 70 4.3.3 Commercial Landuse 70 4.3.4 Social Infrastructure 74 4.4 Pattern of Pedestrian Movement in the Study Area 77 4.4.1 Transport Activities 78 4.4.2 Pedestrian Movement in the Study Area 83 4.4.3 Pedestrian Flow Patterns in the Study Area 92 4.5 Summary 103 ix UNIVERSITY OF IBADAN LIBRARY CHAPTER FIVE: PEDESETRIAN WALKING DISTANCES AND TRIPS GENERATED IN IKEJA 5.1 Introduction 105 5.2 Incidence of Pedestrian Trips 105 5.3 Pedestrian Trip Purposes 106 5.4 Pedestrian Walking Distances 116 5.4.1 Walking Distances to Bus Stations 117 5.4.2 Walking Distance to Landuse Activities 121 5.4.3 Pedestrians Maximum Walkable Distances 129 5.5 Determinants of Pedestrian Trips Generated 133 5.6 Summary 139 CHAPTER SIX: PEOPLE’S DECISION TO WALK AND PEDESTRIAN LEVEL OF SAFETY IN IKEJA 6.1 Introduction 141 6.2 Factors Influencing Decision to Walk 142 6.3 Pedestrians Choice Variables of Preferred Walkways 154 6.4 The Environment for Walking and Pedestrian Level of Safety 163 6.4.1 Factors Affecting Pedestrian Walking Environment 164 x UNIVERSITY OF IBADAN LIBRARY 6.4.2 Development of Pedestrian Level of Safety Model in the Study Area 165 6.4.2.1 Presence of a Sidewalk and Lateral Separation 168 6.4.2.2 Motor Vehicle Volume 177 6.4.2.3 Effect of Speed 178 6.4.2.4 Driveway Access Frequency and Volume 178 6.4.3 Pedestrian Level of Safety Model Results 179 6.5 Summary 182 CHAPTER SEVEN: CONCLUSION 7.1 Summary of Findings of the Study 186 7.2 Conceptual and Theoretical Contributions 190 7.3 Implication of findings to Planning 193 7.4 Further Research Needs 198 7.5 Conclusion 201 REFERENCES 205 APPENDICES 220 xi UNIVERSITY OF IBADAN LIBRARY LIST OF TABLES Table No. Page Table 3.1: Summary of the Sample Size and Questionnaires Administered. 47 Table 4.1: Estimated Number of Economic / Land Use Activities in the Study Area. 71 Table 4.2: Land use Types of the Seventeen Zones Understudy. 76 Table 4.3: Pedestrian Circulation in the Study Area. 93 Table 4.4: Variations in Pedestrian Hourly Flow across Zones. 102 Table 5.1: Male and Female Household Heads and On-Street Persons that Walk and Do Not Walk Frequently in the Study Area. 108 Table 5.2: Pedestrian Trip Purposes in Ikeja. 112 Table 5.3: Average Walking Distances of Household Heads and On-Street Persons to Bus Stations in Ikeja. 118 Table 5.4: Average Walking Distances of Household Heads and On-Street Persons to Landuse Activities in Ikeja. 123 Table 5.5: Recommended Walking Distances to Public Transport Stops and Local Facilities. 128 Table 5.6: Variations and Difference in Pedestrian Walking Distance to Bus Stations and Land Use Activities in Ikeja. 131 Table 5.7: Description of Explanatory Variables Used For the Number of Pedestrian Trips Generated in Ikeja. 135 Table 5.8: Relationship between Number of Pedestrian Trips Generated by Household Heads and On-Street Persons’ in Ikeja. 137 xii UNIVERSITY OF IBADAN LIBRARY Table 6.1: Description of the Explanatory Variables Used for Household Heads and On- Street Persons’ on Decision to Walk.in Ikeja. 143 Table 6.2: Summaries of Exploratory Data of Household Heads and On-Street Persons’ on Decisions to Walk in Ikeja. 147 Table 6.3: Relationship between Household Heads and On-Street Persons’ Decision to Walk in Ikeja. 148 Table 6.4: The Pairwise Combination Scale of Analytical Hierarchical Process. 156 Table 6.5: Pairwise Comparison Matrix Generated for Households and On-Street on Persons’ on Preferred Nature of Pedestrian Facility in Ikeja. 157 Table 6.6: Weighted Preferred Nature of Pedestrian Walkways in Ikeja 159 Table 6.7: Summary of Ranking of the Results Generated for Households and On-Street Persons’ Preferred Nature of Pedestrian Facility in Ikeja. 161 Table 6.8: Recommended Level of Service Threshold for Free Flow Movement on Pedestrian Walkways. 166 Table 6.9: Walkway Level of Service (LOS) Thresholds by Available Space per 2 Pedestrian (m /Ped). 167 Table 6.10: Relationship of Pedestrian Level of Safety along Road Segments in Ikeja. 180 Table 7.1: Classification of Pedestrian Types. 200 xiii UNIVERSITY OF IBADAN LIBRARY LIST OF FIGURES Figure No. Page Figure 2.1: Models of Internal Structure of Cities. 13 Figure 2.2: The Four-Stages of Urban Transportation Modelling. 16 Figure 2.3: Individual Travel Demand Forecasting Process. 17 Figure 2.4: Automatic Microscopic Data Collection in Pedestrian Studies. 27 Figure 2.5: Walking Distance Scheme of Swedish Public Transport Association. 36 Figure 3.1: The Study Area. 46 Figure 3.2: The Delineation of the Study Area into Zones. 47 Figure 3.3: Schematic Presentation of the Research Process of the Study. 48 Figure 4.1: Political Map of Lagos state showing Ikeja Local Government Area. 62 Figure 4.2: Map of Ikeja Local Government Area of Lagos. 63 Figure 4.3: Map of the Study Area. 65 Figure 4.4: Landuse Pattern of the Study Area in Ikeja. 68 Figure 4.5: Landuse Map of the Study Area. 69 Figure 4.6: Landuse Types in the Study Area. 73 Figure 4.7: Road Network Map of Ikeja Local Government Area. 80 xiv UNIVERSITY OF IBADAN LIBRARY Figure 4.8: Road Map of the Study Area in Ikeja Local Government Area. 81 Figure 4.9: Road Map of the Study Area. 82 Figure 4.10: Average Pedestrian Flow per Hour along the Road Networks in the Study Area . . 94 Figure 4.11: Pedestrians Zonal Average Flow in Ikeja. 96 Figure 4.12: Pedestrians Average Street Flow in Ikeja. 97 Figure 4.13: Pedestrians Hourly Flow in Ikeja. 99 Figure 4.14: Pedestrian Average Hourly Flow in Ikeja. 100 Figure 5.1: Household Heads and On-Street persons’ Walk Trip Frequency in Ikeja. 107 Figure 5.2: Male and Female Respondents That Do Walk and Do Not Walk Frequently in Ikeja. 110 Figure 5.3: Household Heads and On-Street Persons Walk Trips in Ikeja. 114 Figure 5.4: Household Heads and On-street Persons Walking Distances to Bus Stations in Ikeja. 120 Figure 5.5: Household Heads and On-Street Persons Walking Distances to Landuse Activities in Ikeja. 126 Figure 6.1a: Pedestrian Walking Alongside Vehicle on the Outside Lane. 169 Figure 6.1b: Pedestrian Separated from Outside Lane by Buffer Zone. 169 Figure 6.2a: On-Street Parking within Roadside Buffer Zone. 170 Figure 6.2b: Drainage or Swade within Roadside Buffer Zone. 170 xv UNIVERSITY OF IBADAN LIBRARY Figure 6.2c: Trees within Roadside Buffer Zone. 171 Figure 6.2d: An illustration of a roadway in Ikeja. 171 Figure 6.3: Quantification of Lateral Separation Elements. 175 xvi UNIVERSITY OF IBADAN LIBRARY LIST OF PLATES Plate No. Page Plate 4.1: Pedestrians Walking in the Middle of Ola-Ayeni Street in Ikeja. 84 Plate 4.2: Parked Vehicles and Trading Activities characterise Oriyomi Street. 85 Plate 4.3: Unguided Movement of Pedestrians at a Section of Awolowo Way. 86 Plate 4.4: Pedestrians Walking by the Roadside along a Section of Awolowo Way. 87 Plate 4.5: Walkway with Grown Weeds along WEMPCO Road in Ogba Area. 88 Plate 4.6: Covered Drainage Used for Pedestrian Walkway along Lateef Jakande Road .89 Plate 4.7: Pedestrians Avoiding Uncovered Drainage Along Oba Kodesoh Street. 90 Plate 4.8: Congested Otigba Street with Pedestrians Competing with Vehicles. 91 Plate 6.1: Pictorial Presentation of figure 6.1a in many road segments in Ikeja. 173 Plate 6.2: A Roadway in Ikeja with a Divide but Lack Buffer and Walkways. 174 xvii UNIVERSITY OF IBADAN LIBRARY LIST OF APPENDICES Appendix No. Page APPENDIX I: Households’ Trip Diary in Ikeja area of Lagos. 220 APPENDIX II: On-street Persons’ Trip Diary in Ikeja area of Lagos. 230 APPENDIX III: Pedestrians Circulation on Road Networks in the Study Area 240 APPENDIX IV: Household Heads and On-Street Persons Walking Distances to Bus Stations in the Study Area. 252 APPENDIX V: Household Heads and On-Street Persons Walking Distance Landuse Activities in the Study Area. 254 APPENDIX VI: Regression Analysis Results of the Relationship between Household Heads and On-street Persons Pedestrian Trips Generated. 256 APPENDIX VII: Software Procedure of CGI Analytical Hierarchical Process 257 APPENDIX VIII: Road Segment Number, Level of Service, Pedestrian Volume, Lateral Separation, Motor Vehicle Volume, Speed of Motor Vehicle and Driveway Access and Volume in the Study Area. 258 APPENDIX IX: Logarithm of Pedestrian Volume, Lateral Separation, Motor Vehicle Volume Speed of Motor Vehicle and Driveway Access Volume. 260 xviii UNIVERSITY OF IBADAN LIBRARY ABSTRACT In spite of the fact that vehicles and pedestrians constitute important urban traffic, conceptual explanations and investments have focused more on vehicular than pedestrian traffic and activities. This study was carried out to examine pedestrian volumes and patterns of flow, factors influencing decision to walk, and pedestrian level of safety in Ikeja, Lagos. Data on the socio- economic characteristics, landuse and street maps of Ikeja were collected from relevant ministries. Seventeen landuse zones were identified in order to examine the spatial variations in landuse type, pedestrian traffic and related activities. Buildings on identified streets were systematically selected. Random sampling was used to select heads of households in selected buildings and pedestrians. Between May and August 2009, data were collected through a questionnaire survey administered to a total of 1,205 respondents. Vehicle speed, road width and number of pedestrians that walkway width carried were recorded along all the 56 streets. Mean and standard deviation were used to present results on pedestrian traffic and walking distances; t-test and analysis of variance were used to measure respondents’ walking distance to facilities. Multiple regression was used to evaluate frequency of pedestrian trips and level of safety on roadways; logistics regression was used to estimate the decision to walk, and analytical hierarchical process was used to rank respondents choice of walkways. An average of 56,663 pedestrians walked along all the streets between 7:00am and 7:00pm; and flow pattern varies significantly ( across zones. The highest average hourly flow of pedestrians ( ̅=6,313±6,765.04) on road networks across zones was observed between 5:00-6:00pm and the lowest ( ̅=1,788±2,277.72) was observed between 7:00-8:00am. Residential landuse (28.1%) and commercial landuse (27.7%) were the most prominent landuse types while financial landuse (10.9%) and industrial landuse (8.0%) were discreet. From 7:00am to 7:00pm, Otigba zone with highest commercial activities (48%) recorded the highest average pedestrian traffic ( ̅=18,791±5,445.59) and Mobolaji Johnson zone with the lowest commercial activities (5%) recorded the least ( ̅=509±182.46). The mean walking distances by household heads to bus stations and landuse activities was 0.244±0.02km while that of pedestrians was 2±0.10km which was significant (t=71.01, p=≤0.05). Trips to work (30.4%); religious centres (20.5%) were the most important trip xix UNIVERSITY OF IBADAN LIBRARY composition while social trips (3.3%) and visit to friends (1.3%) trips were the least. Work trips (β=0.35, t=3.82) and trips to fast food points (β=0.29, t=2.63) were the significant (β) factors explaining frequency of pedestrian trips. Respondents’ decision to walk (e =1.81) under cool weather was almost twice than when it was hot. Female respondents walk (β) (e =1.02) 1.02 times more than their male counterparts. Safety on walkways ranked highest (λ=0.44) while congestion on walkways (λ=0.04) ranked lowest in the choice of walkways. Pedestrians’ level of safety increased with distance of walkways from moving vehicles (β1=0.60, t1=5.14), but decreased with higher vehicular volume (β2=0.20, t2=1.73) and speed (β3=0.07, t3=0.76). There was a limited distance over which household heads could walk this encourages driving close to facilities thereby, creating congestion and parking problems. Increased investment through more pedestrian friendly roadways in central business districts of urban centres would enhance pedestrian mobility and safety. Keywords: Pedestrian Traffic, Safety of Walkways, Urban Centres. Word count: 499 words. xx UNIVERSITY OF IBADAN LIBRARY CHAPTER ONE BACKGROUND TO THE STUDY 1.1 INTRODUCTION Human movements in cities over the world are made possible by transport, which provide vital clues to the understanding of human spatial behaviour. Particularly significant in urban analysis are the day-to-day movements of people, because they represent both functions and processes (Ayeni, 1979; Axhausen and Garling, 1992; Hoyle and Knowles, 1998). They are functions because of spatial relations of different parts of the city they maintain, and they are processes when changes in their volume, intensity and direction come to determine the pattern of growth and organization of the spatial structure of the city. For the fact that cities consist of spatially separated and highly specialized land uses; such as commercial, industrial, institutional and so on, people must move to obtain goods and services. In many countries, residential areas and places of other activities such as work, school, recreation, market, religious camps, and so on are no longer close. People can now choose to live long distance from places of work, school, religious camps and travel everyday using different modes of transport. Transport therefore is highly significant in the existence of urban areas; and also creates demand for it (Hutchinson, 1974; Marlock, 1978; Ojo, 1990; Oyesiku, 2002; Solanke, 2005). Road transport comprises of both motorized and non-motorized mode. While motorized mode includes cars, trucks, buses, motorcycles, and so on, non-motorized include pedestrians and bicycling. Pedestrian as defined by Australian Pedestrian Council (2004) is “any person wishing to travel by foot, wheel chair, or electric scooters, throughout the community. James and Walton (2000) further observed that pedestrian movement to transport experts is a mode of travel taken to access certain destinations on foot. 1 UNIVERSITY OF IBADAN LIBRARY For several years, urban design and city planning communities all over the world have struggled with the challenge of the walking environment in a car-dominated landscape. Many designers of infrastructure planning work from an engineering paradigm that might not have fully recognized human experience as part of the design equation (James and Walton, 2000). Consequently, researches have focused on motorized mode to the exclusion of non-motorized (pedestrian and cycling). A key search of the engineering INSPEC ( a major database for scientific and technical literature) shows a 53 times or more articles published since 1969 with „vehicles‟ in the titles than „pedestrians‟ a count for about 10,211 and 192 respectively (Desyllas, Duxbury, Ward. and Smith, 2003). With rising dependency on the automobile and the disparity in research between motorized and non-motorized modes, motorized modes became ingrained in nations‟ transport policy (Hillman and Whalley, 1979; Brog and Erl, 2001; Gem Zoë, 2001; Desyllas, Duxbury, Ward. and Smith, 2003; Tight, Kelly, Hodgson, and Page 2004; Kim, 2005). But many nations particularly developing countries do not recognize the significance of pedestrian movement and the need for their infrastructural provision. Hence, there is no comprehensive transport policy that caters for this mode. Studies have shown that low and middle-income earners and people with certain cultural background are found to engage in walking (Gunnay, Harvey, Woodside, and Vaganay, 2004; Rahman, 2007). In Nigeria, with diverse cultural background, there is tendency that significant number of Nigeria population will walk, or embark on a walking distance greater or less than 3.2 km propounded by Fruin (1971) in the United States of America to areas of socio-economic activities. Furthermore, researches have also shown that the quality of footpaths or walkways and other pedestrian facilities on roads influence the decision to walk (Hass-Klau., Dowland, and Nold, 1994; Gehl, 1999; Association of Pedestrian Council, 2001). There are evidences in cities such as Gothenburg, Lund and Malmo (Sweden), York and Central London (UK), Portland (USA), and Copenhagen in Denmark. In Nigeria, for instance, many city roads lack walkways and other pedestrian facilities. Where these facilities are available (in form of covered drainages), they become 2 UNIVERSITY OF IBADAN LIBRARY spaces for street trading, on- street parking and avenue to generate revenue for Local Government Authorities who in some other cases allocate the spaces to traders, who later erect kiosks on them. Pedestrian facilities also serve as refuse sites, and home to destitute and robbers. During vehicular traffic peak periods, available walkways for pedestrians turn to motorways for motorcycles and tricycles, thereby exposing pedestrians to danger. The reasons why people choose to walk and the physical factors that influence their decisions vary over space. These factors according to the Association of Pedestrian Council (2001), Rahaman (2007), Tight, Kelly, Hodgson, and Page (2004) and Boon, Tong and Olszewski (2005) include age, sex, level of income, location, weather, distance, season, time, safety, security, dirtiness of walkways, continuity of walkways, cohesiveness of walkways, statistics of which can hardly be found in Nigeria due to non-availability of data. The need to understand the way people move in urban centres leads to the desire to predict their movements in order to assist planners in: (i) optimum location of facilities; (ii) allocation of staff to manage emergency services; (iii) organizing street festivals or religion camps;(iv) designing road network; and (v) pedestrianizing some urban central business districts. In developing countries particularly Nigeria, the transportation system needs continuous evaluation. Achieving this requires designing efficient models or the use of predictive models in explaining pedestrian trips. Furthermore, researching into pedestrian movement in our urban areas will assist various local, states, and the federal governments in: (i) Incorporating pedestrian mode of movement into national transport policy; (ii) Incorporating pedestrian mode into on-going land development process in the urban centres; (iii) Integrating pedestrian infrastructures and facilities with existing mode of transport; (iv) Focusing on areas of density, land use, and network connectivity in urban centres so as to improve the potential for pedestrian travels; 3 UNIVERSITY OF IBADAN LIBRARY (v) Identifying areas in urban areas where pedestrian activities predominate; already take place at high level; (vi) Linking priority investment in pedestrian facilities to the areas where high usage will provide greater justification for the investment especially in relation to other transportation areas; and (vii) Identifying urban centres that may likely receive a higher return for their effort to plan, develop and fund pedestrian facilities. At this juncture, the need for pedestrian study in urban centres cannot be overemphasised. 1.2 THE RESEARCH PROBLEM Movement of people and information has continuously been fundamental components of cities. Cities, people and their activity patterns therefore revealed themselves in transport flow and physical infrastructure that supports them (Wilson, 1972; Ayeni, 1992; Rodridge, 1998). With growing urban population and increasing vehicular volume, many cities are facing mobility and accessibility problems due to heavy traffic congestion, vehicular pollution and other environmental related issues. In order to address this situation, various governments have become more supply oriented and thus pre-occupied with building more roads, flyovers and they have completely neglected other means of transportation (Oni, 1992; Raji and Otun 2008). Transport does not only mean „to take or carry people and goods‟ from one place to another by means of vehicle, aircraft or ship but also by foot (Stradling, Meadows and Beatty, 2000). Stradling, Meadows and Beatty (2000) further observed that people‟s reduction in the use of private vehicles involve users changing their current patterns of life, and the main alternative to the use of private vehicles for short and unburdened trips is by walking. Walking as a component of non-motorized transport is an ancient urban transport mode in both developed and developing countries. In recent times, around 25% of all trips and about 80% of trips under a mile in length are made on foot in the UK (DETR, 1999; National Statistics, 2001; DFT, 2003; Desyllas, Duxbury, Ward. and 4 UNIVERSITY OF IBADAN LIBRARY Smith, 2003; ITS, 2004; Buchanan, 2005). In the United States of America about 100 million Americans do not drive, and about 10% of all trips are made on foot (FHA, 1971; Highway Statistics, 2001).In India and Dhaka city of Bangladesh, 60% of all urban trips depend on walking (Rao and Sharma, 1990; Rahaman, 2007), Maunder (2002), Howe and Dave (2002) observed that the most prevalent mode of transport in many African countries in recent time is walking. In Nigeria, for instance, it accounts for more than 80% of short-distance trips that take place in urban centres (Arosanyin and Ipingbemi, 2004). Walking is the most primordial means of transport, but in this automobile age, it is the most neglected. Many researchers (Daniels and Warnes, 1980; Adeniji, 1981; 1985; 1986; 1991; Oyesiku, 1990; Ojo, 1990; Badejo, 1993; Ogunsanya, 1993; 2002; Adeniji-Soji, 1995; Okoko, 2002; Solanke, 2005), while tabulating modal split of any city; often miss out or focus less on the share of pedestrians. Furthermore, various governments‟ attention has been focused on public transport systems that would move people but pedestrians who form the prominent ingress and egress to the mass transportation system point have been ignored and either without or with little investment (Raji, 2010). Even when pedestrians have perhaps been the most neglected of the transport system, cities have not braced up to the needs of pedestrians in the central areas. An important area or aspect of the overall issue in urban traffic problems is the set of problems that arise in relation to pedestrian trips.Trip analysis practices centrally focus on the problem of congestion, and on the construction of highways in its mitigation. Furthermore, parts of trip surveys have also shown that walking or pedestrian trips account for only 15% of the main mode split creating perceptions that trip by foot is relatively unimportant. As Brög (2001) noted; "Walking is ignored in transport policy and planning because it is often not considered in traditional transport models. But even if it is included in behavioural transport surveys, the methods applied are very often inadequate and insufficient to show its relevance for everyday mobility. And from this neglect ………walking is underestimated for transport needs and in town planning." 5 UNIVERSITY OF IBADAN LIBRARY Most urban planners particularly in developing economy, recognize the need for integrated urban transportation. However, they fail to recognize (i) the differential impact of various transport modes on the general qualities of urban environment;(ii) the unequal impacts of transport investment on the access of various socio-economic group of employment opportunities and to educational and other types of community facilities; (iii) the impact of changes in accessibility on the spatial distribution of urban activities; (iv)the uncertainty under which transport investments are made and the fact that they are made sequentially over a number of years and (v)the relationship of the financial resources required by recommended plans to the resources required by other public sectors. Furthermore, the urban planners who must regulate and manage the transportation system in urban areas have little or no knowledge of modelling tools that can help in understanding pedestrian flows and trips. Few empirical studies have shown that many questions regarding pedestrian trips have been addressed poorly, among of which are: (i) What factors are most important in individual decision to walk? (ii) Which measures are successful in encouraging walking? (iii) What are the maximum distance pedestrians are willing to walk? (iv) What factors influence pedestrian number of walk trips? (v) What are the factors that determine pedestrian level of safety and comfort on roadside environment? As at late 1996, Department of Transport (1998) in the UK observed that solution to the questions raised above remain poorly understood and the picture has changed little today, particularly in most developing countries. For example, in Nigeria, cities and towns are growing and expanding without concomitant response to road transport infrastructure provision, the pedestrian aspect of these infrastructures have been ignored and as well used for other purposes. In Lagos for instance, there is heavy vehicular traffic and motorists experience difficulties to move and park their vehicles. Consequently, cars are parked in a disorderly manner along sidewalks, street corners and thresholds creating serious 6 UNIVERSITY OF IBADAN LIBRARY bottlenecks and traffic congestion within the city. These difficulties are more pronounced in the city‟s central business district. The phenomenon also presents itself in suburban business centers as well. In relation to these problems is the demand for pedestrian trips, non-availability of pedestrian facilities, and non-availability of pedestrian trips information as well as model that can assist in the decision processes. The level of poverty and economic decline in the country has further made pedestrians vulnerable to attack and robberies. This has made the use of pedestrian facilities more dangerous and probably not encouraging. There is also lack of empirical knowledge regarding information about pedestrian activities under varying circumstances that require model-based approach. It is against these backdrops that this study seeks to address questions raised, and to provide further understanding by analysing pedestrian trips, with the view at evolving effective and efficient patterns of pedestrian trips in Ikeja area of Lagos, and thus, contribute to the literature on pedestrian trips in developing countries. 1.2 AIM AND OBJECTIVES OF THE STUDY The aim of this study is to examine the fundamental developments that explain spatio- temporal analysis of pedestrian traffic in Ikeja area of Lagos State, Nigeria; with a view to identifying, understanding and explaining the processes and patterns associated with pedestrian trips in urban areas. The specific objectives are to: 1. Examine pedestrian walking distance and walking limit to various functions, services and bus stations or terminals in the study area. 2. Examine pedestrian trip frequencies in relation to their trip types, economic activities and level of accessibility. 3. Examine the decisions that enable people to walk. 4. Examine pedestrian movement in relation to pedestrian level of safety. 7 UNIVERSITY OF IBADAN LIBRARY 1.4 BASIS FOR THE CHOICE OF THE STUDY AREA Pedestrian activities occur all over Nigeria, but they are more noticeable in urban areas. In Nigeria, urban centres can be found in the 36 states and the Federal Capital Territory, Abuja. This study therefore can be carried out in any Nigerian city, but Lagos is chosen for the study. Lagos stands out as one of the 36 states in Nigeria with the highest concentration of industries and commercial activities with explosive population. The attribute of Lagos state as the commercial nerve of the country, also places her as the most chaotic in terms of pedestrian and vehicular traffic (Raji and Otun, 2008; Raji,2010). Lagos is th also rated as the second fastest growing city in Africa and 7 in the world (Wikipedia, 2009). However, Ikeja, one of the local government areas in Lagos state is most preferred for this study over other urban areas in the state because of: (i) its role as the capital city of Lagos state and the seat of the local council till date, (ii) it is inhabited by people of all sub-ethnic groups in Nigeria and West African states; (iii) it is one of the vibrant Central Business Districts in Lagos State and Nigeria., (iv) it is known for its transit activities, (v) it houses both local and international airports, (vi) it is known for commercial, residential, financial, institutional, trading, shopping mix , religious activities, and so on, (vii) it is known to be one of the major market areas for information technology that attracts people within and outside the country, (viii) it is ranked highest with 35.7% in terms of socio-economic characteristics and development among the Local Government Areas in Lagos State, and (ix) the possibilities of Ikeja being the destinations of many people coming from abroad, north, east, west and south of the urban area. 8 UNIVERSITY OF IBADAN LIBRARY A look at the high intensity of businesses and commercial activities in Ikeja local government area of Lagos, and its roles as the capital of the state and local government headquarter, the amount of pedestrian and vehicular traffic generated daily in and out of Ikeja, has made pedestrian and vehicular congestion a recurrent problem in the area. Parking problems is clearly prominent while pedestrian circulation during the day is critical. However, the study area is part of Ikeja, and it is the core economic area of Ikeja. The boundary is defined by major road corridors in the part of Lagos State. The western boundary is bounded by Lagos –Abeokuta and Agege Motorways, bounded in the east by Lagos – Ibadan Expressway, in the south by Mobolaji Bank Anthony way, and in the north by WEMCO Road. 1.5 LIMITATIONS OF THE STUDY A number of problems are obvious in a research that concerns itself with examination of households, On-street persons (pedestrians found on the streets), and sequence of pedestrian activities. In developed economies, information on travel activities is available at the door steps of agencies such as Land Use Administration, National Household Travel Survey, and Association of Pedestrians Council. Information about travel behaviour can be collected from these institutions by picking travel diaries of individuals, which may be complemented with self-administered questionnaire and observational study. Obtaining travel diaries of households and on-street persons in the study area was not possible through government agencies, hence, the need to use self-administered questionnaires. Although, the respondents in the study area were educated, almost all the respondents sampled did not keep travel diaries. Furthermore, getting information from household respondents required daily visits by the enumerators, and the visitation lasted several weeks before this could be achieved. This involved altering the time frame for data collection and incurring additional costs. Acquiring information from on-street persons is not an easy task either; ever busy and impatient on-street persons ignored the enumerators, particularly the male 9 UNIVERSITY OF IBADAN LIBRARY enumerators. The restructuring and on-going developmental projects in Lagos State also contributed to the problems encountered. Majority of the respondents (particularly, household respondents) are not friendly because, they believe the enumerators could be working for the state government and information obtained from them might be used against them. They were of the opinion that the information was meant for building tax, and some of the household respondents did not return their questionnaires. The streets map obtained from Lagos State Physical Planning Department was not detailed and some of the streets names shown on the map did not match field observations. Consequently, the streets map had to be updated. Taking measurements of road segments in the study area was also very tedious and risky. Apart from the exposure to road traffic accident, the tapes used for measuring the road segments were damaged by passing vehicles during the field survey. In spite of the problems encountered before, during and after the field survey, substantive information required for analysis for the thesis were retrieved from household respondents and on-street persons‟ interviewed. 1.6 ORGANISATION OF THE THESIS Apart from chapter one which provides background to the study, there are six other chapters. Chapter two discusses the theoretical framework and literature review. Chapter three focuses on the methodology. Chapter four examines physical, socio- economic characteristics and movement patterns of pedestrians; chapter five discusses pedestrian walking distances and trips generated; chapter six examines people‟s decision to walk and pedestrian level of safety in Ikeja and the concluding part of the thesis was discussed in chapter seven. 1.7 SUMMARY The chapter introduces the study by examining travel activities and the significance of the research in an urban area. The chapter also discusses the research problem by arriving at questions relevant to the study that are not well understood in the literature 10 UNIVERSITY OF IBADAN LIBRARY and particularly in Nigeria. Achieving the aim of the study requires and exploration of concepts, theories, models and methods that explain the drive of the thesis. These are discussed in the next chapter. 11 UNIVERSITY OF IBADAN LIBRARY CHAPTER TWO CONCEPTUAL FRAMEWORK AND LITERATURE REVIEW 2.1 INTRODUCTION Human movements, particularly pedestrian trips have evoked several ideas and thoughts all over the world. Accordingly, there have been concepts, theories and models on the processes that underlie these ideas and thoughts. Among these concepts, models and theories that are significant to human movements (particularly pedestrians) are: Models of the Urban Spatial Structure, Models explaining Urban Circulation and the Ullman Triad. There are also Urban Transport Models, Behavioural modelling of Urban Traffic, Modelling Techniques of Pedestrian Traffic, Road Networks in Graph Theory and Walking Distance Concept. The chapter also reviewed empirical works in pedestrian movement, and as well examined the gaps that exist in the literature. 2.2 CONCEPTUAL FRAMEWORK 2.2.1 Urban Spatial Structure Human movements in urban areas are consequences of spatial imbalance created by urban land use types. Urban spatial structure therefore, emerged along two different lines of survey. The first, which is relevant to this study, is characterized by classical theories, which give details about economic and human ecological patterns of land use organization through three major theories: The Concentric Zone Theory (Burgess, 1925); the Sector Theory (Hoyt, 1939) and the Multiple Nuclei (Harris and Ullman, 1945). Burgess (1925) theory of “concentric zones” examines the expansion of city by explaining the processes of urban metabolism and mobility that revolved around a single centre; the Central Business District (CBD) (Figure 2.1a). Hoyt (1939) provided alternative explanations by describing urban expansion as „axial growth‟, pushing out from the centre along transport lines. He argued that different income 12 UNIVERSITY OF IBADAN LIBRARY Figure 2.1: Models of Internal Structure of Cities Source: Palen (1981) „The Urban World‟ adapted from Oyesiku (2010). 13 UNIVERSITY OF IBADAN LIBRARY groups tend to live in distinct areas and major lines of transportation constitute lines of least resistance for growth as well being important arteries along which similar types of land uses are situated (figure 2.1b). Nevertheless, mono-centric assumption was a major criticism of Burgess (1925) and Hoyt (1939) and this necessitated the proposition of multiple nuclei theory by Harris and Ullman (1945) (figure 2.1c). Harris and Ullman (1945) observed that the land use of a city is built around several discrete nuclei rather than a single nucleus as postulated by Burgess (1925) and Hoyt (1939). They observed that nuclei are pre-existing agglomerations which become urban nuclei as areas between them are filled through urban growth, or new centres emerging from the need for certain types of services as the size of urban areas increases. Because of different origins, the functions performed by these nuclei differ from city to city. Theories of urban spatial structure provide basis for urban mobility in the city. They also provide explanations to location behaviour of households and groups (Ayeni, 1979; Aluko, 2004). The relevance of these theories to pedestrian movements is based on their trip generating capability. City centres are focal point of socio-economic activities and because of their potential trip attractions, different land use types generate varying pedestrian trips within and outside residential, commercial, industrial, institutional and recreational zones. 2.2.2 Urban Circulation and the Ullman Triad Human movements in urban areas are processes that extended over time and space. These processes which examine routes, modes and speed are called travel, and may be classified into radial, circumferential, residential, travel to and from major activities and travel within the central business district (Hutchinson, 1974; Abler, Adams and Gould, 1977; Adeniji, 1984). The inputs to an urban transport system are the demands for the movements of person and goods between activity centers. These inputs are of two dimensions, (i) the spatial patterns of travel demand that exists throughout an urban region and (ii) the times throughout the day at which the dominant spatial patterns of demand occur (Hutchinson, 1974). A major output of the urban transport system is the travel times 14 UNIVERSITY OF IBADAN LIBRARY that it produces for movement between various parts of an urban region. The magnitude of this output depends on the size of the travel demand and the capabilities of various links of the network. Urban transport system also produces indirect (or secondary) outputs such as the impacts that the transport system has on the spatial distribution of urban activities. However, human movements in urban areas are made up of a number of different trip types that have specific spatial and temporal characteristics. In urban transport analysis, the trip types studied in a particular area depend on the types of transport – planning issues at hand (Solanke, 2005). All movements in urban areas are basically point-to-points movements originating from a set of origins and ending at a set of destinations. It is also possible to resolve these mass movements into individual movements from point to point. Thus, point – to – point movement, leads to trip chaining and also serves as foundation for all kinds of movements. So, the spatial structures of the origins and destination points are important determinants of the structure of passenger movement systems and also contributed to spatial interaction theory postulated by Ullman (1956). Spatial interaction theory as postulated by Ullman (1956) was based on three principles namely; complementarity, intervening opportunity and transferability (the Ullman triad). Complementarity means areal differentiation and the existence of supply and demand in different areas while intervening opportunities set up constraint as to the possibility of interaction. The argument being put forward is the fact that even when there is a supply in an area and a demand in another, interaction would only take place if there are no alternative sources of the same material (Ayeni1974; 1979; Ojo, 1990).Transferability is the ease with which demands are met. In fact, it is a distance issue measured in terms of transfer and costs. Hence, Complementarity may generate interaction but the factor of intervening opportunity brings about areal substitution and transferability factor results in substitution of products (Ullman, 1956) Spatial interaction theory has offered some explanations to the issue of interaction in urban centers. The day-to-day movements of people to places of work, markets, 15 UNIVERSITY OF IBADAN LIBRARY shopping centres, religious camps, recreation and to school, offer explanations to movement in urban areas. In spite of the significance of Ullman‟s theory in explaining spatial interaction, it is rather insufficient in explaining complex interactions in contemporary world due to the dynamics in technology and human activities. Therefore, in the determination of the outcomes of urban transport system, it has been observed that models cannot be the only aid to understanding a complex process but can also serve as a measure of its effectiveness (Thomas and Hugget, 1980; Ayeni, 1979; 1992; Salter 1983). In the last three decades, series of models have been developed in explaining movements in urban areas. . These models which help in forecasting urban trips are discussed in the section that follows. 2.2.3 Urban Transport Models Models symbolize reality and allow further study, analysis, evaluation and manipulation of systems of interest. Mathematical models therefore, serve as functional mechanism for, planning, evaluating and understanding various strategies of urban growth (Ayeni, 1979; 1992). However, the issue of how trips take place involves travel choices of individuals. As most commonly practiced in the world over, trip issues may be represented by “four step process” as shown in figure 2.2 and figure 2.3. These four steps are supposed to represent the thought process of individual, because, individual makes four travel decisions as follows: (i) the decision that a trip is necessary to fulfil some need or purpose (generation), (ii) the decision where that need or purpose is best fulfilled (distribution), (iii) the decision of which means is best to get there (mode choice) and (iv) the decision of which route to take (trip assignment) (Wilson,1974; City of Rifle Transportation Master Plan, 2007). These basic analogies of the traditional 4-stage transport models are discussed below. 16 UNIVERSITY OF IBADAN LIBRARY Trip Generation Trip Distribution Modal Split Traffic Assignment Figure 2.2: The Four-Stage of Urban Transportation Modelling. Source: Paulley (2001) 17 UNIVERSITY OF IBADAN LIBRARY Urban Activities Trip Frequency Destination Home Based, Choice Non-Home Based Mode Choice Roadway Waterway Walkway Railway Figure 2.3: Individual Travel Demand Forecasting Process. Source: Author‟s Conceptualization, 2009. 18 UNIVERSITY OF IBADAN LIBRARY 2.2.3.1 Trip Generation Trip generation is the first step in demand travel forecasting and it involves trip production and trip attraction (Wilson, 1974; Salter, 1983). The aim is to estimate the numbers of trips generated by each zone in a study area. In trip generation, information from land use, various household characteristics such as car ownership, size of household, or income, and economic forecasts are used to estimate how many person trips will be made to and from each zone. Trip purposes that can be used include: home based work trips, home based shopping trips, home based other trips, school trips, non-home based trips, truck trips and taxi trips (Chicago Area Transport Survey, 1956; Salter, 1983; Wright and Ashford, 1989; TPH, 1992; IHT, 2001). Regression and Category (Cross-Classification) Analysis are the major models used in trip generation (Hutchinson; 1974; Wilson, 1974; Ayeni, 1979; Papacostas, 1987; Okoko; 2006); although the most widely used is regression model. Trip generation equations have as their dependent variables the number of trips generated per person or per household for different trip purposes, pedestrian volumes, pedestrian level of service, and so on. The independent variables such as land-use and socio-economic factors are considered as factors influencing trip making (Chicago Area Transportation Study, 1956; Fruin, 1971; Pushkarev and Zupan, 1971; Wilson, 1974; Behnam and Patel, 1977; Ojo, 1990; Kitamura, 1991; 1991; IHT, 2001; Desyllas, Duxbury, Ward. and Smith, 2003; Timmermans, 1992). Other independent variables observed by the studies of (Landis, Vatticuti, Ottenberg Mcloed and Guttenplan, 2001; Rahman, 2007; Boon, Tong and Olszewski, 2005; Nakkash and Grecco ,1972; Vic Kerman, 1974; Golding and Olsen, 1976; Leake and Huzzayin, 1979; Southworth, 1979) are availability of sidewalk, lateral separation, motor vehicle‟s traffic, motor vehicle‟s volume, motor vehicle‟s speed, driveway frequency and accessibility Major problems with regression technique in estimating trip generation rate include (i) errors involved in least-square regression equations to predict future trip generation rates, and (ii) aggregate representation of household travel behaviour by assuming that the choice criteria in micro-economic decision-making for all individual are the same. 19 UNIVERSITY OF IBADAN LIBRARY In spite of inadequacy of regression analysis as observed by (Meier, 1962; Weber, 1964; 1980; Salter, 1983; Thorngren, 1970; Hanson,1979; 1980; Hanson and Hanson, 1979; 1981; Anas, 1983; Horowitz,1980; Lierop and Nijkamp,1982; Ayeni,1986a; Kitamura, 1991) the technique remains a useful tool in forecasting trip generation in the literature. Nonetheless, allocation of the trips generated in terms of origin and destination (OD) is termed trip distribution, and it was discussed in section 2.2.3.2. 2.2.3.2 Trip Distribution Trip distribution forecasts the distribution of trips between pairs of traffic zones and is usually conceptualised in terms of an interaction matrix known as “Origin-Destination Matrix.” The origin-destination matrix shows the amount of trips from any zone (i) to any zone (j), including (i=j). The purpose of trip distribution modelling is to find equations that reproduced the intra- urban and inter-zonal pattern of trips. Trip distribution models estimate trip volumes (Xij) that interchange between all pairs of zone (i) and zone (j). In any urban area, if (Oi) trips are generated from zone i and (Dj) trips are attracted to zone j, this model calculates the volume of trip (Xij) from zone ( i ) to ( j), bearing in mind the effect of travel impedance (λij) which could be the distance between ( i ) and ( j ) or the cost, or the travel time. Although, spatial interaction theory has offered some explanations to the issue of interaction in urban centres and also provide reasons for the day-to-day movements of people to places of work, markets, shopping centres, religion camps, recreation and to school but it is limited in its applicability to complex movement patterns. Nevertheless, the use of gravity or spatial interaction techniques has a long and distinguished history in modelling the relationship between attractions and movement (Batty, 1976; Ayeni, 1979; Foot, 1981). Reilly, a sociologist, was probably the first to introduce the Newtonian concept of universal gravitation to social science. Drawing an analogy from the gravity model, Reilly (1953) postulated that: two cities attract retail trade, primarily shopping goods, from an intermediate city or town.... approximately in direct proportion to the 20 UNIVERSITY OF IBADAN LIBRARY population of the two cities, and inversely proportion to the square of the distances from these two cities to the intermediate town”. Casey (1955) applied Reilly's idea to transportation planning when he discovered that "the purchases of the residents of a neighbourhood attracted to the retail centres is directly proportional to the size of the centres and inversely proportional to the squares of the driving time (distances) from the neighbourhood to the retail centre”. Voorhees (1956) used gravity model to trip distribution in Baltimore by translating trips generated in land use areas into a matrix and he was able to identify the number of trips from origins to each destination in Baltimore in the US. Gravity models enable prediction of intensity of interaction between where people start their journeys (the origins) and where they are going (their destinations), and form the basis of many transport-planning models. However, the model has not been successfully applied to modelling pedestrian movements at the scale of buildings and streets. Furthermore, the uses of Euclidian distance or shortest path through networks in gravity models are less applicable at small spatial scales. Gravity models are also criticised for modelling general patterns of movement and can never be used to model the movement of individuals (Hacklay, O‟Sullivan, Thurstain-Goodwin, and Schelhorn, 2000).It also lacks theoretical basis besides its analogy with Newtonian concept of gravity (Ayeni, 1974; Openshaw, 1976; Lee, 1973). Another model that gives explanation to pattern of movement due to inconsistencies in Gravity model is the entropy maximisation model. The entropy maximization gravitational operatives are derived from the concept of entropy in thermodynamics or statistical mechanics and partially from information theory in communication engineering (Clark and Avery, 1978; Ayeni, 1979). The concept of entropy is probably one of the most misapplied analogies drawn from the physical sciences (Ayeni, 1974; 1979). This misapplication arises from the simple descriptive use of a concept that is purely mathematical and which has very strong theoretical assumptions as well as inadequate appreciation of the difficulties emanating from the fact that the concept has been introduced into geography from two different sources. The simple descriptive use of the concept results in its somewhat ambiguous definition in the literature whilst the dual origin generates a certain degree of contradiction in its usage. Nevertheless, the model provides explanations to 21 UNIVERSITY OF IBADAN LIBRARY movement in urban areas. The next model in urban transport model is modal split. This step involves the decision of individual in the choice of transportation mode to their destinations. 2.2.3.3 Modal Split Model Trips may be made by different modes of travel and the determination of the choice of travel mode by individual is known as modal split (Salter, 1983). Typically, modal choice has two levels, the choice between private and public transport at one level, and between different public transport modes at the other level. Modal split model therefore is a model of human choice that explains how people select between competing alternatives of transport modes (Kanafani, 1983; Hutchinson, 1974). Mode-choice also helps in estimating amount of patronage on different transport modes and indicates the spatial pattern of this demand (Black, 1981). The models also attempt to predict the mode of travel that will be chosen for a particular journey or trip. Daniels and Warnes (1980) identified five sets of variables that influence the choice of mode for a trip. First is the location of the individual to his or her destinations. If an individual lives only a few blocks from his or her destination, he or she might walk or ride a bicycle. Second and related to location factor are sets of trip factors. These factors include trip length, the travel costs, and the travel-time ratio which compares the travel time associated with different modes of transportation. Third is the number of private transportation factor which influence the decision whether to use private transportation. Fourth, is the public transportation factor, which includes accessibility of individual to public transportation facilities such as bus shelters, buses and trains. Fifth, is a set of economic variables, which particularly, involves economic status and auto-ownership of people, for instance, areas characterised by high economic status tend to be less dependent on walking and use of public transportation than areas characterised by low economic status. Modal split could be analysed using any of the following models: (i) the stratified diversion curve model; and (ii) probabilistic models such as discriminant analysis, probit analysis and logit analysis. The diversion curve model is difficult to calibrate if more than two competing travel modes are involved. As a result of this, the 22 UNIVERSITY OF IBADAN LIBRARY probabilistic models such as the discriminant analysis, probit and the multinomial or dichotomous logit models are widely used (Okoko, 2006). In modal split model, decisions are made by comparing operation characteristics of alternative urban transport modes, but there are some subjective factors such as reliability (Golob, Canty, Gustafson and Vih, 1972), convenience (Spear, 1976), comfort and safety (Hartgen and Tamer, 1971), which contribute to any assessment of the attractiveness or otherwise of each of transport mode. Furthermore, socioeconomic characteristics of the travellers, his or her attitude (Golob, Canty, Gustafson and Vih, 1972) and the type of trip all play part in undermining any simplified explanation of choice. Black (1981) was of the opinion that the complexity of factors in individual choice of means of transportation makes it difficult in the explanations of modal split. Furthermore, he observed that the complexity in individual choice factors enhances varieties of modal-split models. Their modelling sequence includes (i) combined with trip generation; (ii) between trip generation and trip distribution (trip end models); (iii) combined with trip distribution (gravity type models); and (iv) between trip distribution and trip assignment (trip interchange models).These modal-split models were based on motorized trips which include the pattern of mass public transport and personal transport but excluded pedestrian trips. 2.2.3.4 Traffic Assignment Model The final task in terms of forecasting traffic flow is to assign trip to a particular road or route within the city. Once trips have been split into highway and transit trips, the specific path that they use to travel from their origin to their destination must be found. These trips are then assigned to that path in the step called traffic assignment. Traffic assignment therefore involves models of route choice and when predicting flows between traffic zones, it involves everybody choosing the routes with the shortest travel time. The process involves the calculation of the shortest routes from each origin to all destinations. Some of the techniques of allocating trips to routes are: (a) All-or-Nothing Assignment (b) Assignment using diversion curves; (c) Capacity restrained 23 UNIVERSITY OF IBADAN LIBRARY Assignment; (d) Multi-path proportional or probability assignment and (e) Linear programming assignment of trips All-or-Nothing Assignment involves a route carrying all the traffic. However, a variety of problems occurred when using this approach. Lane et al (1971) observed that, (i) as with modal choice, people not only consider travel time when selecting route, but also travel costs;(ii) that individual perceptions of travel time varies;(iii) that traffic conditions and travel time vary at different times of the day and (iv) most importantly when two or more routes are close together, one route has to be only marginally quicker than the other for it to be assigned all the trips. In an effort to address the last problem, Cadwallader (1985) observed that many transportation models use proportional or probability assignment rather than All-or- Nothing Assignment. Basically, proportional assignment involves assigning proportions of traffic among a number of alternative routes, as a function of time differences between these routes. Furthermore, he noted that though, this approach is more realistic in terms of producing multipath solutions, it is believed to be rather simplistic, as travel time is still only the determinant of route choice. However, in recent times, various traffic assignment models have been developed and they are based on (i) dynamic or time-dependent network attributes, and (ii) travellers‟ decision making and travelers‟ route travel time perception of the network (Liu et al 2002). An alternative approach in urban traffic or trip which emerged during the 1970s are still relevant in research (Hensher and Stopher, 1979), and this involves analysis of individual travel behaviour. Analysis of individual travel behaviour is also embedded in behavioural models. Thus, section 2.2.4 discusses behavioural modelling of traffic. 2.2.4 Behavioural Modelling of Traffic Behavioural modelling is an alternative approach which emerged during the 1970s and still being refined (Hensher and Stopher, 1979). It is the analysis of individual travel behaviour. A behavioural model according to Domencich and McFadden (1975) represents the decisions that consumers make when confronted with alternative 24 UNIVERSITY OF IBADAN LIBRARY choices. Travel related choices include whether to make a trip or not, time of the day to travel, destination, transport mode and route. A behavioural model is based on a representation of individual choice when faced with alternatives or common experience. Black (1981) suggests that in choice situation, a person weighs up advantages and disadvantages of course action against the advantages and disadvantages of the alternatives. The comparison is made on an assessment of the attributes of each alternative such as quality of pedestrian walkways or facilities. A logical decision is to select the alternative which gives the greatest enjoyment. Hensher (1977c) observed that analysis in behavioural models look more realistic because behavioural pattern of individuals are investigated instead of statistically derived through zonal correlation. One of behavioural models which is relevant to this study is Analytical Hierarchical Process (AHP). Analytic Hierarchy Process (AHP) is a decision algorithm developed by Thomas L. Saaty in the 1970s (Nataraj, 2005). Saaty (1990) describes AHP as “a method of breaking down a complex and unstructured situation into its components parts; arranging these parts or judgments on the relative importance of each variable; and synthesizing the judgments to determine which variable have the highest priority and should be acted upon to influence the outcome of the situation”. Partovi (1994) observed that AHP has three broad steps: (i) the description of a complex decision problem as a hierarchy, (ii) the prioritization procedure, and (iii) the calculation of results. Analytical Hierarchy Process (AHP) is a model with well-defined mathematical structure of matrices that are used to generate eigenvectors that are used in assisting people in making complex decisions (Merkin, 1979; Saaty, 1980; 1994). AHP methodology compares criteria, or alternatives with respect to a criterion, in a natural, pair wise mode. To do so, the AHP uses a fundamental scale of absolute numbers that have been proven in practice and validated by physical and decision problem experiments. The fundamental scale has been shown to be a scale that captures individual preferences with respect to quantitative and qualitative attributes better 25 UNIVERSITY OF IBADAN LIBRARY than other scales (Saaty 1980, 1994). It changes individual choice of preferences that does not fit into linear framework into ratio scale weights. These weights help the decision maker in making a choice. Analytical Hierarchy Process helps planners to capture both subjective and objective evaluation measures. It also provides a useful mechanism for checking the consistency of the evaluation, measures and alternatives suggested by planners and thus, reducing bias in their decision making. Analytical Hierarchical Process can therefore be considered to be both descriptive and prescriptive model of decision making. It is perhaps, the most widely used decision making approach in the world today (Nataraj, 2005). Its validity is based on thousands of applications which are well documented in the literature. Like Logistics Regression and Multiple Regression models used in the study, Analytical Hierarchical Process is also important in this study in that as a model, it can be used to examine in order of hierarchy the most significant factors or variables affecting the decision of households and on-street persons‟ to walk along walkways as pedestrian. It can equally help in pedestrian route selections from many alternative routes. Analytical Hierarchical Process also helps in the examination, construction and maintenance of pedestrian facilities. Several transportation planning researches and textbooks normally use regression analysis and categorical analysis to explain vehicle trips. Pedestrians that form part of the travel are often neglected in the models. The variables tend to focus on vehicle trips and these includes factors such as: household size, distance from CBD, residential density, income, occupation of the household head, and social area indexes (Oi and Whuldiner, 1962; Hutchinson, 1974; Meyer and Miller, 2001). These factors have been commonly used as the independent variables to explain vehicle trip making. However, it is not proven that the same factors are associated with pedestrian trip making. Section 2.2.5 discusses walking distance concept and modelling techniques of pedestrian‟s movement. 26 UNIVERSITY OF IBADAN LIBRARY 2.2.5 Modelling Pedestrian Traffic Pedestrian movement or traffic, in the context of this study has to do with any person that travels by foot and the focus is on walking as a mode of transportation. Thus, this study examines pedestrian modelling techniques as it relates to pedestrian movement. Pedestrian modelling is frequently used for making decision regarding the planning, design, and management of pedestrian areas. Modelling pedestrian flows or movements is quite useful in any strategy aimed at improving efficiency and comfort in mobility for large number of daily visitors, and it as well proves crucial for the profitability of such activities as retailing. For example, the designer of a new shopping mall would be interested in what location people are likely to be attracted to, or the operation of large-scale events (e.g. religious activities), might want to know where congestion will occur so they can develop management plans (Sinclair, 2004; Ronald, 2004; Ronald and Sterling, 2005; Boisvert, 2005; Ronald, Sterling and Kirley, 2006) Approaches to modelling pedestrian movements can be Macroscopic, Mesoscopic and Microscopic (Hoogendoorn and Bovy, 2000; 2001; Hoogendoorn, Bovy and, Damen, 2002; Blue and Adler, 2000). However, the historical event in pedestrian studies and the position of automatic microscopic pedestrian data collection of pedestrian studies as presented by Tecknomo, Takeyama and Inamura (2002) is shown in figure 2.4. 27 UNIVERSITY OF IBADAN LIBRARY Microscopic Macroscopic: Simulation: Fruin (1971), Helbing (1992) HCM (1985) etc Blue&Adler (2000) Pedestrian Analysis A u t o m a t i c Pedestrian Manual, Automatic, Data collection Macroscopic Macroscopic: Microscopic Lu et al (1990) Figure 2.4: Automatic Microscopic Data Collection in Pedestrian Studies. Source: Tecknomo, Takeyama and Inamura (2001) 2.2.5.1 Macroscopic Approach Pedestrian AuMtoicmroasticco pic Macroscopic Macroscopic models neither distinguish individual pedestrian nor describe the PedestrianA nalysis MSaicmrouslcaotipoinc Fruin (1971) behaviour of pedestrians individually. Macroscopic models aggregate the Data Helbing (1992) characteristics of pedestrian movements (Hoogendoorn and Bovy, 2000; Harney, Collection HCM (1985) 2002). However, Fruin‟s work in the 1970s provides an understanding on macroscopic approach. Fruin (1971) developed a level-of-service measurement that showed how congested areas are based on the density of people in that area. Level of Service (LOS) of a pedestrian is a pedestrian threshold, which attempts to measure the level of safety or comfort of pedestrians along roadways. It quantifies congestion by measuring the flow of pedestrian per unit width of walkway. Six levels of Service are identified along roadways (Fruin, 1971) from A (free flow with typically less than 23 people per minute per metre of walkway), B (flow with typically between 23-33 people per minute per metre of walkway), C (flow with typically between 33-49 people per minute per metre of walkway), D (flow with typically between 49-66 people per minute per metre of walkway), E (flow with typically between 66-82 people per minute per metre of walkway) to F (extreme 28 UNIVERSITY OF IBADAN LIBRARY congestion, more than 82 people per minutes per metre of pavement) where progress would be made by means of shuffling. Different areas, such as open space, lifts and stairs, have different density values for each level from A to F, where A is free flow and F is severely congested. However, this does not take into account the origin and destination of pedestrians, but the number of pedestrian passing at a particular point. Mathematical models, such as regression and Markov, Spatial Interactions / Entropy maximizing models have also been used to model pedestrian movement (Harney, 2002). Regression models estimate the number of pedestrian that will visit certain area, based on elements such as retail floor space and parking spaces (Sandahl and Percival, 1972). For example, Pushkarev and Zupan (1971) used regression models to describe observed pedestrians in area of Manhattan in the US. In their study, Pedestrian volume per hour per block was used as the dependent variable. The explanatory variables used include commercial space, office space, cultural and entertainment space, manufacturing space, residential space, parking space, vacant space, and storage and maintenance space. Behnam and Patel (1977) study is similar to Pushkarev and Zupan (1971) study. Using regression analysis, Behnam and Patel (1977) estimated pedestrian demand in high density areas by using existing land use data and pedestrian counts in Milwaukee CBD in the US. They counted the number of pedestrians and observed the characteristics of their trips, including trip times and distances. Based on future land use variables, Behnam and Patel (1977) predicted future pedestrian volumes in Milwaukee CBD. Timmermans,Vander Hagen, and Borgers (1992) used a regression model to describe observed number of pedestrians in various parts of the centre of Oregon in the United State of America and Sweden. Matlick (1996) used regression analysis to describe pedestrian demand by using household population, transportation mode share, and activity centre data. Matlick (1996) analyses determine the priority areas or corridors for improvement of pedestrian facilities. Ercolano, Olson, and Spring (1997) however, used peak vehicles per hour, transit vehicle/ ridership, and non-motorized modal share to estimate the pedestrian travel demand at the peak hour in suburban areas of Baltimore, Plattsburgh in New York to determine the location of pedestrian crossings, sidewalks, and signal re-timings. 29 UNIVERSITY OF IBADAN LIBRARY Moudon, Hess, Snyder, and Stanilov (1997) using regression analysis showed effects of site design on residential density, income, automobile ownership, and intensity and type of commercial development. Six urban areas out of twelve neighborhood sites studied showed on the average, a volume of 37.7 pedestrians per hour per 1,000 residents, while the six suburban areas sampled showed an average volume of 12.5 pedestrians per hour per 1,000 residents. Ewing and Cervero (2001) providing synthesis of the relationship between travel and built-up environment, observed the effect of walking trips are on four variables: that is, prototypical neighborhoods, activity center, land use, and transport network. Ewing and Cervero (2001) results showed that walking trips are associated with transit-oriented neighborhood, the distance between commercial districts and residential areas, higher density areas, land use mixing areas, and multi-story buildings. Quantifying pedestrians‟ perception of safety and comfort in the roadside environment, Landis, Vatticuti, Ottenberg, Mcloed, and Guttenplan, (2001) used stepwise multivariate regression analysis of 1,250 observations from event that placed 75 people walking on a road course in the Pensacola Metropolitan area in Florida. The Pedestrian Level Of Service (LOS) model incorporates statistically significant roadway and traffic variables that described pedestrian‟ perception of safety or comfort in the roadway environment between intersections. In the study of Dhaka city of Bangladesh, Rahman (2007) observed that LOS is a function of safety, security, convenience and comfort, continuity, system coherence and attractiveness. Predicting average total pedestrian flow, (Desyllas, Duxbury, Ward and, Smith, 2003) used multiple regression analysis to test samples from manual observation studies of average total pedestrian flow per hour on 237 sites in Central London in the UK. The model provides predicted flow values for 7,526 street segments in 25 square kilometres of Central London. Boon et al (2005) used regression model to establish the relationship between walking distance perception and the corresponding measured route characteristics like walking time, presence of barriers and signalized crossing. They further used time contour and walking time ratio to determine pedestrians‟ accessibility to city centre. The results of the regression analysis showed that walking facilities and their characteristics did have substantial impact on pedestrians‟ perceptions of personal walking comfort and route quality. 30 UNIVERSITY OF IBADAN LIBRARY The regression model had been criticized by Timmermans, Vander Hagen, and Borgers (1992) on the basis that the model has not been able to provide much insight into the factors influencing route choice behaviour of pedestrians, the sequencing of visits, complementary relationships, the strength of functional relationships between streets and the influence of locational pattern of shops on pedestrian movement. Nevertheless, the model is still relevant in predicting pedestrian movements. The markov chain model is based on the assumption that only the last state occupied by the process is relevant to its future behaviour. The model has been used to analyse the kind and intensity of functional relations in multi-purpose trips (Harney, 2002). Markov Chain models is useful as it can be used to explain where pedestrian have visited most recently as this will have an effect on where to go next, and are most useful for trip chaining analysis (Horton and Schuldiner,1967; Wheeler,1972; Timmermans, Vander Hagen, and Borgers ,1992; Harney, 2002; Ronald, Sterling, and Kirley, 2007). However, this approach is severely limited due to its simplicity. The model does not include preference structure or a choice rule, thereby making the model theoretical underpinning relatively weak and not easily applied to changes in variables or conditions. Over the years, various proposition to overcome these limitations have been made, however, they seem to complicate the model, resulting into slight improvements in its applicability to a worldwide pedestrian movement (Harney, 2002). Spatial interactions/entropy maximizing models are the most popular modelling form earlier used. They can be applied to model many types of interactions; hence, they form the basis of classical transportation models. Spatial interaction / entropy maximizing model form the basis for many computer applications used to model pedestrian and vehicular movements, and their goodness -of – fit has increased dramatically over the years. An example is PEDROUTE developed by Buckman and Leather (1994) a simulation model developed for Hal crow Fox and London underground Limited. PEDROUTE– is a pedestrian modelling software that produced detailed simulation of the movement of pedestrians around a station and provides statistics of their journey times, the delay, congestion and the level of service (LOS) for each segment of the road. Pedestrians are assigned along routes through the station using an integral dynamic assignment taking into account bottleneck and congestion 31 UNIVERSITY OF IBADAN LIBRARY effects. Stations are broken down into different blocks representing stairs, escalators and platforms, ticket halls, with each of these blocks having different speed or flow curves associated with the movement of pedestrian through them. Although providing powerful graphics and computational ability, the underlying assumptions and principles used in PEDROUTE and other similar computer program are the same as other spatial interactions/entropy maximizing models fail to incorporate the individual basic mechanism underlying pedestrian movements. These programs cannot represent the interaction of each pedestrian with other pedestrians and the external environment, only the overall or system-wide behaviour. 2.2.5.2 Microscopic Approach Pedestrian AuMtoicmroasticco pic Macroscopic Microscopic models consider the time-space behaviour of each pedestrian separately. PedestrianA nalysis MSaicmrouslcaotipoinc Fruin (1971) Moreover, the behaviour of pedestrians is described in relation to other pedestrian in Data Helbing (1992) their direct environment. Examples of such models are the microscopic model by Collection HCM (1985) (Helbing, 1997) and the cellular automata model of (Blue and Adler 1999). Helbing and Molnar (1995) observed that pedestrian modelling involves pedestrian movements in either positive or negative social fields, where pedestrian behaves as if acted upon by external forces. Using attraction and repulsion to model behaviour, Helbing, Molnar, Farkas, and Bolay (2001) developed complex equations to describe a range of pedestrian behaviours commonly referred to as “social force” model. They observed that streams formed in the crowd resemble fluid flows. Hoogendoorn and Bovy (2004) used the same starting point of basic mechanics formula and developed a three-layered model encompassing activity choice, way finding and walking. A similar approach is the use of cellular automata (CA) where pedestrian occupies cells on a grid and move according to some simple rules. Cellular automata is an artificial intelligence approach to simulation modelling defined as: mathematical idealizations of physical systems in which time and space are discrete, and physical quantities takes on finite set of discrete values. A cellular automaton consists of a regular uniform lattice, (of „array‟) usually infinite in extent, with discrete variables at each site („cell‟). These models generally use grid-based paths, where one person can occupy a cell at once. Most of the models based on this approach, used the 32 UNIVERSITY OF IBADAN LIBRARY Schreckenberg-Nagel approach to modelling vehicle traffic using CA as a starting point (Nagel and Schreckenberg, 1992). Cellular automata were extended to study pedestrian simulation by Blue and Adler (1998) who were first to generate fundamental pedestrian flows using CA micro- simulation of a pedestrian walkway with unidirectional flow. Each pedestrian in the study was allocated individual walking speeds and other characteristics, and their movements were governed by local rules. These local rules govern when cells are occupied, when pedestrian overtake, when they can change lanes and directs basic forward movement. This simple model replicated both the system-wide effects and individual behaviour of the pedestrian effectively as measured by various performance indicators. With bi-directional pedestrian walkway Blue and Adler (1999; 2000) used micro simulation model for of uni-directional pedestrian flow by updating lane assignment, lane movement, and travel speeds. These experiments were conducted with unidirectional flow with varied lattice widths and lengths. But bi-directional flows were assigned to directional lanes with no direction cross over. The first experiment was to examine the sensitivity to the size of the lattice, and it was found that the changing in the dimensions of the lattice did not provide significant change in system performance. The second experiment examined the distinction that arises when directionality is added to the pedestrian stream, with no cross over between lanes. The results showed that field observations of bi-directional flows do not have different characteristics from single-direction flows (Blue and Adler, 1999). The third experiment examined walkway where opposing traffic mingles and directional lanes are not set up. The results show the modelling power of CA that complex and reasonable group behaviour can emerge from a simple set of behaviourally based rules. The CA pedestrian model shows speed-flow density and fundamental flow characteristics that are acceptable based on their works. Although CA modelling is „grainy‟, discredited in approach, complex movements are accommodated with a manageable set of parameters, resulting in viable directional pedestrian models. In spite of imperfect reproduction of pedestrian movements, Harney (2002) was of the opinion that emergent behaviours realized using this 33 UNIVERSITY OF IBADAN LIBRARY modelling technique shows that CA modelling of pedestrians is a potentially powerful tool for traffic engineers, planner and facility designer. In urban area, all trips or movements either home-based or non-home-based are generated through vehicular traffic, or pedestrian traffic. These movements are channeled through a path which can either be a walkway, a street, a road, a motorway and a highway, all of which are referred to as “networks”. Networks also includes analysis of location of intersections, nodes and terminal, the density and length of routes; the accessibility of individual points on a network to other points; and the distance travelled to reach every point on a network. Section 2.2.6 discusses road networks and walking distance concept and their relevance to the study. 2.2.6 Road Networks in Graph Theory and Walking Distance Concept The significance of road networks and walking distance in the explanation of trips, routes, and distance in geography is well documented in the literature. The two concepts are discussed in the following section. 2.2.6.1 Road Networks The theoretical explanation of road networks in geography is rooted in graph theory. Graph theory is widely used in many disciplines and many applications of the theory exist in the form of network analysis. In geography, roads, transportation networks, boundaries etc are line patterns often referred to as networks. A network is defined as „a mesh‟ of intersecting lines or a set of geographical locations interconnected in a system by a number of routes, junctions and termini (Elliot-Hurst 1974; Bradford and Kent, 1977; Biggs, Lloyd and Wilson, 1986; Ayeni, 1994). A network tells us about how far locations are from one another, whether the lines are joined or not, whether the joining lines are straight or not, what commodities go through the network and whether flow is continuous or intermittent. A graph on the other hand, is defined as an array of points that are connected or not connected to one another by lines. There is little or no concern with the straightness or curvature of the length of the lines (Ayeni, 1994; 2000). 34 UNIVERSITY OF IBADAN LIBRARY Chorley and Hagget (1967), Elliot-Hurst (1974), Bradford and Kent (1977) and also Ayeni (1994; 2000) described graph theoretic analysis of networks as the bridge between human and physical aspects of the subject. In graph theory, links as lines are usually called edges, sides, area segments, or branches. Points on the other hand, are called nodes, vertices, junctions, intersections, terminals etc. In graph theory, there are measures for assessing road network. These measures are based on gross and shortest path characteristics. The gross characteristics include; Cyclomatic number, Beta index, Alpha index and Gamma index. Measures based on shortest path characteristics are the diameter, accessibility index and dispersion index (Bradford and Kent, 1977; Ayeni, 1994; 2000).Any nodes that is well connected to other nodes in a network is said to be accessible. Accessibility is measured using either Konig number or the Shimbel index. However, Shimbel index is the most widely used in measuring level of accessibility. Shimbel index is derived from shortest path matrix and it indicates the number of arc needed to connect any node with all other nodes in the network by the shortest path (Elliot- Hurst, 1974; Bradford and Kent, 1977; Ayeni, 1994; 2000; Okoko, 2006). Networks in graph theoretic forms carry both benefits and penalties. The gains are the high level of abstraction involved. It also enhances flexibility in making analogies between networks in human and physical geography. Network analysis is amenable to rigorous analysis through the use of factor analysis. Most artificial intelligence analysis of patterns in geography using topology makes use of these techniques (Ayeni, 1994; 2000). Roads as network allow us to study common geometrical properties such that: (i) origins of pedestrian trips is represented by nodes (ii) routes or streets that pedestrians move is represented by links and (iii) various destinations of pedestrians is represented by nodes. Networks are structures designed to tie together nodes via routes, whether they are flows of people, goods, information, money, and so on. Pedestrian use different modes and transport facilities to achieve their trips from point of origin to their various points of destinations. These could be achieved through 35 UNIVERSITY OF IBADAN LIBRARY roads, railways, footpaths, streets, etc. At the street level, the level of accessibility of the street network in relation to land use and economic activities may have contributed significantly to the volume of pedestrians produced along such network. Nevertheless, there is also a ‘maximum distance’ pedestrians would wish to walk to bus terminals or stations and to landuse activities along the road network. Therefore, section 2.2.6.2 describes the concept of walking distance. 2.2.6.2 Walking Distance Concept The concept of „walking distance‟ has been employed since mid-sixties as a criterion required in the planning of facilities in new urban sub-divisions. Walking distance is the distance from home or the origin of a trip to the bus stops or other public transport pick up points. Put in a broader perspective, it is a hypothetical distance which people usually those without a car, should be expected to walk to public transport stops as well as to locations of neighbourhood facilities (Adeniji, 1988). For instance, in the UK, 15 minutes is assumed to be the minimum walking distance for persons who do not readily have the use of a private car, and 5 minutes is taken as maximum distance that a car owner can be expected to walk to local facilities or public transport pickup points without using a car. Exploring some urban plans that have adopted walking distance concept in the design of urban physical sub-division, Creswell (1976) observed that in the Abuja aster plan, a region that neither have specific population nor defined land area (during establishment), 400 metres or five minutes walking distance was proposed as maximum distance to neighbourhood facility. In Dodoma in Tanzania, a ten minute or one kilometre walking distance was proposed to neighbourhood facility. As shown in Gothenburg city, Sweden in the 1960s, a planned maximum walking distance to public transport was 500 metres (Gothenburg, 1960). But Swedish Public Transport Association (1969) recommended a walking distance of 300 – 900 metres to public transports stops to Swedish government (Wilson, et al, 1966). Their recommendations were accepted and became the standard criteria for calculating distance in Gothenburg and subsequent urban development schemes in Sweden. According to Swedish Public Transport Association - SPTA (1969) walking distance can be determined if the cross section of each footway is drawn from local centre to 36 UNIVERSITY OF IBADAN LIBRARY its extremity, while the level of the centre is considered to be datum line (see figure 2.5). In calculating the walking distance, the datum line is usually drawn, and for each one metre rise or fall above this datum line, the length of the footway (the walking distance), is reduced by ten metres as presented in figure 2.5. This reduction of walking distance is made so as to provide some allowance for the effects of the neighbourhood topography on the passengers. The walking distance recommended by the Swedish Public Transport Association is as follows: W1 = Actual walking distance in metres W2 = Level walking distance in metres The actual walking distance (W1) is determined by using equation 2.1 W1 W2 10X1  X2  ................. Xn  Y1 Y2  ..........................Yn  2. 1 Datum line X 1 X 2 Y1 Y2 W2 Figure 2.5: Walking distance Scheme of Swedish Public Transport Association. (1969) adapted from Adeniji (1988). Where X = Rise above datum line in metres Y = Fall below datum line in metres 10 = Deduction factor for every one metre rise or fall above or below the datum line. 37 UNIVERSITY OF IBADAN LIBRARY Like other theories, graph theoretical approach and walking distance concept have been criticized on several issues. One of the criticisms of graph theory is the loss of some attributes of networks such as orientation and shape as well as information in respect of volumes of flows. Harary and Palmer (1973) observed that counting graphs meeting specified conditions often known as “graphical enumeration” is a problem. Apart from colouring, there are also routes and covering problems; another common problem associated with graph theory is the sub graph isomorphism problem. This problem involves finding a fixed graph as a sub graph in a given graph. Despite these shortcomings, the theory provides an insight into network analysis in geography and as well help in the measurements of accessibility index of the road networks in the study area using Shimbel index. Regarding the walking distance concept, the result of actual walking distance in equation 2.1 may vary across cities when considering their different socio-economic and physical conditions that also vary. However, in recent times there are computer designed programmes that simulate the movement patterns as well as distance covered by pedestrians in a particular location. However, empirical evidences of pedestrian movement and modelling are discussed in section 2.3 as literature review. 2.3 EMPIRICAL FINDINGS OF PEDESTRIAN MOVEMENT There have been several pedestrian studies based on empirical examination of the effect of land use activities and socio economic characteristics on pedestrian movement and walking trip frequency. In a pedestrian survey on the factors that influence pedestrians decision to walk other than socioeconomic characteristics, Forward (1999) summarized the factors as accessibility, comfort, heavy traffic, safety and security, and aesthetics. Fitzpatrick et al (2006) on the other hand, observed that the decision to walk usually takes into account the distance of the trip, the perceived safety of the route, and the comfort and convenience of walking in relation to alternative mode. 38 UNIVERSITY OF IBADAN LIBRARY Furthermore, Fruin (1971) put the limit of people‟s walking distance at 3.2km in the United State of America. Nonetheless, studies have shown that distances between services have grown and studies have shown that about 80% of trips of trips under one mile or (1.6km) are undertaken on foot in developed countries (Demetsky and Perfater, 1975; Mitchell and Stokes, 1982; Central Bureau Voor Statistics; Forward, 1998a; Forward, 1998b; DETR, 1999; Living Streets, 2001;National Statistics, 2001; DFT, 2003; Desyllas, Duxbury, Ward. and Smith, 2003; ITS, 2004; Buchanan, 2005;; Fitzpatrick et al, 2006). Fears about personal safety are one of the factors that have been identified explicitly in empirical work as influencing both pedestrian route and mode choice (Tight, Kelly, Hodgson and Page, 2004). Studies have shown that some people do not walk because of fear of attack (Crime, Concern 1997, Hamilton, 2000). Tight, Kelly, Hodgson and Page (2004) further observed that this fear is different in men and women, children and adults, elderly and young, ethnic groups and for those with learning and or physical impairment. There is also evidence that levels of fear are greater in urban areas compared with rural areas. Anxieties about personal security are particularly acute at night time and many people; women in particular organize journeys to avoid having to walk at night (Forward, 1998, Mackett, 2001, Living Streets, 2001; Hamilton, 2000). In most studies, night time or the absence of adequate street lighting or dark spots were areas where potential assailants could hide, and were mentioned as factors deterring people from walking. Burkitt (2000) observed in his study that shift workers such as nurses in particular go to extraordinary lengths to make sure that they do not walk or catch public transport at night. Complex social trends have also affected children‟s activities and particularly walking over the past twenty years. In recent years, parents and guardians have come to fear that children could be attacked and abducted by strangers whilst on the streets thus leading to a restriction on children‟s freedom to play out or walk. There have been growing fears about the danger of road traffic that has meant that many more children are being escorted when they go out and not allowed to make journeys on their own. Hillman, Adams and Whitelegg (1990) found that parents restricted their children‟s freedom more because of their fears about road traffic than their fears about strangers assaulting their children. One 39 UNIVERSITY OF IBADAN LIBRARY result of road traffic situations is that more and more parents are deciding that their children should be driven rather than walk to school (Bradshaw and Jones, 2000). Road traffic type and volume are also given as factors in choosing not to walk. Appleyard and Lintell (1972) in a comparison between three streets observed that the street with the greatest amount of traffic resulted in the least amount of contact between people living on opposite sides of the road on the same street. Road traffic encompasses a number of different elements such as volume, speed and other behaviours. In a study of mode choice for short trips (Forward, 1998) travel time was identified as a factor in the decision to walk and if the individuals believed themselves to be “in a hurry‟ they were less likely to make a walking trip. Hass-Klau , Dowland, and Nold (1994) ; Living Streets (2001) observed that urban form, that is, the structure and shape of the urban environment do influence peoples‟ decision to walk. Furthermore they opined that if the urban environment is designed and managed properly, it will encourage walking. Quality of the footpath and other facilities designed for pedestrian use do influence the decision to walk (Pedestrian Association, 2000; Hass-Klau, Dowling and Nold, 1994; NCC, 1997; and Gehl 1999). The particular factors identified in the studies are space, continuity, cleanliness, rubbish, dog dirt and the condition of the pavement. Furthermore, there are evidences that provision of distinct footpaths for pedestrians in cities such as Gothenburg, Lund, Malmö (Sweden), York (UK), Copenhagen (Denmark), Portland (USA) encourages more walking journeys. Weather often comes up in the lists of factors that people find significant in the decision to walk. Forward (1998) showed that for short trips, dry weather had a positive impact on the decision to walk. It is not only the discomfort of walking in inclement weather that can deter people from walking but also the fact that one has to dress in the appropriate clothes for the weather (Hodgson, 2000).Section 2.4 of the thesis examines the gaps observed in the literature. 40 UNIVERSITY OF IBADAN LIBRARY 2.4 GAPS IN THE LITERATURE The literature reviewed above has shown that some urban structures readily lend themselves to integration of public transport into urban development than others, and in the micro urban level of planning, one form of physical unit or the other is relevant to planning concepts especially for the location of facilities and public transport pick- up points. In such physical urban sub units, proximity in the location of local facilities and public transport stops are given serious consideration in order to provide convenient and increase efficiency of public transport systems. In the planning of housing estates or major urban expansion schemes, research has shown that attention is paid more on population size and the sphere of influence of the supporting local facilities, with the neglect or little focus on acceptable walking distances to location of such facilities and public transport pick-up points. Hence, the distance people are ready to walk to public transport stops and facilities is overlooked and least understood. Some researchers believe that the main generator of movement is the configuration of the street network itself, and their patterns of movement are largely determined by this configuration, rather than by the distribution of attractors within the network. They however, ignore the evidence of changing land use patterns on movement rates. So, it is significant to note that the actual street configuration has not changed, but the location of geographical phenomenon such as shops has, untimely responded to changing activity patterns of the town‟s inhabitants. Macroscopic pedestrian models earlier described are useful at the macro-scale in the explanation of pedestrian movement. Unfortunately, this highly aggregative nature of these models constitutes their greatest weakness. These models view human movements on earth‟s surface as being homogeneous. Hence, the four travel decision processes of, the need or purpose of to fulfill a trip (trip generation), where the need or purpose is best fulfilled (trip distribution), the decision of which means to get there (modal choice), and the decision of which route to take (traffic assignment) are considered to be the same. This is rather unrealistic when considering the heterogeneous nature of urban transportation, the structure of urban areas and individual decision making processes that differ. 41 UNIVERSITY OF IBADAN LIBRARY Although, microscopic pedestrian models explain pedestrian movement at micro- scales, but the microscopic models assumptions placed pedestrian into lanes and grids as vehicles. Microscopic models also placed people on walking trips and their movements are guided by set rules such as direction of movement, where to stop, distance to cover and so on. Human movements however are more complex than been restricted to lanes and grids, and their directional movement makes it difficult to understand the behavioural pattern of their movement. Another approach to urban modeling that found favour in urban geography is behavioural approach which puts greater emphasis on the decision making processes that generates various kinds of spatial patterns and introduced into urban geography the study of movement patterns, especially those associated with intra-urban migration and journey to shop (Herbert and Johnston, 1978; Hensher and Stopher, 1979).A major stand within the behavioural approach relate to the notion of individual cognitions of urban environment. Urban dwellers believed to possess cognitive or mental maps of their surrounding environment, and these maps are far from being identical to the physical structure of the city. It is the distortion contained in such maps, however, that are primarily of interest as they shed more light on the behavioral context within which decisions concerning spatial choices are reached. In as much as pedestrian models allow chaining of the origins and destinations of people and as well give room to predicting the likely destination of an individual at a given time, however, they do not include preference structure or a choice rule, thereby making their theoretical underpinning relatively weak and not easily applied to changes in human rational thinking or condition. Based on the background of the study, the research problem and review of literature, section 2.5 examined the hypotheses of the thesis. 2.5 HYPOTHESES OF THE STUDY The hypotheses of this study emanated from background to the study, the research problem, conceptual framework and literature review. These hypotheses are: 42 UNIVERSITY OF IBADAN LIBRARY I. The maximum distances people are ready to walk to bus stations, landuse activities and various functions and services do not vary across zones in the study area. II. The number of pedestrian trips made by households‟ and on-street persons‟ in urban centres is a function of their trip types, number of economic activities engaged in and the level of accessibility. III. The decision to walk and the pattern of movement vary with socio- economic characteristics, nature of pedestrian facilities and factors such as distance, time, season and weather. IV. Most people walk in areas where their level of safety is higher, than in areas where their level of safety is lower and their trips safety is a function of lateral separation, traffic volume, vehicles‟ speed and drive way access. For the testing and understanding of the hypotheses, there arose the need to operationalize certain concepts. For example, level of accessibility, the decision to walk and pedestrian level of safety. Level of accessibility was measured using Shimbel index, the decision to walk was measured using dichotomous response „ready to walk‟ and „not ready to walk‟ and pedestrian level of safety was measured using pedestrian level of service. Details of the operationalization and measurement of these concepts were discussed in appropriate sections of the thesis. 2.6 SUMMARY This chapter examines concepts, models and theories that explain pedestrian movements as a basis for the examination of characteristics and factors of pedestrian movements. Although, the development of models may have identified some critical factors, most of the proved concepts used in these models appear to be minimally necessary but incompletely understood. In their applicability, most of these models were developed in advanced economies where there are adequate and effective pedestrian facilities. Furthermore, most of these models have not been applied to 43 UNIVERSITY OF IBADAN LIBRARY pedestrian movements in developing economies particularly Nigeria. Applying these models to the study in Nigeria where pedestrian facilities are inadequate, and with diverse cultural background may generate similar or different results from those observed in advanced economies. The chapter showed that main sources of information of pedestrian trips in the literature rely on the information based on Census of National Household Travel Survey (NHTS), airport, substation, underground metro lines, rail station, shopping mall and stadia.The data of which can hardly be found in country like Nigeria. Hence, Pedestrian modelling researches based on households and people walking along city streets in the literature particularly in Nigeria are grossly inadequate. Furthermore, the distance people are ready to walk; the factors that encourage people to walk; and factors that explain pedestrian level of safety along walkways have been advanced in the literature. However, little is known about these factors in Nigeria, particularly Lagos the economy nerve centre of the country. Also, very little researches look specifically at how far pedestrians walk to any destination. The survey often examines the number of pedestrian trips made, but do not include walk trip distances. Thus, making the maximum distance people can walk to urban facilities least understood. Based on the analysis above, there is the need for empirical research in the area of pedestrian movements and modeling in Nigeria using both households and city street data so as to contribute to knowledge in the area of pedestrian movements and modeling in Nigeria. The methodology of the study was examined in the chapter that follows. 44 UNIVERSITY OF IBADAN LIBRARY CHAPTER THREE METHODOLOGY 3.0 INTRODUCTION This chapter of the thesis discusses the methodology used in accomplishing the aim and objectives of the study through the testing of hypotheses of the last chapter. Details of the methodology include research design, types of data source and method of data collection, sample size, questionnaire administration and sampling procedure, measurement of variables and techniques of analysis. 3.1 RESEARCH DESIGN Survey research design was employed in obtaining data and variables required in the explanation of pedestrian movement in this study. Movements in urban areas are concerned with spatial interaction. The spatial interaction involves examination of both vehicular and human movements in cities. The focus of this study however is on pedestrian movement patterns. The study is based on aggregate movement pattern of urban residents as pedestrian in relation to their socio economic characteristics and disaggregate pattern of their movement as pedestrian based on their level of service, level of economic activities, safety, comfort, security, distance, weather and so on in the urban area. The study focused on household heads and on-street persons‟ (people observed while walking on the streets) in Ikeja area of Lagos. The choice of household heads and on- street persons is because most individual is a pedestrian at a particular time of the day. Particularly, a member of a household can be a pedestrian either at the beginning, at the middle or at the end of household member‟s journey. On-street survey was used to obtain information from pedestrians walking on the streets. Observational study was used to record and measure pedestrian flow and pedestrian level of service along streets segment in the study area respectively. 45 UNIVERSITY OF IBADAN LIBRARY Quantitative and qualitative measures were used to examine urban land use pattern; purpose of trip and pedestrian trip types in the study area. The delineation of the study into seventeen zones (see Figure 3.1 and Figure 3.2) was based on: (i) the need to observe spatial variations in the landuse types, pedestrian traffic and related activities of the subunits that made up the study area, (ii) the need to focus on areas in Ikeja where pedestrian activities predominate and (iii) the studies by Hutchinson (1971), Salter (1983), Okoko (2002) who were of the opinion that survey area in which trip making is to be studied in detail may be bounded by an external cordon, such that all developed area which influences travel patterns are included with areas which are likely to be developed. Permitting disaggregation of data obtained for the study area therefore, required the division of the study area into zones and each zone is named after a popular street inside the zone (see Table 3.1). Information was sought on pedestrian day-to-day movement and land use pattern of Ikeja metropolitan area of Lagos. Other information sought include: availability of pedestrian infrastructure, factors affecting pedestrian walkability, pedestrian walking distances as well as maximum distance pedestrians are ready to walk to various activities or functions. Availability and non-availability of pedestrian walkways inform the need to model pedestrian level of safety in the study area. Variables used in explaining the research hypotheses were operationalized under measurement of variables. Schematic presentation of the research process is presented in Figure 3.3. Figure 3.3 showed the delineation of study area into zones after a pilot study. The zones consist of industrial, institutional, residential, commercial and other landuse area. Information was sought from households and on-street persons through questionnaire and direct observations. The inter-nets and journal publications and maps are also of important sources of information. Road width and vehicle speed were measured with tapes and radar gun respectively. Statistical software enables analysis of data obtained using logistic and multiple regression. Other techniques used include analytical hierarchical process, Shimbel Index and ArcView GIS. 46 UNIVERSITY OF IBADAN LIBRARY Table 3.1: Summary of the Sample Size and Questionnaires Administered Zones Name of Zones Number Estimated Stratum of the Household On-street Total Number of Questionnaire Retrieved Code of Streets in Population in Anticipated Sample Size Persons Sample each Zone each Zone Household Sample Size for On-street Sample Size Size each Zone Household Sample Size Persons Sample Size Male Female Male Female (n) (Nk) (nk) 1 Otigba Area 19 9,975 1,002 79 16 95 56 23 05 11 2 Awosika Area 12 6,300 525 50 10 60 35 15 02 08 3 Obanta Area 19 9,975 1,002 79 16 95 63 16 07 09 4 Kudeti Area 22 11,550 997 91 19 110 45 40 11 08 5 Akeem Balogun Area 13 6,825 1,001 54 11 65 39 13 07 04 6 Ajanaku Area 14 7,350 912 58 12 70 31 27 04 08 7 Governor Area 10 5,250 988 41 09 50 30 11 03 06 8 Kadiri Area 10 5,250 988 41 09 50 23 18 04 05 9 Olanrewaju Area 11 5,775 1,008 46 09 55 29 17 05 04 10 Mobolaji Johnson Area 07 3,675 998 29 06 35 21 08 01 05 11 Kasumu Aleshinloye Area 09 4725 991 37 08 45 22 14 04 04 12 Morrison Area 18 9,450 1,004 75 15 90 46 25 06 09 13 Allen Area 15 7,875 996 62 13 75 28 32 05 08 14 Unity Area 17 8,925 1,006 71 14 85 31 39 03 11 15 Alabi Area 13 6,825 1,001 54 11 65 37 15 04 07 16 Community Area 15 7,875 996 62 13 75 29 32 08 05 17 Acme Area 17 8,925 1,006 71 14 85 41 27 05 09 Total 241 N =126,525 16,421 1000 205 1205 978 205 Source: Field Survey, 2009 47 UNIVERSITY OF IBADAN LIBRARY Figure 3.1: The Study Area within Ikeja Local Government Area. Source: Field Survey, 2009. 48 UNIVERSITY OF IBADAN LIBRARY Figure 3.2: The Delineation of the Study Area into Zones. Source: Field Survey, 2009. 49 UNIVERSITY OF IBADAN LIBRARY Study Area Reconnaissance Survey Delimitation of Study Area into Zones Industrial Institutional Residential Area Commercial Other Landuse Area Area Area Area Data Obtained Household On-Street Road width and Data Data Vehicle Speed Questionn aire Direct On-street Maps, Internet, Measurement of Administration Observation Interview Journals etc. roads and Vehicles‟ Speed Techniques of Analyses Statistical Software Analytical Shimbel ArcView Radar Gun (SPSS) Hierarchical Index GIS Process Level of Pedestrian Vehicular Logistic Accessibility Flow Pattern Speed Multiple Regression Regression Analysis Preferred Analysis nature of walkways Results Implications Figure 3.3: Schematic presentation of the Research Process of the Study. Source: Field Survey, 2009. 50 UNIVERSITY OF IBADAN LIBRARY 3.2 TYPES OF DATA AND METHOD OF DATA COLLECTIONS Data used for the study were collected from both secondary and primary sources. Descriptions of data types and methods of data collection of the thesis are described in section that follows. 3.2.1 Secondary Sources Secondary sources used are the Local Government Year Book, Annual Abstract of Statistics, Local Government Digest, Technical Reports, Academic Journals and other periodic publications of Lagos State that contains historical development of Ikeja. Administrative and landuse maps of Ikeja were obtained from the Lagos State Urban Development Planning Department. Population figures of Ikeja Local Government Area were obtained from National Population Commission (NPC) office in Lagos. The population figures obtained from National Population Commission were used to explain the significance of population distribution in the development of the study area. Data on Ikeja road network and length of road network was obtained from Ikeja Local Government Secretariat. Information from this source was used to determine the number of road network in each zone in the study area. This information was useful in the questionnaire administration for both household and on-street respondents. Furthermore, information on the road network eased administration of questionnaire and as well helped in identification and selection of streets that engage in pedestrian activities on daily basis. A review of research work on regional development in developing societies reflects concentration of social and economic variables in various forms. In the study area, the following variables were considered to complement available data of socio economic development of the seventeen zones under study. They are: (i) High, medium and Low density residential areas and Government Reservation and Acquisition Areas; 51 UNIVERSITY OF IBADAN LIBRARY (ii) Commercial Area; (iii) Institutional Area; (iv) Shopping Malls; (v) Recreational Area; (vi) Industrial Area; (vii) Airport; (viii) Open Space; and (ix) Health Facilities. Explicitly, these variables include retail shops, manufacturing industries, motels, hotels, restaurants and bars, construction and allied industries, financial and financial related institutions, hospitals /maternities/clinics, printing and publishing companies, Secondary schools, Primary schools, nursery schools, office and other governmental and non-governmental agencies and religion activities. These variables were chosen for the study because they are functions and services that are measurable. They have also been recognized as measures of urban socio economic growth and are found relevant in the volume of pedestrian trips generation in the study. 3.2.2 Primary Sources Primary sources of data used to complement secondary sources in the study include questionnaire administration, on-street interview, observational study, and field measurement. 3.2.2.1 Questionnaire Survey Questionnaires in appendix I were used to obtain information from urban residents. The questionnaires were designed to capture the behaviour of household heads and on-street persons. Household, on-street person‟s survey and survey based on the land use types are known to be significant in decision making of individual movement patterns. These surveys also constitute the major points of origin or destination of urban trips as discussed in chapter two. 52 UNIVERSITY OF IBADAN LIBRARY The questionnaire (appendix I) provided information on urban residents‟ movement pattern, the proportion of household heads that are pedestrians, and their purposes of trip as pedestrians. Information on the questionnaire was divided into four sections. The first section provides information on household composition, and their socio economic characteristics. Questions on household composition include the nature of the family system, and the relationship of individual household to the household head. Information on socio economic characteristic include, age, sex, marital status, household size, level of education, income, occupation, auto ownership, household driver, and length of stay in the area under survey. The second section of the questionnaire focused on household heads and on-street person‟s origin, destination and trip purposes. Information obtained include intra-city trips to home, work, shopping, school, social function, hospital, leisure and sport, recreation and religious activities for four weeks. The third section of the questionnaire dealt with household and on-street persons‟ trip types. Questions in this section include origin of trip, trip destination, trip length; time spent on the journey, preferred mode of transport, days of the week the trip was made and distance of trip to residence. The fourth section focused household and on-street persons‟ trips as pedestrian. Questions in this section include preferred walking distance to the bus stations and land use activities, number of times household and on-street persons visit activities such as hospital, recreation centre, manufacturing industries, shopping malls, hotels and restaurants, retail shops as pedestrian. Other information includes availability of pedestrian facilities such as walkways, zebra crossing and pedestrian traffic light along individual route choice. In collecting this information retrospective activities listing questionnaire was used. The choice of a retrospective activities listing questionnaire is based on difficulties observed by Ojo (1990), Oyesiku, (1990), Solanke, (2005) and Samuel (2005) in the use of self-administered travel diary in Nigeria. So, urban residents were required to give account of their daily movement activities for a week. The choice of a week is based on: (i) the willingness of respondents to fill in the form not more than a week and (ii) the need to allow respondents relating their recent activities because; information of their movement is believed to be fresh in their memory. However, the survey lasted for 16 weeks (May 2009 and August, 2009) and this afforded the 53 UNIVERSITY OF IBADAN LIBRARY opportunity to collect essential information from household heads and on-street persons (pedestrians interviewed while walking on the street). 3.2.2.2 On-street Interview and Observational Survey In addition to household response, on-street interview based on the design on-street person‟s questionnaire was used. Personal observation was used to complement On- street interview along selected streets in the study area. Direct observation was equally used to record pedestrian flow, on-street parking, nature of walkways, and walkway width in the segregated road segments in the study area. Most of the questions asked are similar with household heads questionnaire as described in appendix II. Other information obtained includes socio-economic characteristics of the respondents, modal choice between (i) origin and boarding point, (ii) boarding point and alighting point, (iii) alighting point and destination and factors influencing pedestrians‟ walkability. 3.2.2.3 Field Measurement Pedestrian and vehicular traffic situation in the study area were recorded through a digital camera. Measurements of walkways and road width were made in fifty-six (56) road segments in the study. The number of pedestrian walking along road networks in the zones understudy were also recorded in a data sheet with the help of 35 field assistants. Furthermore, average speeds of vehicles along 56 road segments in the study area were also recorded during off-peak period of vehicular flow using radar gun. The choice of off-peak period was based on the fact that at peak period, the average speed of vehicles on the road network is approximately zero and this is due to congestion and long queue of vehicles along the road networks under study. 3.2.3 Sample Size Ikeja Local Government Area had a population of 313,196 inhabitants‟ comprising of 169,233 male and 143,963 female (National Bureau of Statistics, 2006). Considering the need for a representative sample for the study area (a part of Ikeja LGA) which is difficult to obtain through electoral districts, telephone directories and existing 54 UNIVERSITY OF IBADAN LIBRARY political wards in the local government area, the study made use of the number of buildings in each of the seventeen zones created. On the average, a zone has about 666 buildings, an average of 35 buildings per street. With an average of 15 people per building and total of 241 streets, the estimated population for the study area was about 126,525 ( ). Bruton (1975) however, recommended sample sizes between 1% for population under 50,000 and 10% for population over 100,000. Neumann (1994) on the other hand observed that the question of sample size can be addressed in two ways. One way is to make an assumption about the population of interest and use statistical equations to determine the degree of confidence (or number of errors) that is acceptable and in variance with the population. A second way and more frequently used method is a rule of thumb - a conventional or commonly accepted amount which is based on past experience and requirement of statistical methods. Supporting his argument, Neumann (1994) concluded that sample of 2,500 that is used for population of 200 million can also be used for 10 million population due to practical limitation of costs and time of the researcher. In this study, based on the costs, time and difficulties observed in administration of 170 pilot questionnaires and Neumann (1994) rule of thumb, one thousand (1000) household heads was selected as total sample size for household heads, and the sample size was drawn from anticipated sample size of sixteen thousand four hundred and twenty one (16,421) households of estimated population of 126, 525 in the study area. The distribution of 1000 sample size used in the study in table 3.1 was based on statistical allocation of sample size to strata. There are various methods of allocating sample size to strata of which include (i) Proportional Allocation, (ii) Equal Allocation, (iii) Optimum Allocation and (iv) Neyman Allocation (Okafor, 2002). Improvement in the methods of allocating sample size to strata increases from proportional allocation to that of Neyman allocation. Based on the data available for the study, proportional allocation was used. 55 UNIVERSITY OF IBADAN LIBRARY In proportional allocation, Thomsen (1976), Sukhatme, B. V. S., and Asok (1984) and Okafor (2002) observed that the stratum sample is selected such that the size of the sample is proportional to the total number of units in each stratum, such that ( ) varies directly as ( ) or ( ) varies directly as ( ). If the total sample that is to be allocated is ( ) , then the stratum sample is given as: 3.1 Where nh  Stratum sample size = the sample size of each zones n Total number of sample to be allocated ( ) Nh  Units or stratum of the anticipated population N  Observed total population = Estimated population of the study area. However, two hundred and five (205) on-street persons were randomly interviewed and the sample size was based on the number of on-street respondents that yielded to the interview. As earlier discussed in section 1.5 (limitations of the study) in chapter one, it was difficult to seek the attention of on-street person‟s during the survey period. Hence, the total number of on-street persons willing to be interviewed in the seventeen zones understudy summed up to two hundred and five (205) and number of on-street persons‟ interviewed in each of the zones understudy are presented in table 3.1. However, in the course of retrieving questionnaire administered to household heads in the seventeen zones understudy, 948 respondents were retrieved for analysis as shown in table 3.1. The questionnaires comprises of 606 male household respondents and 372 female household respondents. Regarding on-street persons, 84 respondents are male and 121 respondents are female. In summary, of the 1,205 questionnaires administered in the course of this study, 1,183 questionnaires were retrieved for analysis. The number (1,183) questionnaires retrieved for analysis, represents 98.2% response rate of the total (1,205) questionnaires administered and the value (98.2%) is supportable in analysing the findings of the study. 56 UNIVERSITY OF IBADAN LIBRARY 3.2.4 Questionnaire Administration and Sampling Procedure For the purpose of questionnaire administration, a pilot study of 140 household heads and 30 on-street persons‟ questionnaires was administered in the 17 zones. This exercise gave an insight into inherent problems in the questionnaire design and necessary corrections were made. There are 241 road segments (some not named) in the study area (see table 3.1) with a zone having a minimum of 7 streets and another having a maximum of 22 streets. Since identification of individual household head for administering the questionnaire in urban area like Ikeja is a non-trivial task, the following process was adopted. The process includes: (i) Stratification of streets into 17 zones in other not to oversample a particular zone. (ii) A simple random sampling was employed in the selection of streets that questionnaires were administered in each zone. (iii) On each street, a systematic random sampling technique was employed in the selection of housing units of the targeted respondents. (iv) Selection of eligible household heads within selected household was made. In a multi-housing unit, a simple random sampling technique was employed in the selection of a household head. (v) A random sampling technique was used in the administration of questionnaires to on-street persons along selected streets of the seventeen zones under study. 3.3 MEASUREMENT OF VARIABLES AND TECHNIQUES OF ANALYSIS Measurement of variables and techniques used in the study were discussed in section 3.3.1 and section 3.3.2. 3.3.1 Measurement of Variables Operationalizing variables in research is fundamental in the design of methods needed to achieve the stated objectives and hypotheses of the study. Variables used in the study include trip characteristics or purposes (work, religious, recreation, business, social, shopping, schooling, visiting, exercising); economic activities respondents 57 UNIVERSITY OF IBADAN LIBRARY engage in (industries, hotels and restaurants, shopping malls, financial and related institution, fast food points); and level of accessibility. The variables also include; respondents‟ socio-economic characteristics (age, sex, marital status, level of income and education, employment status, number of vehicles owned work location); preferred nature of walkways (safety, security, ,cleanliness, spacious); distance; time, season and weather. Other variables include lateral separation; motor vehicle‟s volume and speed; and vehicles‟ driveway access In order to measure these variables, some calibrations were made. The descriptions of how the variables were calibrated are discussed in relevant sections of the thesis. Techniques of analysis used in the thesis are discussed in the section that follows. 3.3.2 Techniques of Analysis Statistical techniques were used in analysing information obtained from questionnaires administered, field observations and field measurements made include descriptive and inferential statistical techniques. Descriptive statistical techniques such as frequency tables, bar graphs, percentages, averages, range and standard deviation were used to discuss some of the data collected on the field. The first hypothesis in this study seeks to examine the variation in respondents‟ maximum walking distance to walk to bus stations, various functions and services. Examining this hypothesis, means, range, standard deviation, student t-test and analysis of variance were used. The second and fourth hypotheses used multiple regression analysis to explain the dependent variables with respect to some explanatory variables. While the second hypothesis predicted the number of pedestrian trips made by household heads‟ and on-street persons‟ with respect to their trip types, number of times economic activities were visited and level of accessibility; the last hypothesis modelled pedestrians (level of safety) using pedestrian threshold (Level of Service) along walkways or road corridors. Pedestrian level of safety was predicted with respect to lateral separation, traffic volume, vehicle speed and drive way access and volume. 58 UNIVERSITY OF IBADAN LIBRARY In the second hypothesis, level of accessibility was measured using Shimbel index ( ∑ ) described in chapter two. Shimbel index was used in the computation of nodes, links of streets in each of the seventeen zones understudy. The third hypothesis examined household heads and on-street persons‟ decision to walk in an urban system and their pattern of movement with respect to explanatory variables such as socio-economic characteristics of household heads and on-street persons, preferred nature of pedestrian facility and factors such as distance, time, season and weather. The statistical techniques used are Logit regression model and Analytical Hierarchical Process (AHP) model. In analysing the third hypothesis however, logit or logistic regression analysis was used. The choice of logistic model over Ordinary Least Square (OLS) regression, linear probability, discriminant function analysis and probit models was that, linear probability model assumes linear increase in the explanatory variables as well make the incremental effects of these variables constant, which is not applicable in dichotomous response variables. Discriminant function analysis on the other hand has many assumptions, of which the data for the explanatory variables must represent a sample from multivariate normal distribution. Although the relationship between probit and logit models is almost similar in concept and interpretation, the logit model is preferable if data used for the study are not normally distributed and involve complex samples. For this research, the data used are not likely to be normally distributed and also involve a two-stage sampling techniques. Statistical software used includes Statistical Packages for Social Sciences (SPSS 17.0). Statistical Packages for Social Sciences (SPSS 17.0) was used to run both descriptive and inferential statistical analyses of the study. They were also used in the explanation pedestrian movement in the study area. Nevertheless, the study used both qualitative and quantitative approaches in achieving the aim and objectives of the research. 59 UNIVERSITY OF IBADAN LIBRARY 3.4 SUMMARY The chapter discusses hypotheses of the study and methods of analysis used in addressing the study objectives and the set hypotheses. The chapter explored the primary and the secondary sources of information of the research. Furthermore, the chapter explains the sample size, questionnaire administration and sampling procedure of the study. In the same manner, the techniques used in analysing the research hypotheses were discussed by justifying the use of such techniques for the study‟s hypotheses. 60 UNIVERSITY OF IBADAN LIBRARY CHAPTER FOUR PHYSICAL, SOCIO-ECONOMIC CHARACTERISTICS AND MOVEMENT PATTERNS OF PEDESTRIANS 4.1 INTRODUCTION This chapter examines the physical and socio-economic characteristics of the study area in order to explaining the activities that generate pedestrians‟ movements. The chapter discusses Lagos and Ikeja, where the subunits that make up the study area were mapped. The chapter is divided into three subsections under the following headings: (i) Ikeja and the Study Area, which describes the geographical setting of Ikeja, (ii) land use pattern in Ikeja, which explains the landuse activities in Ikeja and the study area in relation to urban spatial structure models discussed in chapter two and (iii) patterns of pedestrian movements in Ikeja. 4.2 IKEJA AND THE STUDY AREA th Ikeja is a Local Government Area in Lagos State. Lagos was created in 27 may 1967 in the south western part of Nigeria. Lagos is the economic, financial and commercial nerve centre of Nigeria and it attained mega city status in 1995 (see figure 4.1). The capital of the Lagos state is located at Ikeja which is one of the twenty local government areas of the state (see figure 4.2). Ikeja occupies a unique position among the 20 local government areas in Lagos state. It is located on and occupies an area of about 46.2 kilometres square in the State (Lagos State Physical Planning and Urban Development, 2008). It is bounded by Agege LGA in the in the west, Ifako-Ijaye LGA in the north-west, Alimosho LGA in the south-west, Kosofe LGA in the east and east, Oshodi-Isolo and Mushin LGA in the south and Ogun State in the north (see figure 4.3). 61 UNIVERSITY OF IBADAN LIBRARY Proposed Ikeja Model City Plan, June 2008 Figure 4.1: Political map of Lagos state showing Ikeja Local Government Area. Source: Lagos State Physical Planning and Urban Development (2008). 62 UNIVERSITY OF IBADAN LIBRARY Figure 4.2: Ikeja Local Government Area and Development Council Areas. Source: Lagos State Physical Planning and Urban Development (2008). 63 UNIVERSITY OF IBADAN LIBRARY Figure 4.3: Ikeja Local Government Area within the Bounded Areas. Source: Lagos State Physical Planning and Urban Development (2008). 64 UNIVERSITY OF IBADAN LIBRARY Ikeja is one of the local government areas that double as the state capital. It is also one of the most developed, harbouring most of the medium scale industrial establishments in the state. Ikeja LGA has busy streets such as Allen Avenue, Opebi Road, Simbiat Abiola Road, Oba Kodesoh street, Awolowo way, Adeniyi Jones Avenue, Aromire Avenue, Toyin street, Oriyomi street, Olowu street, Afariogun street, Oba Akran Avenue, Francis Oremeji street, Ola-Ayeni street, Pepple street, Unity street, Otigba street and so on. Ikeja Local government area has high rate of accessibility in terms of transportation network to almost all communities in the local government area. Apart from being a local government area with the busiest local airport and largest International Airport, Ikeja also serves as rail terminal to Tran-city rail services of Nigeria Railway Corporation from Ifo in Ogun State to Lagos State. It is also a home of one of the largest ICT centre in West Africa popularly called „Computer Village’. Ikeja is equally a home of many daily newspapers, Radio (Radio Lagos, Choice FM, Eko FM) and (Television stations Lagos State Television, Muhri International Television). However, the study area is small part of Ikeja LGA as shown in figure 4.4. Figure 4.4 shows the study area with the classified names of the zones. Having one of the largest market areas in Sub-Saharan Africa, Ikeja contains different types of land uses that generate both human and vehicular traffic on daily basis. The land use types that generate both human and vehicular movements, particularly pedestrian movement in the study area are discussed in section 4.3. 65 UNIVERSITY OF IBADAN LIBRARY Figure 4.4: Map of the Study Area. Source: Field survey, 2009. 66 UNIVERSITY OF IBADAN LIBRARY 4.3 LAND USE PATTERN IN THE STUDY AREA Trips are embarked upon in cities for several reasons, and landuse analysis is a convenient way to study the activities that provide the basis for urban transport components (that is trip generation, trip distribution, modal split and traffic assignment) discussed in chapter two. Travel activity patterns are greatly influenced by urban morphology or the general land use arrangement. The travel volume of an area therefore depends on the numbers of trips that starts or end there. The land uses of the study area were discussed under the following classifications. (i) Residential Landuse (ii) Industrial Landuse (iii) Commercial Landuse (iv) Social Infrastructure 4.3.1 Residential Landuse Residential landuse is predominant in the study area. Due to the high level of commercial and industrial activities in Ikeja, one might not notice the predominance of residential landuse. Ikeja is made of planned and unplanned residential area. The buildings are engulfed by urban land development. Housing layout patterns are of both close and open types with most compounds containing more than two buildings. Most of these buildings are of Brazilian type and some are bungalows with rectangular shapes. The layout shows little regard for modern planning standards because majority of the houses do not have access roads. This is common in some parts of Otigba Area, Obanta Area, Unity Area, Alabi Area, Awosika Area and in the core area of Ikeja. The core areas of Ikeja are now covered and surrounded by new buildings that are made up of residential and commercial developments with modern designs. These developments are found along Kodesoh Road, Obafemi Awolowo Way, Olowu Road, Opebi link, Oba Akran Avenue, Aromire Street, Adeniyi Jones Avenue, Opebi Street, 67 UNIVERSITY OF IBADAN LIBRARY Allen Avenue, and Toyin Street amongst others. Generally, residential buildings in the study area feature all categories of buildings except the non-use of multi-storey blocks of more than six floors for residential buildings. However, there are commercial and hotel buildings of more than six floors in the study area. Ikeja has modern government and private residential estates. Such estates are located along Kodesoh Road, Obafemi Awolowo way, Central Business District, Adeniyi Jones, Opebi, Shonibare Estate, WEMA Board Estate, Omole Estate and Government Reservation Area (GRA). The main factors that contributed to the expansion of Ikeja into an important residential area was the establishment of Government Reservation Areas in the 1930s, the establishment of industrial estate in the late 1950s, the establishment of many estates in early 1960s, and the provision of houses for low-income industrial workers by the then Western Nigeria Housing Corporation (Aluko, 2004). Different types of housing units can be found in the study area. The residential landuse comprises of both high and low income group. The higher income group live in areas of low density residential areas and are characterised by tracts of open land with detached buildings and single family units. Figure 4.5 show land use pattern in in the study area as obtained from Lagos State Physical Planning and Urban Development. In recent times, private developers are actively engaged in erecting more houses in Ikeja to meet the increasing demand arising from the rapid growth of the city and Lagos metropolitan area. Also, areas that were strictly residential are now being used as religion, trading, industrial, financial, and commercial centres. 68 UNIVERSITY OF IBADAN LIBRARY Figure 4.5: Land Use Map of the study area. Source: Lagos State Physical Planning and Urban Development (2008). 69 UNIVERSITY OF IBADAN LIBRARY 4.3.2 Industrial Landuse Industrial land uses are prominent especially in the form of industrial streets in many part of the study area. Ikeja is a leading industrial area not only in Lagos state but in Nigeria in general. The most prominent industrial estate is Ikeja Industrial Estate located along Oba Akran Avenue. Other industrial areas within Ikeja are in Ladipo Oluwole Road, Henry Carr Street, Metal Box Road Acme Road, Acme Crescent, Akanni Doherty, Cocoa Industrial Road, Akilo Road, Dampson Street, Vanni Close, Universal Metal Crescent, Surulere Industrial Road, Lateef Jakande Road, and Adeniyi Jones Avenue. These industries are made up of large, medium and small scale industrial complexes. Sapara Street, is involve in textile, glass and metal fabrication, food processing company, pharmaceutical , publishing, plastics and construction materials (see Table 4.1) for details. Blue chip industries that are located in the seventeen zones in and around Ikeja include Guinness, Nigerian Textile, Niger Paints, Hagemeyer, Specomills Plc, Dunlop Plc (now focus in marketing the product and also divest into properties), WAHUM, West African Distillers, Melta Box, Tower Aluminium, Cadbury, 7-Up and Coca- Cola. Other areas of industrial activities are Oregun road corridor, where there are many publishing companies which include Newswatch Magazine, Prime People, Quality Magazine and Clay processing industries. 4.3.3 Commercial Landuse With respect to commercial activities, Ikeja is next to Lagos Island (Balogun, Odumosu and Ojo, 1994). Like Industrial landuse, many streets in Ikeja are commercial streets. Its commercial activities range from banking, finance, shopping complexes, hotels, pharmaceutical stores, canteens, books-shops, auto-sales, electronics, textiles, decoration, gift-items, clinic and hospitals, professional services and business centres. These activities are well represented in the zones understudy. Table 4.1 shows some of these activities. 70 UNIVERSITY OF IBADAN LIBRARY Table 4.1: Estimated Number of Economic / Land Use Activities in the Study Area. Zones Zones Name Number of Economic/Land Use Activities HOSP INDU HRES GNGA FINI SCHL SHPM FAST RTAIL 1 Otigba Area 2 28 15 18 16 7 2 6 2673 2 Awosika Area 6 5 4 25 8 8 2 2 654 3 Obanta Area 4 5 4 8 6 5 3 2 786 4 Kudeti Area 7 25 3 28 18 18 1 8 124 5 Akeem Balogun 2 10 5 20 16 3 1 2 20 Area 6 Ajanaku Area 3 11 9 25 20 11 1 12 212 7 Governor Area 2 10 2 8 2 3 0 0 106 8 Kadiri Area 2 3 12 18 2 8 1 0 121 9 Olanrewaju Area 3 17 1 22 18 4 0 4 24 10 Mobolaji 2 10 2 48 10 6 0 2 28 Johnson Area 11 Kasumu 2 5 8 16 8 10 0 4 236 Aleshinloye Area 12 Morrison Area 2 22 5 24 10 16 0 4 258 13 Allen Area 6 6 18 29 16 12 1 10 269 14 Unity Area 3 5 15 18 8 8 0 2 1047 15 Alabi Area 3 3 16 8 10 16 0 6 325 16 Community Area 2 9 3 6 4 10 0 1 109 17 Acme Area 2 9 6 8 2 8 1 1 848 Total 53 183 128 329 174 153 13 66 7220 Source: Field Survey, 2009. Note: HOSP = Hospitals, INDU = Industries, HRES = Hotel and Restaurants, GNGA = Governmental and Non-Governmental Agencies, FINI = Financial Institutions, SCHL = Schools, SHPM = Shops and Shopping Malls, FAST = Fast Food Points, RTAIL = Retail Activities. 71 UNIVERSITY OF IBADAN LIBRARY Banks are well represented in the zones understudy in Ikeja. These Banks are located along Oba Akran Avenue, Awolowo Way, Simbiat Abiola Road or Medical Road, Toyin Street, Allen Avenue, Aromire Avenue, Adeniyi Jones Avenue, Lateef Jakande Road, ACME Road, Opebi Road, Opebi Link, Kudirat Abiola way, Billings‟ Way, to mention but a few. There are also several micro-financial institutions in the seventeen zones in Ikeja and these include Micro-Finance banks, Bureau De Change and finance houses. Trading, shopping and markets are some of commercial activities found in the study area. Daily and nights markets in the study area include Ipodo, Afariogun, Alade and Ashade markets. Modern shopping complexes in the study area include Shoprite, Ikeja Plaza, Oshopey plaza, Rodeo Shopping Mall, Obafemi Awolowo Plaza, UTC, Juli Pharmacy, Masco Supermarket, and Cash „N‟ Carry. It is also important to note that some of the major roads have grown into full scale shopping streets. Such streets include Obafemi Awolowo way, Allen Avenue, Adeniyi Jones, Oriyomi Street, Otigba Street, Olowu Street, Ola-Ayinde Street, Unity Road, Oba Kodesoh Street, Opebi Street, Opebi link and some part of Kudirat Abiola way. Other streets are Billing‟s way, Toyin Street and entire streets in Otigba Area (1) of the study area popularly known as ‘computer village’. Generally many other collector and access roads in the study area have been engulfed by commercial activities and this generates pedestrian movement in and around the city. Another commercial land use in the study area are hotels and restaurants. Hotels of international standards are available in the study area. The hotels in the study area include Sheraton Hotel and Towers, Airport Hotel, Ikeja Federal Palace Hotel, De Renaissance Hotel, Water Park, Jabita Hotel and Base Water Hotel. There are also many Canteens and Restaurants in the subunits. Some of the residences in the subunits have been converted into restaurants. The number of hotels and restaurants observed in the study area is as shown in table 4.1. However, figure 4.6 shows observed land use types in the study area. 72 UNIVERSITY OF IBADAN LIBRARY Figure 4.6: Observed Land Use Types in the study area. Source: Field Survey, 2009. 73 UNIVERSITY OF IBADAN LIBRARY 4.3.4 Social Infrastructure Social infrastructure also referred to as community facilities and services is one of the landuse activities observed in the study area. Some of these facilities are institutional, educational, health, religious and recreational. There are many institutional facilities that are of public and semi-public landuse. These institutional facilities include administrative buildings for Federal, State and Local Governments. The State Government Permanent Secretariat is located at Alausa along Obafemi Awolowo Way; the former State Government Secretariat is along Oba Akinjobi Street. The secretariat still accommodates many state government functions. Some of the Federal Government Establishments in Ikeja are Power Holding Company Nigeria Plc., offices, Nigeria Postal services, Ministries, Judiciary Buildings, Police College, Department of Customs and Excise Training School, Army Barracks, Nigerian Air Force Base, the State secretariat and Ikeja Local Government Secretariat both located along Obafemi Awolowo Way. Regarding educational facilities, there are over one hundred and twenty five secondary schools and over one hundred primary schools in Ikeja. There is also a Women Training Centre, and over fifty Adult Literacy Centres. The Police College at Ikeja is famous for the training of police officers. The state main library is located at Ikeja while there is a mobile library at the old secretariat. At Ayo-Alabi Street, there is training centre for Customs officials. Lagos State University Teaching Hospital meant to train Doctors, is also located in Ikeja. There are private and public health facilities in the study area. The Specialist Hospital (Eko Hospital) is located along Mobolaji Bank Anthony Way. Lagoon hospital is located along Obafemi Awolowo Way. The two hospitals are perhaps one of the best private hospitals in the country. Ikeja Local Government Area operates over 47 clinics out of which six of them are primary health centres. There is also a State General Hospital in Ikeja. The State Hospital Health Services are being complemented by many privately owned clinics and hospitals among which are Holy Trinity Hospital, Eko Hospital, Nene Clinic, Unity Hospital, Maritol Hospital, Dolapo Clinic, Soleye Hospital and several ones. 74 UNIVERSITY OF IBADAN LIBRARY Various religious groups peacefully co-exist in and around the study area. These religious groups include Muslims, Christians and Traditional Religious worshippers. Many churches in Ikeja comprise of both orthodox and pentecostals. These include Catholic Church, Anglican Church, Methodist Church, Baptist Church and many Spiritual Churches. Many residential areas are currently being used for fellowship and some floors in multi-storey buildings accommodate religious functions. This attribute is common among the Pentecostal Churches and they include Winner‟s Chapel, Redeemed Christian Church of God, Believer‟s World, The Redeemed Evangelistic Ministry and host of others. Different Islamic denominations among which include Ansar-Ud-Deen, Nawarudeen, Ahmadiyah, Nasfat and many others are also present. The use of many of the residential areas in the study area for religious activities has led to increasing pedestrians‟ movement along many of the streets on daily basis. Recreational facilities flourish in the subunits. These recreational facilities are of passive and active types. They are passive because some of the facilities generate pedestrian traffic during festive periods, and are active because some of the facilities generate pedestrian traffic on daily basis. Some of these facilities are available in individual homes, hotels, public places and schools. Places with good recreational facilities such as lawn tennis and swimming pools, table tennis, and video viewing centres are Lagos Country Club, Ikeja Club, Water Park, Sheraton Hotel, Ikeja Federal Palace hotel and Airport hotel. Other recreational areas are Lagos State Television Complex, Muhri International Television Complex, Water Parks, Fela Anikulapo Kuti Shrine (African Shrine), The Golf Course located near the Government Reservation Area and various public places. The study showed that the study area consists of different land uses. Ikeja is positioned as the state capital, seat of Lagos state government, headquarter of the local government, location of both Local and International Airports, encourage pedestrians activities and movement in and around the city. Table 4.2 shows the mixed land use types in Ikeja and the distinctive characteristics. 75 UNIVERSITY OF IBADAN LIBRARY Table 4.2: Landuse Types of the Seventeen Zones Understudy. Percentage of Land Use Activities in the Zones Zones Name of Zones Land use Classified Name Residential Commercial Religious Financial Institutional Industrial Type 1 Otigba Area Mixed Resido-Commercial 26 48 10 13 3 - 2 Awosika Area Mixed Resido-Commercial 31 43 13 8 3 2 3 Obanta Area Mixed Resido-Industrial 29 10 15 18 2 26 4 Kudeti Area Mixed Resido-Commercial 35 28 16 20 1 - 5 Akeem Balogun Mixed Resido-Institutional 21 16 14 5 41 3 Area 6 Ajanaku Area Mixed Resido-Commercial 21 46 12 18 3 - 7 Governor Area Mixed Resido-Institutional 38 16 18 2 26 - 8 Kadiri Area Mixed Resido-Commercial 38 27 13 6 10 6 9 Olanrewaju Area Mixed Resido-Industrial 17 12 14 7 15 35 10 Mobolaji Mixed Resido-Institutional 21 5 16 10 36 12 Johnson Area 11 Kasumu Mixed Resido-Commercial 31 30 16 6 15 2 Aleshinloye Area 12 Morrison Area Mixed Resido-Commercial 37 33 18 8 4 - 13 Allen Area Mixed Resido-Commercial 31 41 9 13 5 1 14 Unity Area Mixed Resido-Commercial 26 48 11 13 2 - 15 Alabi Area Mixed Resido-Commercial 33 36 11 16 4 - 16 Community Mixed Resido-Commercial 22 30 12 16 17 3 Area 17 Acme Area Mixed Resido-Industrial 21 2 16 6 9 46 Total Average 28.1 27.7 13.8 10.9 11.5 8.0 Source: Field Survey, 2009 76 UNIVERSITY OF IBADAN LIBRARY The mixed nature of landuse and their trip generating capability in Ikeja LGA and the subunits attract both vehicular and pedestrian traffic. Trip generation involves trip production and trip attractions for individual zones which are related primarily to the type of land use. Theories of urban spatial structure {Concentric Zone Theory (Burgess, 1925); Sector Theory (Hoyt, 1939) and Multiple Nuclei (Harris and Ullman, 1945)} discussed in chapter two of the thesis, provide basis for mobility in the study area. The subunits into which the study area was divided show the characteristics of sector model of axial growth along transport routes with few income groups tend to live in distinct area. Areas where such „distinct area‟ can be found in the subunits are Allen Area (13), Alabi Area (15) and Kudeti Area (4). Subunits that show similar characteristics of multiple nuclei model include Otigba Area (1) , Governor Area (7), Awosika Area (2), Obanta Area (3), Akeem Balogun Area (5), Ajanaku Area (6), Kadiri Area (8), Olanrewaju Area (9), Secretariat Area (10), Kasumu Aleshinloye Area (11), Morrison Area (12), and Unity Area (14), Community Area (16) and Acme (17). In these subunits, a Central Business Districts (CBD) exists in each of the zones. Commercial and business activities are concentrated in the Central Business District where jobs, office buildings and especially stores are located as observed in the classical models. However, some small business activities that cannot pay the high rent are found along the street corridors of the Central Business District (CBD) in the study area. The activities of these small businesses along the street corridors of the CBD is in variance with one of the characteristics of multiple nuclei model that states that „certain activities unable to generate enough income to pay for high rent in particular location are forced to locate at the site with low rents’ Section 4.4 of the study thus focused on the movement of people in and around Ikeja, with emphasis on pedestrian movement and their pattern of flow in the study area. 4.4 PATTERN OF PEDESTRIAN MOVEMENT IN THE STUDY AREA The needs of people lead to engagement in activities such as work, markets, business, shopping centres, religious camps, recreations and schools. These activities necessitated day to day movement of people. As indicated in chapter two of the thesis, 77 UNIVERSITY OF IBADAN LIBRARY an input to an urban transport system is the demand for the movement of people and goods between urban activity centres as provided by Ullman (1956) spatial interaction theory. Furthermore, concentration of businesses, availability of industries, commercial activities, institutional and recreational facilities, local and international airports and other landuse and economic activities, contribute to increasing number of vehicles and people found in Ikeja on daily basis. These landuse and economic activities enhance trip generation by attracting people and vehicles and as well produce people‟s movement in and around Ikeja. This section therefore discusses movement of people in Ikeja with respect to transport activities and pedestrians‟ movements. The section also examines the flow pattern of pedestrian along the road networks. 4.4.1 Transport Activities The dominant mode of transport in Ikeja is road transport, and the players managing transport system in the city are government and private individuals. There are different types of road networks in Ikeja. These roads include six federal roads, thirty state roads and over hundred local government roads. Many of these roads are tarred and the means of transporting people include taxi cabs, tricycles, motorcycles, buses of various types and walking. Other forms of transportation in and around the study area include railway, with its line running along the western boundary of Ikeja and one of its terminals located at Ikeja. Agege Motor Road which is parallel to the railway lines splits into two with one carriageway connecting Agege Area and the dual lanes connecting Ota area in Ogun State. The Lagos-Ibadan expressway also runs along the eastern boundary of the city (see Figure 4.7). The roads helped in carving out the subunits (the study area) in Ikeja, and as well delineating the study area into zones. Walking is common not only because there is limit to vehicular movement within the city but, being the oldest form of transport, transit activities and economic activities of people in the study area are achieved by walking to points of activities. 78 UNIVERSITY OF IBADAN LIBRARY Furthermore, the zones not only act as transit point to commuters but also serve as point of origins and destinations to people that walks or trek from their homes and end their trips respectively in the nooks and crannies of streets in the seventeen zones. The road map of the study area in Ikeja local government area is presented in Figure 4.8 and that of Figure 4.9 clearly shows the road network of the study area. 79 UNIVERSITY OF IBADAN LIBRARY Figure 4.7: Road Network Map of Ikeja Local Government Area. Source: Lagos State Metropolitan Transport Authority (2008). 80 UNIVERSITY OF IBADAN LIBRARY Figure 4.8: Road map of the study area in Ikeja Local Government Area. Source: Field Survey, 2009. 81 UNIVERSITY OF IBADAN LIBRARY Figure 4.9: Road map of the Study Area. Source: Adapted from figure 4.8. 82 UNIVERSITY OF IBADAN LIBRARY 4.4.2 Pedestrian Movement in the Study Area Generally, roads in Ikeja were laid when the city had a single centre and before rapid growth in personalised forms of motorised transport. The primary road network radiates from the city centre to the surrounding areas, but orbital or circumferential links was missing. Some of the roads have one lane in each direction, where the roads are wide; a lane is often occupied by parked vehicles. Where they are narrow, they are often occupied by traders, private vehicles, motorcycles, commercial vehicles, and pedestrians have no choice than to walk in the middle of the road as shown in plate 4.1, and plate 4.2. About two hundred and thirty-four, (97.1%) of roads in the study area lack pedestrian facilities. Sidewalks are missing in most parts of Ikeja road network, so pedestrians and vehicles share available space and this leads to unguided movement of pedestrians on the road network (see Plates 4.3 and 4.4). The implication is the frequent conflict between pedestrian and vehicular traffic; in order words, pedestrians are exposed to road traffic accidents. Where pedestrian sidewalks do exist, such as WEMCO Road, Lateef Jakande Road, Awolowo Way and Mobolaji Bank Anthony Way, they are poorly maintained, contain open drainage, grown with weeds or they are taken over by tricycles, or on- street traders or by the adjoining properties. Examples of these menaces can be found in Oriyomi, Afariogun, Ipodo, Unity road, Ola-Ayinde streets to mention but a few (see Plates 4.5, 4.6 and 4.7). Major problems of road transportation in the study area and the entire Ikeja LGA include high vehicular traffic, traffic congestion, non-availability of defined bus stops, inadequate pedestrian walkways and pedestrian crossings and the absence of car parks (see Plate 4.8). 83 UNIVERSITY OF IBADAN LIBRARY Plate 4.1: Pedestrians Walking in the Middle of Ola-Ayeni Street in Ikeja. Source: Field Survey, 2009. 84 UNIVERSITY OF IBADAN LIBRARY Plate 4.2: Parked Vehicles and Trading Activities characterise Oriyomi Street. Source: Field Survey, 2009. 85 UNIVERSITY OF IBADAN LIBRARY Plate 4.3: Unguided Movement of Pedestrians at a section of Awolowo Way Source: Field Survey, 2009. 86 UNIVERSITY OF IBADAN LIBRARY Plate 4.4: Pedestrians Walking by the Roadside along a Section of Awolowo Way Source: Field Survey, 2009 87 UNIVERSITY OF IBADAN LIBRARY Plate 4.5: Walkway with Grown Weeds along WEMPCO Road. Source: Field Survey, 2009 88 UNIVERSITY OF IBADAN LIBRARY Plate 4.6: Covered Drainage Used for Pedestrian Walkway along Lateef Jakande Road. Source: Field Survey, 2009 89 UNIVERSITY OF IBADAN LIBRARY Plate 4.7: Pedestrians Avoiding Uncovered Drainage Used as Walkway along Oba Kodesoh Street. Source: Field Survey, 2009 90 UNIVERSITY OF IBADAN LIBRARY 1 Plate 4.8: Congested Otigba Street with Pedestrians Competing with Vehicles Source: Field Survey 2009. 1 Plate 4.1 - Plate 4.8 show roadway situations in the study area – Uncovered drainage, congested roadways, and walkways grown with weeds. A roadway where vehicles and pedestrians claim right -of -way by competing with available road space. 91 UNIVERSITY OF IBADAN LIBRARY 4.4.3 Pedestrian Flow Patterns in the Study Area The flow of people along the streets in the study area was captured through direct observation and counting. The observation and counting lasted for 8 weeks with the help of 35 field assistants between 7:00 am to 7:00 pm. The numbers of pedestrian passing through both side of the road network were counted by two field assistants at selected points on each road network in the study area for every one hour. Table 4.3 shows aggregate data of pedestrian flow and figure 4.10 shows the map of average flow pattern of pedestrian in the study area. Details of the data of pedestrian flow pattern along the road networks of the seventeen zones are presented in appendix III. 92 UNIVERSITY OF IBADAN LIBRARY Table 4.3: Pedestrian Circulation in the Study Area Zones 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm-6pm 6pm - Zone Zonal Standard Average 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 7pm Total Average Deviation Street Code Flow Flow 1 9178 16081 20224 19315 15644 15233 14005 16068 21254 26988 29670 21837 225497 18791 5445.59 6833 2 5172 7905 2234 1402 1600 2524 1995 1038 930 5857 7378 1871 39906 3326 2425.22 4988 3 889 2001 1781 1369 1313 1575 1349 1168 2160 4050 3028 1983 22666 1889 845.87 1133 4 526 3353 2389 1368 1463 2615 1306 1303 1587 2858 4739 3038 27551 2212 1123.04 1148 5 692 1074 1447 1484 1002 815 367 376 578 3277 3593 2052 16757 1396 1027.38 3351 6 601 1289 1334 858 895 577 555 525 1063 1824 1972 1201 12694 1058 466.91 604 7 856 3540 3596 1613 1506 1195 1093 723 691 3394 5440 2898 26545 2212 1457.16 6636 8 540 1900 1767 1357 757 632 495 439 384 2808 5000 3167 19246 1604 1368.41 1375 9 507 821 1039 871 947 688 671 553 593 917 1187 801 9595 800 197.18 600 10 212 421 620 605 599 397 419 347 288 685 863 657 6113 509 182.46 873 11 513 2609 983 917 1467 1898 372 475 499 1531 3156 790 21057 1268 860.93 1914 12 791 1373 1597 1588 1125 875 806 615 771 2439 3730 2632 18342 1529 912.41 764 13 1479 4253 4551 2167 2106 3297 1411 1503 1790 4550 5959 3741 44807 3734 1464.96 1948 14 4171 8789 9361 7851 7298 6302 4726 5799 8607 14399 15639 8578 101520 8460 3336.20 2820 15 2442 5054 4915 4021 3578 3287 2307 2517 4255 7627 9149 4954 54106 4509 1992.98 2459 16 587 1939 2586 810 671 1018 472 393 750 1997 1872 681 13776 1148 708.01 984 17 1234 4443 2214 1444 2105 3069 758 703 742 2591 4951 2356 26610 2218 1338.16 1401 Total 30390 66845 62638 49040 44076 45997 33107 34545 46942 87792 107326 63237 679935 56663 H. Average 1788 3932 3685 2885 2593 2706 1947 2032 2761 5164 6313 3720 S. Deviation 2277.72 3806.91 4608.68 4435.33 3610.29 3455.16 3189.25 3735.02 5029.51 6295.79 6765.04 4905.74 93 UNIVERSITY OF IBADAN LIBRARY . Figure 4.10: Average Pedestrian Flow per Hour along the Road Networks in the Study Area. Source: Field Survey, 2009 94 UNIVERSITY OF IBADAN LIBRARY Column sixteen and Figure 4.11 show average zonal flow of pedestrians in the study area. From figure 4.11, Otigba Area (1) has the highest (18,971) average pedestrian flow for the 12 hour period in the seventeen zones. Unity Area (14) is next to Otigba Area with an average pedestrian circulation of 8,460 people from 7am to 7pm.Coincidentally, Otigba Area and Unity Area are very close to each other and the two zones are zones of mixed land uses as presented in figure 4.6. Kudeti Area (4) and Governor Area recorded equal number (2,212) of pedestrian flow. Pedestrians average zonal flow from 7am to 7pm in order of magnitude include Alabi Area (15), Allen Area (13), Awosika Area (2), Acme Area (17), Obanta Area (3), Kadiri Area (8), Morrison Area (12), Akeem Balogun Area (5), Kasumu Aleshinloye Area (11), Community Area (16), Ajanaku Area (6), Olanrewaju Area (9) and Mobolaji Johnson Area (10)with values (4,509), (3,734), (3326), (2218), (1,898), (1,604), (1,529), (1,396), (1,268), (1,148), (1058), (800) and (509) pedestrians respectively. Figure 4.12 shows average pedestrian flow along the road networks in each zone. From figure 4.12, Otigba Area with thirty three road networks has an average flow of (6, 833) pedestrians between 7am and 7pm in each of the street. Governor Area with four road networks is next to Otigba Area. An average flow of pedestrians in each of the four streets is 6,636 pedestrians. Awosika Area with an average flow of 4,988 pedestrians along each road network is next to Governor Area, and it has eight road networks. Figure 4.12 further shows that Akeem Balogun Area with five road networks recorded an average flow of 3,351 pedestrians along each of the road network between 7am to 7pm. Unity Area has the highest road networks among the zones. With thirty-six road networks, Unity Area has an average flow of 2,820 pedestrians walking on each of the road networks between 7am and 7pm of the study. Average pedestrian flow along the road networks in other zones in the study area in order of magnitude include Alabi Area, Allen Area, Kasumu Aleshinloye Area, Acme Area, Kadiri Area, Kudeti Area, Obanta Area, Mobolaji Johnson Area, Morrison Area, Ajanaku Area and Olanrewaju Area with (2,459), (1,948), (1,914), (984), (873), (764), (604) and (600) pedestrians respectively. 95 UNIVERSITY OF IBADAN LIBRARY Pedestrians Zonal Average Flow 20000 18000 16000 14000 12000 10000 8000 6000 4000 2000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Zones Figure 4.11: Pedestrians Zonal Average Flow in Ikeja. 96 Number of Pedestrians UNIVERSITY OF IBADAN LIBRARY Pedestrians Average Street Flow 8000 7000 6000 5000 4000 3000 2000 1000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Zones Figure 4.12: Pedestrian Average Street Flow in Ikeja. 97 U Number of Pedestrians NIVERSITY OF IBADAN LIBRARY Figure 4.13 shows pedestrians‟ hourly flow across the seventeen zones and figure 4.14 shows average hourly flow of pedestrians in the study area. Regarding the period of highest pedestrian flow in the study area, figure 4.13 shows that pedestrian flow is very high between 5pm and 6pm in fourteen zones. These zones are Otigba Area, Kudeti Area, Ajanaku Area, Governor Area, Kadiri Area, Olanrewaju Area, Mobolaji Johnson Area, Kasumu Aleshinloye Area, Morrison Area, Allen Area, Unity Area, Alabi Area, and Acme Area with (29,670), (4,739), (3,593), (1,972), (5,440), (5,000), (1,187), (863), (3,156), (3,730), (5,959), (15,639), (9,149) and (4,951) pedestrians respectively. In Awosika Area, the highest flow (7,905 pedestrians) was recorded between 8am and 9am.Between 4pm and 5pm, the highest pedestrian flow (4,050 pedestrians) and (1,997 pedestrians) was recorded in Obanta Area and Community Area respectively. Concerning the lowest period of pedestrian circulations in the study area, the lowest pedestrian flow in Otigba Area (9,178 pedestrians), Obanta Area (889 pedestrians), Kudeti Area (526 pedestrians), Olanrewaju Area (507 pedestrians), Mobolaji Johnson Area (212 pedestrians) and Unity Area (4,171 pedestrians) was recorded between 7am and 8am.In Akeem Balogun Area, Kasumu Aleshinloye Area, Allen Area, and Alabi Area, the lowest pedestrian flow (367 pedestrians), (372 pedestrians), (1,411 pedestrians) and (2,307 pedestrians) was recorded between 1pm and 2pm. Zones with lowest pedestrian flow between 2pm and 3pm are Ajanaku Area (525 pedestrians), Morrison Area (615 pedestrians), Community Area (393 pedestrians) and Acme Area with 615 pedestrians. Between 3pm and 4pm, the lowest pedestrian flow was recorded in Awosika Area, Governor Area and Kadiri Area with 930, 691 and 384 pedestrians respectively. Figure 4.14 on the other hand shows an average hourly flow of pedestrians at aggregate level. As expected based on the results in figure 4.13, the results in figure 4.14 indicates that the lowest average hourly pedestrian flow in the study area was recorded between 7am and 8am and the highest flow was recorded between 5pm and 6pm with flow rate of 1,788 and 6,313 pedestrians respectively. Between 7am and 8am, there is 54.5% increase in pedestrian flow. Between 9am and 10am and 3pm and 4pm pedestrian flow in the study area decreases by 25.6% and increases by 46.5% between 3pm and 4pm and 4pm and 5pm. Furthermore, between 4pm and 5pm, and 5pm and 6pm pedestrian flow increases by 18.2%. 98 UNIVERSITY OF IBADAN LIBRARY Pedestrians Hourly Flow 35000 30000 25000 7am – 8am 8am – 9am 9am-10am 20000 10am-11am 11am-12pm 12pm-1pm 1pm-2pm 15000 2pm-3pm 3pm-4pm 4pm-5pm 5pm-6pm 10000 6pm -7pm 5000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Zones Figure 4.13: Pedestrians Hourly Flow in Ikeja 99 Number of Pedestrians UNIVERSITY OF IBADAN LIBRARY Pedestrians Average Hourly Flow 7000 6000 5000 4000 3000 2000 1000 0 Time of the Day Figure 4.14: Pedestrian Average Hourly Flow in Ikeja. 100 Number of Pedestri ans UNIVERSITY OF IBADAN LIBRARY Figure 4.14 also revealed that there is a reduction of 41.1% in the average number of pedestrians recorded between 5pm and 6pm and 6pm and 7pm. Figure 4.14 also showed that between 7am and 8am and 6pm and 7pm of the study, there is 51.9% increase in pedestrian flow in the study area. The percentage increase in pedestrian flow from 7am to 7pm in the study area can be attributed to the variations in the hourly flow pattern of pedestrian across zones as shown in table 4.4. 101 UNIVERSITY OF IBADAN LIBRARY Table 4.4: Variations in Pedestrian Hourly Flow across Zones Source of Sum of Square Degree of Mean Square F-Ratio F-Ratio Variations Freedom at 5% Between 3733689246.578 16 233355577.911 Groups 56.76 1.52 Within 767713908.417 187 4105421.970 Groups Total 4501403154.995 203 Source: Field Survey, 2009. 102 UNIVERSITY OF IBADAN LIBRARY Using analysis of variance, the result in table 4.4 shows that the calculated F-Ratio ( ) is greater that critical value of F-Ratio( ). The analysis in table 4.4 shows that at 5% level of significant, there is a significant variation in the hourly flow of pedestrians across the zone. From the study, the volume of vehicular traffic and pedestrian trips generated daily in the study area have made vehicular congestion and parking problem a regular problem and the consequence is indiscriminate parking on roadside or walkways meant for pedestrians as shown in plates 4.1, 4.2 and 4.3. With little attention been paid to pedestrian needs, such as bus stops, bus shelters, pedestrian crosswalks and sidewalks in the study area and entire Ikeja LGA; vehicular and pedestrian conflict occurred frequently and pedestrians are the most affected. 4.5 SUMMARY This chapter examines the socioeconomic activities and physical characteristics of Ikeja and the subunits that constitute the study area. The study shows that there are various socio-economic activities in Ikeja, and these activities are vibrant due to availability of different land uses. As the capital city of Lagos, and the headquarter of one of the twenty local governments in Lagos, Ikeja is not only an administrative and political capital but also serves as one of the commercial nerve centres of Lagos state due to concentration of industries, shopping malls, hotels and trading activities. The chapter also examines transport activities, pedestrian movements and flow pattern along the streets in the study area. These movements have shown that landuse and economic activities in the study area are pull factors and thus have the capability in generating both vehicular and pedestrian trips and flow at different time of the day. In Ikeja, pedestrian movements are clearly visible. However, pedestrian facilities such as walkways or sidewalks and sheds are lacking. The chapter showed that within the congested road ways, pedestrians and motor vehicle drivers do claim right of way. This occurs simply because pedestrian walkways are inadequate and where they are available, are few and in form of half-covered drainage, which are rarely used. At times; they are not used because they are occupied by parked vehicles, traders, 103 UNIVERSITY OF IBADAN LIBRARY commercial motorcycles and tricycles. Pedestrians thus, jaywalked. The consequence of which may result into pedestrian-vehicular conflict and road traffic accidents which may also result into either disability or loss of life on the part of pedestrians. The chapter also examines the flow pattern of pedestrians along road networks in the subunits. There was a spatio-temporal variation in pedestrian flow across the zones understudy. The flow patterns along the streets start to increase as early as 7:00am in the morning and tend to decrease around 7:00pm in the night. Between 7:00am and 7:00pm, the total average flow of pedestrian along the road networks in the study area is 56,663 pedestrians and the pattern of flow varies significantly ( ) across zones. The variations of pedestrian flow observed across the zones can be attributed to the variations in landuse activities within the zones. Availability of different landuse and places of economic activities in Ikeja explain the volume of pedestrian traffic generated and the flow pattern observed along the road networks. The volume and flow pattern of pedestrians are for different purposes and as well for tolerable walking distances to either bus terminals or places of economic activities. In line with the aim and objectives of the study, pedestrian walking distances and volume trips generated to various activities were discussed in the chapter that follows. 104 UNIVERSITY OF IBADAN LIBRARY CHAPTER FIVE PEDESTRIAN WALKING DISTANCES AND TRIPS GENERATED IN IKEJA 5.1 INTRODUCTION Spatial imbalance as observed by Ullman (1956) in geographical regions produces spatial interaction in terms of movements. Pedestrian movements in the study area can be attributed to the spatial imbalance in terms location of facilities and landuse activities. People thus move from one location to another in order to access available facilities and landuse activities. The movement of people along road networks as pedestrian and the observed flow patterns in the study area is based on different purposes and for varying walking distances. This chapter therefore examines household heads and on-street persons‟ pedestrian trips frequency by gender, trip purposes and walking distances to bus stations or public transport pick-up points and places of economic activities. Furthermore, the chapter assesses maximum walkable distances of household heads and on-street persons to bus stations and landuse activities in the study area. The hypotheses which state that: (i) The maximum distance people are ready to walk to bus stations, various functions and services do not vary; and (ii) the number of pedestrian trips made by household heads‟ and on-street persons‟ in urban centres which is assumed to be the function of their trip types, number of economic activities engaged in and level of accessibility were also tested 5.2 INCIDENCE OF PEDESTRIAN TRIPS Examining the frequency of household heads and on-street persons walk trips in the study area, respondents were asked if they do walk frequently as pedestrians. The results of the survey showed that out of 978 household heads, a high proportion (60.7%) of the respondents (see Figure 5.1) do walk frequently as pedestrians in and around Ikeja, and 39.3% of the respondents do not walk frequently as pedestrians. 105 UNIVERSITY OF IBADAN LIBRARY This result does not necessarily mean that household heads in Ikeja are mostly pedestrian, but shows how frequently household heads walk. 106 UNIVERSITY OF IBADAN LIBRARY Household Heads and On-Street Persons That Do Walk Frequently and Do Not Walk Frequently In and Around Ikeja 90 80 70 60 50 40 30 20 10 0 Do Walk Do Not Walk Household 60.7 39.3 On-Street Persons 89.8 10.2 Figure 5.1: Household Heads and On-Street persons‟ Walk Trip Frequency in Ikeja. Source: Field Survey, 2009 107 UN Percentage Response I VERSITY OF IBADAN LIBRARY Table 5.1: Male and Female Household Heads and On-Street Persons that Walk and Do Not Walk Frequently in the Study Area. Variable Do Walk Frequently Do Not Walk Frequently Male Female Male Female Household 270 (45.5) 324 (54.5) 336 (87.5) 48 (12.5) Heads On-Street Persons 72 (39.3) 112 (60.9) 12 (57.1) 09 (42.9) Source: Field Survey, 2009. 108 UNIVERSITY OF IBADAN LIBRARY Table 5.1 shows that out of 594 household heads that walk frequently as pedestrian, 45.5% of the respondents are male and 54.5% are female. Of the 384 household heads that do not walk as pedestrians, 87.5% respondents are male and 12.5% are female. The results of the response of (205) on–street persons sampled as shown in figure 5.1 and table 5.1, about (189) respondents representing (89.8%) on-street persons do walk frequently as pedestrians in and around Ikeja while (21) respondents representing (10.2%) on-street persons do not walk as pedestrians. Table 5.1 further shows that, out of (189) on-street persons that walk frequently as pedestrian, (39.1%) of the respondents are male and 60.9% of the respondents are female. Out of (21) on-street persons that do not walk frequently as pedestrians in and around Ikeja, (57.1%) are male respondents and (42.9%) are female. The results of the respondents (household heads and on-street persons) sampled (see table 5.1) showed that larger percentage of the household heads and on-street persons do walk frequently as pedestrians. 109 UNIVERSITY OF IBADAN LIBRARY Male and Female Respondents That Do Walk and Do Not Walk Frequently In and Around the Study Area 90 80 70 60 50 40 30 20 10 0 Do Walk Do Not Walk Male 49.6 50.4 Female 88.4 11.8 Figure 5.2: Male and Female Respondents that do walk and do not walk frequently in Ikeja. Source: Field Survey 2009 110 UN Percentage Response IVERSITY OF IBADAN LIBRARY The findings in figure 5.2 further revealed that high proportion of female respondents (household heads and on-street persons) representing (88.4%) do walk frequently as pedestrians compare with their male respondents with (49.6 %) response rate. The difference in the response rate of male and female respondents sampled on „how frequent they walk as pedestrian‟ showed that women sampled have propensity to walk than men and the difference may be attributed to the difference in their travel needs. Therefore, the demand for transportation mode such as airplanes, trains, cars, buses, tricycles, bicycles as well as walking varies and for different purposes. Thus, the purpose, for which household heads and on-street persons‟ travel as pedestrian is presented in the section that follows. 5.3 PEDESTRIAN TRIP PURPOSES One key element of pedestrian trips is the creation of mobility by providing accessibility to different important activities such as work, education, services, social functions, sport and as well as creating enhanced work-related mobility to public transportation (SOU,2001; Weststrand, 2003; TRB, 2006; Raji , 2010). Trip purposes in urban areas can be achieved through different means of transport including walking. Responding to trip purposes, 94.6% of household heads and on- street persons sampled walk trips (pedestrian trips) start from home between the hours of (5:30am-6:00am) in the morning and 86.7% of the trips end at home between the hours (6:00pm-8:00pm) on daily basis. Pedestrian trip purposes embark upon by the household heads and on-street persons involve obligatory trips and discretional trips. Obligatory trips (regular trips) include activities such as journey to work, businesses and schooling. Discretional trips (voluntary trips) include activities such as recreation, religious function, shopping, social functions, visiting friends and exercising. Table 5.2 presents household heads and on-street persons‟ pedestrian trip purposes in the study area. 111 UNIVERSITY OF IBADAN LIBRARY Table 5.2: Pedestrian Trip Purposes in Ikeja. Purpose of Total Walk Trips Household Head Walk On- Street Persons Walk Trips Trips Walk Trips Frequency % Frequency % Frequency % Work 1162 30.4 985 30.6 177 29.6 Recreation 257 6.7 251 7.8 06 1.0 Religious 784 20.5 619 19.2 165 27.6 Function Business 353 9.2 259 8.0 94 15.7 Schooling 284 7.4 246 7.6 38 6.4 Shopping 666 17.4 637 19.8 29 4.9 Social 128 3.3 88 2.7 40 6.7 Function Visiting 48 1.3 0 0.0 48 8.0 Friends Exercising 139 3.6 139 4.3 0 0.0 Source: Field Survey, 2009 112 UNIVERSITY OF IBADAN LIBRARY Table 5.2 shows that (985) of household heads with (30.6%) response rate and (177) on-street persons with (29.6%) response rate, walk as pedestrians for work purpose. The result shows that work accounted for the highest proportion of household heads and on-street persons‟ trip purposes. Afterwards, 19.8% of household heads walk as pedestrian for shopping purpose while 27.6% on-street persons walk as pedestrian for religion functions. Religion function, with (19.2%) of the respondents is next to shopping in household heads‟ response to purpose of walking in Ikeja. About 15.7% of on-street persons walk as pedestrians in Ikeja in order to engage in business activities. Both household respondents and on-street person‟s purpose of walking as pedestrians in Ikeja also include recreation, visiting friends, and social functions. None of the household heads‟ walk as pedestrian to visit friends and none of the on-street persons walk as pedestrians for the purpose of exercising. Results of the study in figure 5.3 further shows that out of 1,183 respondents sampled, high proportion of the respondents, (30.4%) walk as pedestrians for work purpose. 113 UNIVERSITY OF IBADAN LIBRARY Household Heads and On-Street Persons Pesdestrian Trips 35 30.4 30 25 20.5 20 17.4 15 10 9.2 7.4 6.7 5 3.3 3.6 1.3 0 Purpose of Trips Figure 5.3: Household heads and On-Street Persons Walk Trips in Ikeja. Source: Field Survey, 2009 114 UN Percentage Response IVERSITY OF IBADAN LIBRARY The purpose of walking as pedestrian in order of importance by household heads and on-street-persons in figure 5.3 include religious function with 20.5% respondents, shopping with 17.4% respondents, business with 9.2% respondents, schooling with 7.4% respondents, recreation with 6.7% respondents, exercising with 3.6% respondents, social function with 3.3% respondents and visiting friends with 1.3% respondents. From the results of the study, it was observed that household heads and on-street persons‟ work and school activities span from Monday to Friday, in some cases spill to Saturday. While Monday to Friday work activities last between the hours 07:00am to 4:00pm daily, Saturday work activities last between 08:00am to 12:00pm in the afternoon. Recreation activities of respondents extend from Monday to Sunday but at varying time period. Monday to Friday recreation activities last between 6:00 pm to 9:00pm, Saturday recreation activities last between 12:00 in the afternoon to 2:00pm and Sunday recreation activities last between 1:00pm to 2:00pm. Monday to Friday recreation activities according to the respondents are made to avoid being stocked in traffic congestion. Religious activities also spans from Monday to Sunday. Monday to Friday activities, last between 4:00pm to 6:00pm except Tuesday and Wednesday that respondents indicated that their church programme do spill to 8:00pm.Saturday activities start from 4:00pm and end by 10pm and Sunday activities last between 10:00am to 4:00pm. Business activities of household heads and on-street persons extend from Monday to Sunday and their pedestrian trip has no time bound. Shopping activities is daily and spans from Friday to Sunday with concentration on weekend (Saturdays and Sundays).While week days shopping activities last between 4:00pm to 6:00pm, weekend shopping activities last between 9:00am to 2:00pm. Social activities of household heads and on-street persons span for Thursday, Friday, Saturday and Sunday. This activity last between 8:00am to 2:00pm on Thursday and 115 UNIVERSITY OF IBADAN LIBRARY 10:00am to 6:00pm on Saturday and Sunday. Friend visit are on weekends (Saturday and Sunday). On walk trip as exercise, respondents were of the opinion that they do embark on walk trip as pedestrian during weekends (Saturday and Sunday) and this activity last between 6:00 am to 9:00 am. All activities discussed involve households and on-street persons‟ movement from different origins to different destinations and these movements demand household heads and on-street persons covering varying distances. Section 5.4 examines walking distances covered by household heads and on-street persons‟ to bus stations or public transport pick up points, land use and places of economic activities as pedestrians. 5.4 PEDESTRIAN WALKING DISTANCES Walking distance concept has its origin in the neighbourhood design concept. It is important in determining the level of accessibility of commuters and pedestrians to transport facilities. Public transport services are expected to be provided within walking distances. Walking distance can be measured in units of time and also in spatial terms. For example, 5 minutes walking distance or 100m walking distance. Interest in walking distance stems from the fact that public transport is more attractive with a shorter average walking distance. The shorter the walking distance, the more attractive the public transport. Walking distance has been employed since mid-sixties as a criterion required in the planning of facilities and landuse activities in new urban sub-divisions. Walking distance to and from home; to the bus stops and other public transport pick up points, landuse and places of economic activities can be covered either at the origin of a trip , at transit or at the destination of the trip. Walking distance therefore, is the distance which people especially those without a car, should be expected to walk to public transport stops as well as to locations of local facilities. For instance, in the UK, fifteen minutes is assumed to be the minimum average walking distance for persons who do not readily have the use of a private car, and 5 minutes is the expected average distance for car owners to walk to local facilities or public transport pickup points without using a car (Adeniji, 1988; Okoko, 116 UNIVERSITY OF IBADAN LIBRARY 2006).The results of the study on walking distances of households and on-street persons‟ to bus stations or public transport pick-up points, landuse and places of economic activities and pedestrian maximum walking distances is discussed in sections 5.4.1 and 5.4..2 and 5.4..3 respectively. 5.4.1 Walking Distances to Bus Stations In the study, individual household heads and on-street persons in the study area were asked on the distance they walk to bus stations or public transport pick-up points. Individual response of 978 household heads and 205 on-street persons‟ sampled are presented in appendix IV. However; table 5.3 presents the mean walking distance of household head and on-street person based on seventeen subunits in which the study area was delineated. 117 UNIVERSITY OF IBADAN LIBRARY Table 5.3: Average Walking Distances of Household heads and On-Street Persons to Bus Stations in Ikeja. Zone Name Household On-Street Aggregate Codes head Mean Person Mean Mean Walking Walking Walking Distances (m) Distances (m) Distances (m) 1 Otigba Area 206 269 238 2 Awosika Area 195 263 229 3 Obanta Area 203 312 258 4 Kudeti Area 183 258 221 5 Akeem Balogun 196 264 230 Area 6 Ajanaku Area 233 293 263 7 Governor Area 158 333 246 8 Kadiri Area 211 335 273 9 Olanrewaju 209 300 255 Area 10 Mobolaji 260 283 272 Johnson Area 11 Kasumu Aleshinloye 205 310 258 Area 12 Morrison Area 165 327 246 13 Allen Area 178 278 228 14 Unity Area 143 200 172 15 Alabi Area 163 321 242 16 Community 159 323 241 Area 17 Acme Area 246 293 270 Mean Trip Length 195 292 244 Range 117 135 98 Standard Deviation 31.37 33.78 23.79 Source: Field Survey, 2009 118 UNIVERSITY OF IBADAN LIBRARY The distances household heads and on-street persons walk to bus stations or public transport pick-up points varies in Ikeja. In column three of table 5.3 household head in Mobolaji Johnson Area (10) walks about 260metres to bus stations. The result of 260 metres walking distance by household head in Mobolaji Johnson Area (10) shows that household head in Mobolaji Johnson Area (10) walks more distances than any household heads in Ikeja. Household head walking distance to bus station in Mobolaji Johnson (10) is similar to that of on-street person in Kadiri Area (8) where on-street person mean walking distance to bus station is 335metres. The result of walking distance of on-street person in Kadiri (8) shows that on-street person in Kadiri Area (8) walk more distance to bus stations than any on-street persons and household heads in Ikeja. Furthermore, the shortest walking distance to bus stations in Ikeja was made by household head in Unity Area (14). Coincidentally, the shortest walking distance made by on-street person in Ikeja was made by on-street person in Unity Area (14).Unlike, Mobolaji Johnson Area (10) and Kadiri Area (8) where bus stations are located far away from respondents (household heads and on-street persons), in Unity Area (14), household head and on-street person walk little distance to bus stations because of the concentration of public transport pick-up points in and around the area. In Unity Area (14), it is significant to note that household heads find it easier to board different types of vehicles by stepping out of their various homes. This is because many residential areas in Unity Area (14) are surrounded by ever busy streets or roads. Column five of table 5.3, examines mean walking distances of respondents (both household heads and on-street persons) and this is presented in figure 5.4. From figure 5.4, respondents in Unity Area (14) walk less distance (172metres) to bus stations while respondents in Kafi Area (8) walk more distance (273 metres) to bus stations in Ikeja. 119 UNIVERSITY OF IBADAN LIBRARY Mean Walking Distances to Bus Stations 300 273 272 270 263 258 255 258 250 246 246 242 238 241 229 230 228 221 200 172 150 100 50 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Zones Figure 5.4: Household Heads and On-street Persons Mean Walking Distances to Bus Stations in Ikeja. Source: Field Survey 2009 120 UNI Distances (m) VERSITY OF IBADAN LIBRARY The results showed that respondents in Unity Area (14) walk less distance to bus stations than respondents in Kadiri Area (8). Nonetheless, in row nineteen of table 5.3, household heads in Ikeja walk fewer distances with mean trip length of 195metres to bus stations than on-street persons who walk more distances to bus stations with mean trip length of 292 metres. Section 5.4.2 discusses household heads and on-street persons walking distances to landuse and places of economic activities in Ikeja. 5.4.2 Walking Distances to Landuse Activities. Apart from assessing household heads and on-street persons walking distances to bus stations in Ikeja, household heads and on-street persons walking distances to landuse and places of economic activities was also examined. In terms of distance cover, the study revealed that household heads and on-street persons walking distances to landuse and places of economic activities in Ikeja differs from walking distances of household heads and on-street persons to bus stations. In the course of the study, it was equally observed that household heads and on-street persons walking distances to landuse and places of economic activities‟ vary among respondents and as well across zones in which the study area was divided. Table 5.4 shows that household heads in Otigba Area (1), Ajanaku Area (6), Mobolaji Johnson Area (10), Alabi Area (15) and Community Area (16) walk a distance of 1.52kilometre to landuse and places of economic activities in Ikeja. This means that there is equidistance in household heads walking distance to landuse and places of economic activities in Ikeja. Similarly, in Kudeti Area (4), Kadiri Area (8) and Acme Area (17) the mean walking distances of household heads to landuse and places of economic activities is 1.67kilometre. Furthermore, in Akeem Balogun Area (5) and Olanrewaju Area (9), household heads walk a distance of 1.44kilometre to landuse and places of economic activities. The similarities in the distance cover by foot to landuse and places of economic activities in (Otigba Area, Ajanaku Area, Mobolaji Johnson, Alabi Area, and Community Area), (Kudeti Area, Kadiri Area and Acme Area) and (Akeem Balogun Area and Olanrewaju Area) by household heads may have been connected with widespread of 121 UNIVERSITY OF IBADAN LIBRARY landuse and places of economic activities that require varying distances. Walking distances of household heads to landuse and places of economic activities in Awosika Area (2), Obanta Area (3), Governor Area (7), Kasumu Aleshinloye Area (7), Morrison Area (12), Allen Area (13) and Unity Area (14), are 1.34kilometre, 1.49kilometre, 1.55kilometre, 1.40kilometre , 1.68kilometre , 1.63kilometre and 1.41kilometre respectively. 122 UNIVERSITY OF IBADAN LIBRARY Table 5.4: Average Walking Distances of Household heads and On-Street Persons to Landuse Activities in Ikeja. Zone Classified Name Household Head On-Street Aggregate Mean Codes Person Walking (km) (km) Distances (km) 1 Otigba Area 1.52 2.31 1.92 2 Awosika Area 1.34 2.48 1.91 3 Obanta Area 1.49 2.45 1.97 4 Kudeti Area 1.67 2.47 2.07 5 Akeem Balogun 1.44 2.38 1.91 Area 6 Ajanaku Area 1.52 2.53 2.03 7 Governor Area 1.55 2.09 1.82 8 Kadiri Area 1.67 2.78 2.23 9 Olanrewaju 1.44 2.49 1.97 Area 10 Mobolaji 1.52 2.48 2.00 Johnson Area 11 Kasumu 1.40 2.79 2.10 Aleshinloye Area 12 Morrison Area 1.68 2.18 1.93 13 Allen Area 1.63 2.53 2.08 14 Unity Area 1.41 2.38 1.90 15 Alabi Area 1.52 2.75 2.14 16 Community 1.52 2.50 2.01 Area 17 Acme Area 1.67 2.38 2.03 Mean Trip Length 1.53 2.47 2.00 Range 0.34 0.70 0.33 Standard Deviation 3.77 5.76 0.1 Source: Field Survey, 2009 123 UNIVERSITY OF IBADAN LIBRARY Regarding walking distances of on-street persons to landuse and places of economic activities in Ikeja, table 5.4 shows that walking distance of 2.48kilometre by on-street person to landuse and places of economic activities in Awosika Area and Mobolaji Johnson Area is equal. Similarly, on-street persons in Akeem Balogun Area (5), Kadiri Area (8) and Acme Area (17) walking distance to landuse and places of economic activities is 2.38km. In other areas such as Otigba Area (1), Obanta Area (3), Kudeti Area (4), Ajanaku Area (6), Governor Area (7), Olanrewaju Area (9), Kasumu Aleshinloye Area (11), Morrison Area (12), Allen Area (13), Unity Area (14). Alabi Area (15) and Community Area (16), the mean walking distances of on-street persons to landuse and places of economic activities are 2.31km, 2.45km, 2.47km , 2.53km, 2.09km , 2.49km, 2.79km , 2.18km, 2.53km , 2.78km , 2.75km and 2.50km respectively. The results in table 5.4 further shows that the lowest walking distance of 1.34km to landuse and places of economic activities was made by household heads in Awosika Area and the highest walking distance (1.68km) was made by household heads in Morrison Area. In the case of household heads in Awosika Area, the result obtained may have been connected with the location of Awosika Area in Ikeja. Awosika Area is located where landuse and places of economic activities are of close proximity to respondents‟ residences. Unlike Awosika Area, Morrison Area is predominantly residential and this suggests that household heads will walk longer distances to landuse and places of economic activities in Ikeja. Regarding on-street persons, table 5.4 equally shows that the lowest mean walking distance made by on-street person to landuse and places of economic activities was in Governor Area and the highest walking distance was made by on-street persons in Kasumu Aleshinloye Area with trip lengths of 2.09km and 2.79km respectively. In the case of on-street persons in Osin Area where the mean walking distance to landuse and places economic activities of on-street person is at its lowest, 38% of landuse activities are residential; 16% is commercial, 18% is religion, 2% is financial and 26% is institutional (see table 4.2). Concentration of residential activities with moderate commercial and little financial activities may have also been responsible for the observed walking distance made by on-street persons to landuse and places of economic activities in Ikeja. This means that most on-street persons‟ in Governor 124 UNIVERSITY OF IBADAN LIBRARY Area sometimes result into the use of public transport to access landuse activities and places of economic activities in Ikeja. In case of Kasumu Aleshinloye Area where walking distance of on-street persons to landuse and places of economic activities is at its highest, 31% of landuse activities are residential, 30% is commercial, 16% is religion, 6% is financial, 15% is institutional and 2% is industrial (see table 4.2). Though residential activities dominate landuse activities in Kasumu Aleshinloye Area, availability of different landuse activities at varying proportion attract people and this may have been responsible for observed walking distance in the area. However, in the last row of table 5.4 the mean walking distances (2.47km) of on- street persons to landuse and places of economic activities is higher than the mean walking distances (1.53km) made by household heads to landuse and places of economic activities. Like the results of mean walking distances of household heads and on-street persons to bus stations in Ikeja, the results of mean walking distance of respondents to landuse activities indicates that on-street persons‟ walk more distances to landuse and places of economic activities than household heads in Ikeja. Figure 5.5 on the other hand shows respondents (both household heads and on-street persons) mean walking distances to landuse and places of economic activities in Ikeja. The results from figure 5.5 shows that the lowest walking distance (1.91km) to landuse and places of economic activities in the study area was made by respondents in Awosika Area and Akeem Balogun Area. Furthermore, the highest mean walking distance to landuse and places of economic activities (2.23km) in the study area was made by respondents in Kadiri Area. Invariably, it can be concluded that respondents in Kadiri Area walk more distances to landuse and places of economic activities in Ikeja. 125 UNIVERSITY OF IBADAN LIBRARY 2.5 Mean Walking Distances to Landuse Activities 2.23 2.14 2.1 2.07 2.08 2.03 2.03 2 2.01 2 1.97 1.97 1.92 1.91 1.91 1.93 1.9 1.82 1.5 1 0.5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Zones Figure 5.5: Household Heads and On-Street Persons Mean Walking Distances to Landuse Activities in Ikeja Source: Field Survey, 2009. 126 UN Distance (km) IVERSITY OF IBADAN LIBRARY In the literature, Cresswell (1973) proposed average walking distances between 300m and 900m from home to public transport pick-up points or bus stations on housing types in table 5.5. Cresswell (1976) is of the opinion that these estimates are believed to be the basis for planning in housing. He further stated that, in Abuja the Federal Capital Territory of Nigeria, without specific population or defined land area (during the time of establishment), a walking distance of 400 metres or 5 minutes‟ walk was proposed. Based on Cresswell (1973) observations of 300m to 900m average walking distance from home to public transport pick-up points, the results of the study showed that the mean walking distance of the people in the study area between 143m and 335m.The result thus, falls short of Cresswell propositions. 127 UNIVERSITY OF IBADAN LIBRARY Table 5.5: Recommended Walking Distances to Public Transport Stops and Local Facilities Types of Building Walking Distances (Metres) Multi-Family Housing ≥ 5 Storey 300 3 - 4 Storey 400 2 Storey 500 Single Family Housing Terraces Houses 500 Other Types 600 – 900 Source: Cresswell, R. W. (1976) Passenger Transport and the Environment. 128 UNIVERSITY OF IBADAN LIBRARY From the results of the study, it can be suggested that with on-going re-construction in Lagos, an average walking distance between 300m to 900m proposed by (Ling, 1969; Pass and Fasta, 1973; Cresswell, 1976) to bus stations and public transport pick-up points may not be appropriate So, transport and urban planners need a comprehensive study of people‟s behaviour or incorporate a walking distance between 143m to 335m obtained in the course of the study into the scheme. Fruin (1971) on the other hand, propounded a maximum walking distance of 3.2km in the United States of America to landuse activities such as school, market, work, stadia and so on. In this study, it was observed that the trip length household heads and on-street persons will walk to landuse and places of economic activities are on the average of 1.51km and 2.47km respectively. Furthermore, on the average, the trip length of respondents (both household heads and on-street persons) to landuse and places of economic activities is 2km.The result of the study is in variance with the proposition of 3.2km propounded by Fruin (1971) in the United States of America. Fruin (1971) proposition may not be appropriate for planning in Ikeja and other cities in Nigeria that have similar characteristics of Ikeja. The variations observed in the mean walking distances of household heads and on- street persons in each zone of the study area; signify the limit (maximum walking distances) individual household head and on-street person will walk to bus stations and landuse activities. Establishing the significance of the variations noticed in walking distances of the respondents necessitated the explanation of the research hypothesis of the study that states that: “the maximum distances people are ready to walk to bus stations, landuse activities and various functions and services do not vary across zones in the study area”. Section 5.4.3 of the study examines the significance of maximum walkable distances of household heads and on-street persons in Ikeja. 5.4.3 Pedestrians Maximum Walkable Distances In this study, the distances people walk to bus stations, landuse and places of economic activities is presented in appendix IV and appendix V respectively. However, the results of mean walking distances for household heads and on-street persons in table 5.3 and table 5.4 show the limit respondents can walk to bus stations, 129 UNIVERSITY OF IBADAN LIBRARY landuse and other places of economic activities across zones in which the study area was divided. Examining the significance variations in the mean walking distances of household heads and on-street persons sampled, test for the significance of mean walkable distances of household heads and on-street persons was carried out using Analysis of Variance (ANOVA). In generating table 5.6, tables 5.3 and 5.4 were used. At disaggregate level row one to row ten of table 5.6 examines the variations in mean walkable distances of individual household head and on-street person‟s to bus stations and landuse activities respectively and row eleven to row fifteen of table 5.6 assesses the aggregate level of variations in the mean walkable distances of (both household heads and on-street persons) to bus stations and landuse activities in Ikeja. Results in row one to row five of table 5.6 shows that the calculated F-Ratio is . The critical value at 5% level of significance is . From the analysis, the value of calculated F-ratio is greater than the critical value, , and it can be inferred that the maximum distances household heads and on-street persons are ready to walk to bus stations do vary across zones in the study area. 130 UNIVERSITY OF IBADAN LIBRARY Table 5.6: Variations in Pedestrian Walking Distance to Bus Stations and Land Use Activities in Ikeja. Variation in Pedestrian Walking Distance to Bus Stations Sources of Sum of Square Degree of Variance of Variance Freedom Mean Square F- Ratio F-Ratio at 5% Between 79976.500 1 79976.500 Groups 70.831 4.150 Within Groups 36131.529 32 1129.110 Total 116108.03 33 Variation in Pedestrian Walking Distance to Land Use Activities Sources of Sum of Square Degree of Variance of Variance Freedom Mean Square F- Ratio F-Ratio at 5% Between 2.298 1 2.298 Groups Within Groups 4.516 32 0.141 16.288 4.150 Total 116108.03 33 Variation in Pedestrian Walking Distance to Bus Stations and Land Use Activities Sources of Sum of Square Degree of Variance of Variance Freedom Mean Square F- Ratio F-Ratio at 5% Between 15099558 1 15099557.765 Groups Within Groups 3562450.1 32 111326.566 135.633 4.150 Total 1866.2008 33 Difference in Pedestrian Walking Distance to Bus Stations and Land Use Activities Household t-test t-test at 5% Heads X1 = 0.244km δ1 = 0.02 On-Street 71.01 2.02 Persons X2 = 2.000km δ2 = 0.01 Source: Field Survey Computation, 2009. 131 UNIVERSITY OF IBADAN LIBRARY Similarly, results in row six to row ten of table 5.6 shows that the calculated F-Ratio is . The critical value at 5% level of significance is . From the analysis, the value of calculated F-ratio is greater than the critical value, . From the analysis, it can also be deduced that the maximum distances household heads and on-street persons are ready to walk to landuse and place of economic activities do vary across zones in the study area. The variations in the mean walkable distances of household heads and on-street persons to bus stations and landuse activities in the analyses (row one to row ten in table 5.6) can be linked to the results obtained in table 5.3 and table 5.4. Table 5.3 and table 5.4 showed variations in the mean walking distances of household heads and on- street persons to bus stations and landuse activities across zones in the study area. Furthermore, table 5.3 and table 5.4 equally showed that the average walking distance or trip length of household heads to bus stations and landuse activities is less than average walking distance or trip length of on-street persons to bus stations and landuse activities in the study area. The difference in walking distances of the respondents to bus stations and landuse activities in the study area is a reflection of behavioural pattern of individual household head and on-street person in the choice of comfortable walking distance. The results in row one to row ten of table 5.6 may also be connected with the location of individual household head and on-street person from bus stations and landuse activities that requires varying distances. Therefore, differences in walkable distances of household heads and on-street persons‟ to bus stations and landuse activities and the significant variations observed in mean walking distances of household heads and on-street persons to bus stations and landuse activities using ANOVA, negate the first hypothesis of the study that states that: the maximum distances people are ready to walk to bus stations, landuse activities and various functions and services do not vary across zones in the study area. Furthermore, results in row eleven to row fifteen of table 5.6 on the other hand, revealed aggregate level of respondents (both household heads and on-street persons) 132 UNIVERSITY OF IBADAN LIBRARY walkable limit to bus stations and landuse activities. The result showed that calculated F-Ratio is , the critical value at 5% level of significance is . From the analysis, the value of calculated F-ratio is greater than the critical value . From the analysis it means that the maximum distances household heads and on-street persons are ready to walk to bus stations, landuse activities and various functions and services differ across zones in the study area. The t-test conducted on the mean walking distance of respondents {household heads to bus stations and landuse activities ( ̅=0.244km, δ=0.02) and that of on-street persons ( ̅=2.000km, δ=0.10)} is significant at (t=71.01, p=≤0.05).The conclusion of significant variations and difference in the mean walking distances of both households and on-street persons‟ to bus stations and landuse activities in the study area further disprove the research hypothesis at aggregate level. The results obtained from the study has shown that at individual level as well as group level in Ikeja, the maximum distances people are ready to walk to bus stations, landuse activities and various functions and services do vary. In various economic and landuse activities embark upon by household heads and on- street persons, there are number of times they engage in these activities weekly, monthly and even yearly as pedestrian. In the course of this research, household heads and on-street person‟s daily activities were recorded and the number of times these activities were generated with respect to their pedestrian trip characteristics or purpose, socio-economic activities and level of accessibility. Section 5.5 of the thesis therefore, discusses the determinants of the number of pedestrian trips generated by respondents in Ikeja. 5.5 DETERMINANTS OF PEDESTRIAN TRIPS GENERATED Travel frequency is generally employed as one of the criteria in the analysis of travel activity patterns. In line with the second objective of the thesis and to achieve hypothesis II of the study that states that: „„the number of pedestrian trips made by households‟ and on-street persons‟ in urban centres is a function of their trip types, number of economic activities engaged in and level of accessibility.‟‟ 133 UNIVERSITY OF IBADAN LIBRARY Household heads and on-street persons‟ number of pedestrian trips generated (both household heads and on-street persons‟ walk trip frequencies to various activities) was set against number of variables in the field study. Analysing household heads and on-street persons‟ number of pedestrian trips generated, multiple regression analysis was employed. Multiple regression was employed to express the relationship between household heads‟ and on-street persons‟ number of pedestrian trips generated against explanatory variables such as trip types (work, shopping, business, social function, religious function , visiting friend, recreation, schooling and exercising); number of economic activities (industries, restaurants, shopping malls and fast food points) respondents engaged in; and pedestrian level of accessibility in each of the seventeen subunits in which the study area was divided. The explanatory variables are presented in table 5.7. 134 UNIVERSITY OF IBADAN LIBRARY Table 5.7.: Description of Explanatory Variables Used For the Number of Pedestrian Trips Generated in Ikeja. Variables Description Household and On-street (HO) Number of times individual household and Y persons Volume of Pedestrian on-street respondents walk to each Trips = activities Trip Types or Purposes = X1 HO_WORK = WORK Work; 1 if going to work, 0 otherwise X2 HO-SPPIN = SHOP Shopping; 1 if going for shopping, 0 otherwise X3 HO-BUSNES = BNES To take transit; 1 if going to take transit, 0 otherwise X4 HO_SCIAL = SOFU Social; 1 if going for social functions, 0 otherwise X5 HO_RLIGON = RELI Religion; 1 if going for religion functions, 0 otherwise X6 HO_VISITFR = VIST Visiting; 1 if going for visit , 0 otherwise X7 HO_RCRTN = RECR Recreation; 1 if going for recreation, 0 otherwise X8 HO_SCHLNG= SCHL Schooling; 1 if going to school, 0 otherwise X9 HO_EXERCISE = EXER Exercise; 1 if going for exercise, 0 otherwise Number of Economic Activities = X10 NINDU Number of times a respondent engaged in industrial activities in the study area X11 NHRES Number of times a respondent visit hotels or restaurants in the study area. X12 NFINI Number of times a respondent engaged in financial and related institutions activities in the study area. X13 NSHPM Number of times a respondent visit shopping malls or retail outlet in the study area. X14 NFAST Number of times a respondent visit fast food points in the study area. Level of Accessibility = X15 Zone 1 to Zone 17 1 if there is a direct link between nodes in the street graph of respective zones, 0 otherwise N 1183 Source: Field Survey, 2009. Note: Nodes represent junctions, round about in the study area; Link represents streets or road network in the study area. Where, Households and On-Street Persons Volume Pedestrian Trips = Trip Types or Purposes = , Number of Economic Activities = Level of Accessibility = 135 UNIVERSITY OF IBADAN LIBRARY The choice of these variables is not limited to their measurement capability in urban decision analysis but also based on the response of 1183 respondents (household heads and on-street persons) to the explanatory variables in the administered questionnaire. The regression equation is thus expressed in equation 5.1 as: 5.1 Where = Household heads‟ and on-street persons‟ number of walk trips. = Household heads‟ and on-street persons‟ trip characteristics = Number of times household heads and on street persons visited economic Activities. = Level of accessibility in each zone. = regression coefficients = random error term Equation 5.1 is simplified into equation 5.2 with the variables expressed as: ( ) ( ) ( ) 5.2 Where = Number pedestrian walk trips Checking for collinearity among the explanatory variables, it was observed that there is a high correlation ( ) (see appendix VI) between explanatory variables (recreation and schooling) and (recreation and number of times respondent visit fast food points). Since the correlation between explanatory variables (schooling and fast food points) is not high( ) , recreation variable was removed from the analysis. The summary of the regression results is presented in table 5.8. In appendix VII, all the explanatory variables are significant at 95% confidence level except schooling, social function, number of times respondents engage in financial institutions and level of accessibility of respondents in the study area, and they are not presented in table 5.8. 136 UNIVERSITY OF IBADAN LIBRARY Table 5.8: Relationship between Number of Pedestrian Trips Generated by Household heads and On-Street Persons‟ in Ikeja. Variables Variables Unstandardized Standardized Standard Acronym Regression Regression Error t-values Coefficients Coefficients(Beta) Work WORK 1.024 0.353 0.101 10.125 Religion RELI 1.351 0.320 0.161 8.415 Business BNES 0.435 0.074 0.165 2.627 Shopping SHOP 1.042 0.258 0.146 7.138 Visiting Friends VIST 4.284 0.076 2.324 1.843 Exercising EXER 2.851 0.082 1.172 2.432 Industries NINDU 0.452 0.061 0.166 2.726 Hotels and NHRES 0.345 0.034 0.313 1.103 Restaurants Shopping Mall NSHPM 2.897 0.047 1.836 1.577 Fast Food NFAST 4.735 0.288 1.241 3.816 Points Constant 0.735 6.512 0.113 Model 2 Adjusted R 0.792 Model F -Ratio 189.623 N 1183 Source: Field Survey Computation, 2009. 137 UNIVERSITY OF IBADAN LIBRARY From the results in table 5.8, the regression equation for number of pedestrians trips generated in Ikeja is presented in equation 53 as: ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) 5.3 The regression equation showed that the most significant explanatory variables that contributed to the number of pedestrian trips generated by 1183 respondents (both household heads and on street persons) in Ikeja is work. Presenting the contributions of the explanatory variables in order of importance include work, religious functions, number of times household heads and on-street persons visit fast food points, shopping, exercising, visiting friends, business, number of times household heads and on-street persons engaged in industrial activities, number of times household heads and on-street persons visit shopping malls, hotels and restaurants in Ikeja. Ayeni (1975; 1979) , Lewis (1981) and Oyesiku (1990) observed that the standardised partial coefficient as presented in column four of table 5.8 guarantees measurement units of the independent variables when interested in relative effect of the explanatory variables. Therefore, the regression equation 5.3 presents in order of importance the independent variables using the standardised partial coefficient in table 5.8. Work is the most significant variable in the number of pedestrian trips generated by household heads and on-street persons while friends' visit is the least in respondents‟ number of pedestrian trips made in Ikeja. The signs of the partial regression coefficient for all the explanatory variables are positive. This indicates an increase in household heads and on-street persons‟ number of pedestrian trips result in a unit increase in the explanatory variables (trip characteristics, economic activities and accessibility). As earlier discussed in this chapter, that „work purpose‟ account for the largest proportion of household heads and on-street persons‟ pedestrian trip purposes in Ikeja, „going to work‟ happens to be the most significant of the explanatory variables that 138 UNIVERSITY OF IBADAN LIBRARY explain household heads and on-street persons number of pedestrian trips generated in the regression analysis. Therefore, the result obtained from the regression analysis, is in line with the works of (Ayeni, 1979; Daniels and Warnes, 1980; Tanimowo, 1983; Ojo, 1990; Zeeger et al 1994; Solanke, 2005 Rahaman, 2007) where trips to work and services constitute a dominant proportion of trips generated by urban residents studied. 5.6 SUMMARY This chapter shows that larger proportion of household heads and on-street persons do walk frequently. This does not necessarily mean that household heads and on-street persons in Ikeja are mostly pedestrians. Out of the proportions of respondents that walk frequently among household heads and on-street persons, the result shows that female respondents walk more frequently than the male respondents. The chapter also discussed household heads and on-street persons‟ pedestrian trip purposes. Nine categories of household heads and on-street persons‟ pedestrian trip purposes were observed in Ikeja. The pedestrian trip purposes include, work, religion function, business, recreation, shopping, schooling, social function, visiting friends and exercising. As presented in table 5.2 and figure 5.3, work accounted for the highest walk trip purpose and the least is visiting friends. Interestingly, religion function is next to work. The tendency of religion function to rank high after work may be peculiar to Nigeria in Sub-Saharan Africa countries where people place value on religion. Furthermore, outright conversion or the use of most commercial buildings and residential buildings in the study area as earlier discussed in chapter four for house fellowship, churches may also be a possibility for such ranking. The chapter shows that walking distances of respondents varies across zones in the study area. At aggregate and disaggregate levels, there are significant variations in the mean walking distances of household heads and on-street persons to bus stations, landuse and places of economic activities. Furthermore, it was observed that household heads walk less distances to bus stations, landuse and places of economic 139 UNIVERSITY OF IBADAN LIBRARY activities in the study area than on-street persons. This situation may be attributed to the fact that household heads take pleasure in the comfort of their vehicles, which signifies that household heads are likely to bring their vehicles as close as possible to activity centres. The implication is that where there are no parking facilities at these centres, people may decide to park along the streets or walkways meant for pedestrians (which is obvious in (plate 4.1, plate 4.2 and plate 4.8) thereby not only creating traffic congestion along the streets but also expose pedestrians to danger. Where there is enforcement regarding on-street parking, this may result to underutilisation of such facilities. The chapter also looked at the explanatory variables that explain the number of pedestrian trips generated by household heads and on-street persons. In the results, work, religious function, and shopping, significantly explain number of pedestrian trips generated by household heads and on-street persons in that order. The level of importance of shopping activities to household heads and on-street persons and their level of patronage may have contributed to the significance of regression coefficient representing the number of times respondents engaged in shopping activities in Ikeja. The results of the regression analysis also showed a similar trend in the percentage response of household heads and on-street persons regarding their walk trip purposes as shown in table 5.2 Generally, movements of pedestrians in Ikeja at different time of the day can be attributed to the generating capability of various land uses which serves as either origins or transits or destinations of household heads and on-street persons‟ trips. The incidence of household heads and on-street persons pedestrian trips; the purpose of pedestrian trips; the maximum distance household heads and on-street persons are prepare to walk in achieving the trip purposes and the factors that determines the number of trips generated have been discussed in this chapter. However, the question that comes to mind is that: (i) what informs household heads and on-street persons‟ decision to walk? (ii) what is their level of safety when generating these trips? Providing explanations to these questions, involve examining the third and the fourth objectives of the thesis, and it requires an in-depth study of people‟s decision to walk and their level of safety on roadways in Ikeja. 140 UNIVERSITY OF IBADAN LIBRARY CHAPTER SIX PEOPLE’S DECISION TO WALK AND PEDESTRIAN LEVEL OF SAFETY IN IKEJA 6.1 INTRODUCTION As discussed in chapter five, the purpose for which people travel, creates the demand for the use of different modes of transportation including walking. The field study also shows that the number of pedestrian trips generated by female respondents‟ is higher than their male counterpart. But the observed difference in the gender trips generated may not be limited to the variations in their travel needs but, can also be attributed to their decision to walk. People decision to walk is influenced by several factors. Examining these factors require the use of logistic regression to model 1183 respondent‟s readiness to walk against predetermined explanatory variables that are described in this chapter. The chapter also analysed the most preferable determinants in the usage of pedestrian facilities in Ikeja using the Analytical Hierarchical Process (AHP) model. The chapter also provides explanation to pedestrian level of safety along 56 road segments in the study area using pedestrian level of service (the threshold of pedestrians on walkways) as surrogate to pedestrian level of safety. Of the 56 road segments sampled, only 6 of the road segments have walkways or sidewalks. The chapter discusses pedestrian level of safety in relation to lateral separation (pedestrian distance from moving or passing vehicles), motor vehicle volume, motor vehicle speed and access of vehicles to adjoining properties along the road segments using multiple regression analysis. Factors influencing decision to walk is discussed in the section that follows. 141 UNIVERSITY OF IBADAN LIBRARY 6.2 FACTORS INFLUENCING DECISION TO WALK There are several factors that influence people‟s decision to walk to various activities such as journey to work, shopping centres, recreation, social functions, religion centres and so on. In this study, the decision of household heads and on-street persons to walk was set on predetermined explanatory variables such as socio-economic characteristics (age, gender, marital status, education, employment, auto ownership, number of vehicle owned, monthly income and work location); preferred nature of pedestrian facilities (road traffic situation, safety, security, cleanliness, spacious); and other factors such as distance, time, season and weather. These explanatory variables or factors are presented in table 6.1. 142 UNIVERSITY OF IBADAN LIBRARY Table 6.1: Description of the Explanatory Variables Used for Household Heads and On-Street Persons‟ on Decision to Walk.in Ikeja. Explanatory Variables Description Decision to Walk Ready to walk Number of households and on-street persons that are: Not ready to walk Ready to walk = 1, Not ready to walk = 0 Socio-economic Data Age Age of Respondents 1 if 35years, 0 if ≤35years Gender Gender of respondents 1 if female, 0 if male Income Respondents monthly income: 1 if ≤ N 7,500:00, 0 if N 7,500:00 Marital Status Single Marital status: 1 if single. Married Marital status: 0 if married. Education Qualification Education Respondents Education: 1 Having Secondary Education or Less, 0 otherwise. Employment Employment status: Government employed 1, Self- employed 0. AUTO Household auto ownership, 1 if having none, 0 otherwise NVEHI Number of Vehicle in household, 1 if less than 2, 0 if ≥ 2. WLCT Work Location: Within Ikeja = 1 , Outside Ikeja = 0 Nature of Pedestrian Facilities (Walkways) RTRFS Traffic Situation: 1 if walkway congested, 0 walkway not congested SAFTY Safety: 1 if walkway is safe, 0 if walkway not safe SCRTY Security: 1 if walkway is not secured, 0 walkway secured CNTNTY Continuity: 1 if walkway is continuous, 0 walkway not continuous CLNESS Cleanliness : 1 if walkway is not clean, 0 if walkway is dirty SPCIUS Spacious: 1 if 2 or more people can walk , 0 otherwise Other Factors DSTCE Distance: 1 if journey length ≥3.2km, 0 journey length < 3.2km TIME Time of the day: 1 if night, 0 if daylight SNSON Season: 1 if rainy season, 0 if dry season WTHER Weather: 1 if cool , 0 if hot Source: Field Survey, 2009. 143 UNIVERSITY OF IBADAN LIBRARY In order to provide explanation to these variables, household heads and on-street persons response was modelled in equation 6.1 such that: 6 .1 The decision to walk ( ) is a binary dependent response variable of ‘ready to walk’ and ‘not ready to walk’, represent socio-economic variables, represent nature of pedestrian facilities (walkways), and represent other factors in table 6.1. In binary dependent variable, most assumptions of Ordinary Least Square (OLS) regression are violated; hence, Ordinary Least Square is not suitable. However, Probit and Logit analyses are appropriate for the data obtained. Logistic regression answers similar questions as discriminant analysis. However, logistics regression is often preferred to discriminant analysis as it is more flexible in its assumptions and types of data that can be analyzed. Logistic regression can handle both categorical and continuous variables, and the predictors do not have to be normally distributed, linearly related, or of equal variance within each group (Tabachnick and Fidell 1996). In Logistic regression, the objective is the same as Ordinary Least Square (OLS). The goal is to model dependent variable in terms of one or more independent variables (Dayton, 1997; Ananth and Kleinbaum, 1997; Adeleke and Adepoju, 2010). Logistic regression is then useful only for dependent variables that are categorical such as alive or dead, male or female, yes or no and so on. Logistic and probit regression models are equally designed to analyse qualitative data reflecting a choice between two or more alternatives (Jones, 2007), and as well capable of transforming binomial data into linearity, which in the case of this study include „ready to walk’ and ‘not ready to walk’. The logistic model thus represents a convenient way of quantifying the relationship between the characteristics of the household heads and on-street persons‟ decision to 144 UNIVERSITY OF IBADAN LIBRARY walk. The dependent variable takes the value of 0 or 1, where 1 represents „ready to walk’ and 0 „not ready to walk’ or otherwise as described in table 6.1. Although the relationship between probit model and logit model is almost indistinguishable, Finney (1952), Greene (2003), Jones (2007), Vincent (2008) were of the opinion that if we are not sure that the data are normally distributed (which may be true for the data of this study), then, they suggest the use of logit model over probit model. Thus, the study made use of logit or logistic model. In this study, the dependent variable, decision to walk ( ) was transformed into dichotomous response variable Y with binary outcomes taking two values such that * , -+ with if the respondent‟s response is ‘ready to walk’ and if response is „not ready to walk’. Based on the above, the probability of the respondents ready to walk which correspond to Y 1 was derived using the following linear probability model depicted in equation 6.2 ( ) 6.2 where ( ) are the explanatory variables described in table 8.1 and ( ) means that respondents are ready to walk. Similarly, , ( )- represent respondents are not ready to walk. If respondent is ready to walk, it is represented in equation 6.3: ( ) 6.3 [ ( )] , - Where Equation 6.3 represent cumulative logistic distribution function, with ( ) ranging from ( ); ( ) range between ( ), and ( ) is non-linearly related to the explanatory variables ( ) and ( ).The implication is that Ordinary Least Square Regression cannot be used to estimate the parameters. Then probability of readiness to walk is given by: 145 UNIVERSITY OF IBADAN LIBRARY ( ) , - Then, ( ) is the probability of „not ready to work‟, and can be written as shown in equation 6.4: ( ) ( ) 6.4 , - The probability that a household head and on-street person is ready to walk to the probability of a household head and on-street person not ready to walk is known as (odds ratio), and it is represented by equation 6.5. , - 6.5 ( ) , - From natural logarithm of equation 6.5, the logit model for the study is given as: [ ] , ( )- ( ) Where: Decisions to walk, Socio-economic characteristics of households and on-street persons, preferred nature of pedestrian facility Other factors, Logit regression coefficients and the constant. The probability for the logit regression model for the study is presented in equation 6.6 as. ( ) 6.6 Based on the results obtained from the study area, table 6.2 summarises descriptively, information obtained from households and on-street persons on decision to walk based on the explanatory variables presented in table 6.1 and table 6.3 summarises logistics regression results on decision to walk by households and on-street persons‟ in the study area in Ikeja. 146 UNIVERSITY OF IBADAN LIBRARY Table 6.2: Summaries of Exploratory Data of Household Heads and On-Street Persons‟ on Decisions to Walk in Ikeja Response Variables N Marginal (%) Response Variables N Marginal Response Variables N Marginal (%) (%) DTW RESPONSE Auto-Ownership Cleanliness Ready to walk 778 65.8 Own Vehicle 986 83.3 Walkway dirty 15 1.3 Not ready to walk 405 34.2 None 197 16.7 Walkway clean 1168 98.7 SOCIO-ECONOMIC Number of Vehicle Spacious CHARACTERISTICS Age < 2 850 71.9 Walkway spacious 1045 88.3 18 – 38 308 26.0 ≥ 2 333 28.1 Walkway not spacious 138 11.7 39 – 59 567 48.0 Monthly Income Continuity ≥ 60 308 26.0 < N7,500 : 00 8 0.7 Walkway continue 1085 91.7 Gender ≥ N7,500 : 00 1175 99.3 Walkway not continue 98 8.3 Female 493 58.3 Work Location OTHER FACTORS Male 690 41.7 Ikeja 956 80.8 Distance Marital Status Outside Ikeja 227 19.1 ≥ 3.2km 342 28.9 Single 254 21.5 PREFERRED < 3.2km 841 71.1 NATURE OF PEDESTRIAN FACILITY Married 929 78.5 Road Situation Time to Walk Education Walkway congested 35 3.0 Night 327 27.6 Having primary educ. 87 7.4 Walkway not congested 1148 97.0 Day 856 72.4 Having secondary educ. 226 19.1 Safety Season to Walk Having post-secondary 870 73.5 Walkway safe 1175 99.3 Rainy 220 18.6 educ. Employment Walkway not safe 8 0.7 Dry 963 81.4 Government employed 771 65.2 Security Weather to Walk Self- employed 412 34.8 Walkway not secured 25 2.1 Cool 1143 96.6 Walkway secured 1158 97.9 Hot 40 3.4 Source: Field Survey, 2009. 147 UNIVERSITY OF IBADAN LIBRARY Table 6.3: Relationship between Household Heads and On-Street Persons‟ Decisions to Walk in Ikeja. 0dd Ratio 95% C.I. for Exp (β) Variables Coeff. (β) Std. Error Wald-Statistics df P-value Exp (β) Lower Upper 1 AGE -0.212 0.172 1.523 1 0.217 0.809 0.578 1.133 1 GEN 0.024 0.126 0.035 1 0.852 1.024 0.800 1.311 1 MAR -0.080 0.152 0.279 1 0.597 0.923 0.686 1.243 1 EDU 0.060 0.161 0.140 1 0.709 1.062 0.775 1.456 1 EMP -0.063 0.131 0.228 1 0.633 0.939 0.726 1.215 1 AUT -0.004 0.176 0.000 1 0.982 0.996 0.705 1.407 1 NVE 0.038 0.144 0.070 1 0.791 1.039 0.783 1.378 1 MIN -1.579 1.094 2.083 1 0.149 0.206 0.024 1.759 1 WCL 0.135 0.157 0.747 1 0.388 1.145 0.842 1.556 1 RTS -1.190 0.363 0.274 1 0.601 0.827 0.406 1.666 1 SAF 0.056 0.741 0.006 1 0.940 1.058 0.248 4.519 1 SCR -0.442 0.478 0.855 1 0.355 0.643 0.252 1.641 1 CTN 0.297 0.220 1.832 1 0.176 1.346 0.875 2.071 1 CLN -0.427 0.593 0.517 1 0.472 0.653 0.204 2.088 1 SPS1 0.120 0.194 0.382 1 0.536 1.128 0.771 1.650 1 DIS -0.098 0.141 0.479 1 0.489 0.907 0.688 1.196 1 TIM -0.023 0.144 0.026 1 0.873 0.977 0.737 1.295 1 SEA - 0.064 0.167 0.146 1 0.702 0.938 0.676 1.302 1 WEA 0.594 0.336 3.128 1 0.077 1.811 0.938 3.496 Constant 2.195 1.592 1.901 1 0.168 8.977 2 2 Source: Field Survey, 2009 Number of Observations = 1183 Log Likelihood = 1506.672 Pseudo - R = 0.009 LR X = 13.677 148 UNIVERSITY OF IBADAN LIBRARY From table 6.3, the logistic model gives: , ( )- + The logit response variables „decision to walk‟ refers to the process of household heads and on-street persons readiness to walk that carries value (1) and readiness not to walk that carries (0).The reference point is readiness to walk, and this was discussed in relation to categorical covariates that carries the value (1) as shown in column one of table 6.3. In logistic model, the interpretation is based on whether the (β) coefficient is larger than zero or not (that is β < 0 or β > 0) and the odd ratio (e ) of (β) logistic regression is based whether (e ) is larger than (1) or not. In testing for the significance of the logistics regression coefficients, Wald‟s Chi- 2 square (X ) was used, such that the null hypothesis ( ) shows that all the logistics regression coefficients except, the constant equal to zero. Equation 6.7 was used to obtain Wald‟s Chi-square. Using equation 6.7, the value of Wald‟s Chi-square is 13.677. ,( ) ( )- 6.7 Where: ,( ) ) At one degree of freedom and 95% confidence level, the table value ( ) was low enough to reject the null hypothesis. From the analysis, 13.677 is greater than 0.00039321 and it can be concluded that, the entire 149 UNIVERSITY OF IBADAN LIBRARY coefficients in the model are not equal zero ( ). This means that not all the factors in the model do have similar influence on respondents‟ decision to walk. The geographical implications of the significance of the logistics regression coefficients and conclusion drawn were described below based on the results in table 6.3. From table 6.3, the results of socio-economic variables of the respondents showed that the estimated odd ratio of household heads and on-street persons‟ readiness to walk are lower for age group above 35years old .This means that household heads and on- street persons within age group above 35 years old have negative effect on likelihood of readiness to walk. It therefore implies that household heads and on-street persons with age group (≤ 35years) are more likely to walk. On gender basis, the result showed that the estimated odd ratio of household heads and on-street persons‟ readiness to walk is higher in female respondents than estimated odd ratio of readiness to walk of male respondents. This shows that sex of respondents have positive effects on readiness to walk. Although, the coefficient of logit regression coefficient is not significant, the odd ratio showed that a female respondent have 1.024 times more likely to walk than male respondents. The results of the logistic regression further affirmed the response of household heads and on- street persons‟ response whether they do walk or not. As shown in figure 5.2, of the male respondents sampled 49.6% do walk while 50.4% do not walk. Regarding female respondents sampled, 88.4% do walk while 11.6% do not walk. Based on marital status, the result revealed that the estimated odd ratio of household heads and on-street persons‟ readiness to walk is lower in respondents that are single than estimated odd ratio of household heads and on-street persons‟ that are married. Single household heads and on-street persons‟ therefore have negative effects on the readiness to walk. The odd ratio showed that respondents that are single have 0. 923 times more likely to walk than respondents that are married. The result further revealed that married respondents are more likely walk than the single respondents. In terms of education, household heads and on-street persons‟ with secondary and lower level of education have high estimated odd ratio of readiness to walk than 150 UNIVERSITY OF IBADAN LIBRARY estimated odd of not ready to walk. This means that respondents with secondary and lower level of education have positive effect on readiness to walk. The odd ratio also revealed that, a respondent with secondary and lower level of education has 1.062 times more likely to walk than respondent whose level of education is above secondary education. The results of education qualification showed that respondents with secondary and lower level of education are more likely to walk than respondents with post-secondary education. Regarding employment status, the result revealed that respondents estimated odd ratio of readiness to walk is lower for respondents who are employed by government than estimated odd of self-employed respondents. The result further revealed that the odd ratio of a government employed respondent readiness to walk is 0.939 times more likely to walk than self-employed respondent. This means that household heads and on-street persons‟ that are self-employed are more likely to walk than household heads and on-street persons‟ that are working with the government. The result of auto ownership showed that the estimated odd ratio of household heads and on-street persons‟ readiness to walk is lower for household heads and on-street persons‟ that owned vehicle than estimated odd of household heads and on-street persons‟ that has no vehicle. This implies that auto ownership has negative effect on readiness to walk. The odd ratio revealed that a household head and on-street person that owns vehicle is 0.996 times more likely to walk than a household head and on- street person that has no vehicle. The result further revealed that respondents without vehicle are more likely to walk than respondents with vehicles. On the number of vehicles owned by household heads and on-street persons‟, the result revealed that, the estimated odd ratio of household heads and on-street persons‟ readiness to walk is high for respondents with one vehicle than the estimated odd of household heads and on-street persons‟ that has two or more vehicles. The odd ratio showed that a respondent with one vehicle has 1.039 times more likely to walk than a respondent with two or more vehicles. The study further revealed that the estimated odd ratio of household head and on- street persons‟ readiness to walk is lower for household heads and on-street persons‟ whose monthly income is more than N7,500:00 than estimated odd of household 151 UNIVERSITY OF IBADAN LIBRARY heads and on-street persons‟ that earns less or exactly N7,500:00 monthly. The result showed that the odd ratio of a household head and on-street person whose monthly income is more than N7, 500:00 is 0.206 times more likely to walk than a household head and on-street person that earn exactly N7, 500:00 or less. The implication is that household heads and on-street persons that earn N7, 500:00 or less monthly income are more likely to walk than household heads and on-street persons that earn more than N7, 500:00 as monthly income. On work location, of household heads and on-street persons‟, the result revealed that the estimated odd ratio of household heads and on-street persons‟ readiness to walk is high for households and on-street persons‟ living within Ikeja than the estimated odd of households and on-street persons‟ that live outside Ikeja. The odd ratio showed that a respondent living within Ikeja has 1.145 times more likely to walk than a respondent living outside Ikeja. The result showed that work location has positive effect on readiness to walk .In summary, household heads and on-street persons‟ that live within Ikeja are more likely to walk to walk to their place of work than respondents whose work location is outside Ikeja. Table 6.3 also provides explanation to preferred nature of pedestrian facility (walkways or sidewalks) in the logistic model. The results showed that the estimated odd ratio of household heads and on-street persons‟ readiness to walk is high for safety, continuity and the space on pedestrian walkways. Based on safety, estimated odd of household heads and on-street persons‟ who preferred walkways that are safe are higher than household heads and on-street persons that preferred walkways that are not safe. Regarding continuity of walkway, estimated odd of household heads and on-street persons‟ who preferred walkways that are continue is higher than the estimated odd of household heads and on-street persons‟ who preferred walkways that are not continued. Regarding the space on the walkways, estimated odd of household heads and on-street persons‟ who preferred spacious walkways are higher than estimated odd of household heads and on-street persons‟ who did not prefer spacious walkways. These imply that safety of walkways; continuity walkways and spacious walkways have positive effect on household head and on-street person‟s readiness to walk. 152 UNIVERSITY OF IBADAN LIBRARY The odd ratio revealed that a household head and on-street person that preferred safety of walkways is 1.058 times a household head and on-street person who does not prefer safety of walkways. The odd ratio of a household and on-street person that preferred continuity of walkways is 1.34times more likely to walk than a household head and on-street person who does not prefer continuity of pedestrian walkways. Regarding the space on the walkways, the odd ratio showed that household head and on-street person that preferred spacious walkways is 1.128times more likely to walk than when pedestrian walkways are not spacious. The results of the survey further revealed that the estimated odd ratio of household heads and on-street persons‟ readiness to walk is lower for road situation, security and cleanliness of the walkways. Based on road situation, estimated odd of household heads and on-street persons‟ who preferred walkways that are congested are lower than household heads and on-street persons that preferred walkways that are not congested. Regarding security, estimated odd of household heads and on-street persons‟ who preferred walkways that are secured is lower than the estimated odd of household heads and on-street persons‟ who preferred walkways that are not secured. On cleanliness, estimated odd of household heads and on-street persons‟ who preferred clean walkways are lower than estimated odd ratio of household heads and on-street persons‟ who did not prefer clean walkways. These imply congested walkways, secured walkways and clean walkways have negative effect on household head and on-street person‟s readiness to walk. The odd ratio of road situation, security and cleanliness of pedestrian walkways revealed that a household head and on-street person that preferred congested walkways is 0.827 times more likely to walk than a household head and on-street person who does not prefer congested walkways. Also, the odd ratio a household and on-street person that preferred secured walkways is 0.643times more likely to walk than a household and on-street person who does not prefer congested pedestrian walkways. Regarding cleanliness of the walkways, the odd ratio showed that a household head and on-street person that preferred clean walkways is 0.492 times more likely to walk than a household head and on-street person who does not prefer clean pedestrian walkways 153 UNIVERSITY OF IBADAN LIBRARY Table 6.3 further revealed that the estimated odd of respondents readiness to walk, has positive effect on weather while distance, time and season have negative effects. Based on weather, the estimated odd of respondents‟ readiness to walk when the weather is cool are higher than the estimated odd of respondents who are ready to walk when it is sunny. The odd ratio also showed that a household and on-street person who will walk when the weather is cool is 1.811 times a household head and on-street person who will walk when it is sunny. On distance, the estimated odd of respondents on readiness to walk when the distance is equal or greater than 3.2 km is lower than the estimated odd of respondents readiness to walk a distance is less than 3.2km.The odd ratio of a respondent readiness to walk when distance is equal or greater than 3.2 km is 0.907 times respondent who will walk a distance less than 3.2km.Based on time, estimated odd of respondents readiness to walk when in the night is lower than the estimated odd of respondents readiness to walk when it is daylight. The odd ratio also showed that a respondent readiness to walk when it is night is 0.977 times a respondent who will walk when it is daylight. On season, the estimated odd of respondents‟ readiness to walk when it is rainy is lower than the estimated of respondents‟ readiness of to walk when it is dry season. The odd ratio further revealed that a respondent readiness to walk when it rains is 0.93 times a respondent who will walk when it is dry season. The hierarchy of importance of the factors that encourages or discourages respondents‟ decision to walk on walkways is presented in the next section. 6.3 PEDESTRIANS CHOICE VARIABLES OF PREFERRED WALKWAYS The importance of logistic regression analysis in modelling scalable factors is well documented in the literature. However, the hierarchy of importance of preferred nature of pedestrian walkways variables, as presented in table 6.2, is quite different from the value of logistic regression coefficients. In order to ascertain the most significant of these variables (safety, security, road situation, continuity, cleanliness and space) when compared with respondents‟ response and logistic regression coefficients obtained in tables 6.2 and 6.3 respectively. 154 UNIVERSITY OF IBADAN LIBRARY The study used Analytical Hierarchical Process (AHP) model to scale respondents‟ response of on these variables (safety, security, road situation, continuity, cleanliness and space) in order to arriving at the most significant variable that explains households and on-street persons choice of walkways or sidewalks. The results obtained using AHP is compared with results obtained based on percentage response of respondents and coefficients of logit model on the variables. Analytic Hierarchy Process as described in chapter two of the study is a decision- making technique developed in the 1970s by mathematician Thomas L. Saaty. AHP is used in making decisions that are complex, unstructured, and contain multiple attributes. The decisions that are described by these criteria do not fit in a linear framework; because they contain both physical and psychological elements (Partovi, 1994; Palmer, 1999; Mian and Dai, 1999; Nataraj, 2005). The choice of AHP is not limited to its application as decision making algorithm, but also to the fact that perceptions of households and on-street persons about safety, security, road situation, continuity, cleanliness and space of the walkways in the study area could be a function of both physical and psychological elements that may not fit in a linear framework and this informs it use in the study. As a decision algorithm, (safety, security, road situation, continuity, cleanliness and space) of the walkways were tabulated to form a pair wise comparison matrix as presented in the questionnaire of the study. Households and on-street persons response to these variables in the questionnaire is based on a scale (1 to 9) as shown in table 6.4. 155 UNIVERSITY OF IBADAN LIBRARY Table 6.4: The Pairwise Combination Scale of Analytical Hierarchical Process. Intensity Definition Explanation 1 Equal importance Two activities contribute equally to the object 3 Moderate importance Slightly favours one over another 5 Essential or strong importance Strongly favours one over another 7 Demonstrated importance Dominance of the demonstrated in practice 9 Extreme importance Evidence favouring one over another of highest possible order of affirmation 2,4,6,8 Intermediate Values When compromise is needed Source: Adapted from Nataraj S (2005) 156 UNIVERSITY OF IBADAN LIBRARY Table 6.5: Pair wise Comparison Matrix Generated for Households and On-Street on Persons‟ on Preferred Nature of Pedestrian Facility in Ikeja. Safety Cleanliness Security Road Continuity Spacious Situation Safety 1.0000 9/1 7/1 5/1 3/1 1//1 Cleanliness 0.1111 1.0000 9/1 9/1 9/1 9/1 Security 0.1429 1.0000 1.0000 1/1 9/1 9/1 Road 0.2000 0.1111 0.1111 1.0000 1/1 3/1 Situation Continuity 0.3333 0.1111 0.1111 0.3333 1.0000 1/1 Spacious 0.5000 0.1111 0.1111 1.0000 0.3333 1.0000 Source: Field Survey, 2009. 157 UNIVERSITY OF IBADAN LIBRARY Based on the scale of (1) to (9) table 6.5 present pair wise comparison matrix generated from the field survey. Using information in table 6.5, the weight or eigen values of these variables were generated in table 6.6 using Analytical Hierarchy Process calculation software (Japanese version developed) by CGI (see appendix VIII for input and output system of the software). 158 UNIVERSITY OF IBADAN LIBRARY Table 6.6: Weighted Preferred Nature of Pedestrian Walkways in Ikeja. Variables Eigen value Safety 0.4408 Cleanliness 0.2007 Security 0.2022 Road Situation 0.0418 Continuity 0.0488 Spacious 0.0658 Source: Generated using GCI Analytical Hierarchical Process Software 159 UNIVERSITY OF IBADAN LIBRARY After obtaining the weight or eigen value of the variables in table 6.6, the next step in AHP is examination of consistency of the eigen value generated. In order to determine the consistency of the eigen value generated for the variables, Consistency Index (CI) formula as shown below was used. Maximum weight generated from table 6.5 = Consistency index is given as: Where (n) = matrix size, Consistency Index (CI) = 0.6525. In order to accept the weight or the eigen vector generated in table 6.6, the consistency index (CI) must be less than random consistency index (RCI). From random consistency table, for (n=6), (RC = 1.24). From the analysis, CI = 0.6525 RCI = 1.24 CI < RC, that is (0.6525 < 1.24), since the value of RC is greater than the value of CI, the value of eigen vector in table 8.6 is consistent. Hence, the weight or eigen values generated for the variables are accepted. The result of the weight obtained for the variables represent the priority of the respondents in the choice of preferable nature of pedestrian walkways in their decision to walk. Most important variable that will inform households and on-street persons‟ decision to walk on walkways is safety, and the least important variable is road traffic situation. Table 6.7 therefore, shows the ranking of the variables using percentage response of respondents in table 6.2, logit regression coefficients of the variables in table 6.3 and the weight or eigen vector generated in table 6.6 using pair wise comparison matrix. 160 UNIVERSITY OF IBADAN LIBRARY Table 6.7: Summary of Ranking of the Results Generated for Households and On- Street Persons‟ Preferred Nature of Pedestrian Facility in Ikeja. Variables % Response Logit Reg. Coeff. AHP Weight Eigen % Rank β Rank vector Rank Safety 99.3 1 0.056 3 0.4408 1 Cleanliness 98.7 2 -0.427 6 0.2007 3 Security 97.9 3 -0.442 5 0.2022 2 Road Situation 97.0 4 -0.190 4 0.0418 6 Continuity 91.7 5 0.297 1 0.0488 5 Spacious 88.3 6 0.120 2 0.0658 4 Source: Field Survey, 2009. 161 UNIVERSITY OF IBADAN LIBRARY In table 6.7, safety of walkway was ranked 1st as the most significant variable among rd the variables in column two and six but ranked 3 in the fourth column. Cleanliness of the walkway and security along the walkway ranks are similar in column two and nd six, when cleanliness of walkway was ranked 2 after safety of walkway in column rd two, security along walkway was ranked 3 .Similarly, when security along walkway nd rd was ranked 2 after safety in column six, cleanliness of the walkway was ranked 3 . th In the fourth column, security along walkway was ranked 5 while cleanliness was th ranked 6 . th Road traffic situation along walkway was ranked 4 in column two and column four th but, ranked 6 in column six. Continuity of the walkway follow similar trend with th road traffic situation. Continuity of the walkway was ranked 5 in column two and st th column six but, ranked 1 in column four. Space along the walkway was ranked 6 in nd th column two, 2 in column four and 4 in column six. st rd From the analysis, safety was ranked between 1 and 3 , followed by security, cleanliness, continuity, spacious and road traffic situation in that order. From the analysis, it shows that safety and security along walkways is very important in household heads and on-streets persons‟ decision to walk. The advocacy toward a liveable community, where walking and the use of public transport is an increasing option to improving pedestrian movement and safety, areas typically seen as being non-pedestrian-friendly now serves as transit routes. However, as vehicular volumes continue to increase, pedestrians‟ ability to walk or cross many roadways safely is often obstructed. And based on the significance of safety and security of walkways in people‟s decision to walk, the study examines pedestrian level of safety in roadside environment in the section that follows. 162 UNIVERSITY OF IBADAN LIBRARY 6.4 THE ENVIRONMENT FOR WALKING AND PEDESTRIAN LEVEL OF SAFETY The environment for walking or walking environment entails: (i) Provisions of sidewalks and walkways with buffer zones to separate pedestrians from the roadway; (ii) Provisions of street furniture and marked crosswalk. walkways are kept clear of poles, sign posts, news-paper racks, and other obstacles that could block pedestrian paths and marked crosswalks indicate locations for pedestrians to cross and signify to motorists to yield to them; (iii) Provisions of curb ramps and transit stop. Curb ramps (wheelchair ramps) provide access between the sidewalk and roadway for people using wheelchairs, strollers, walkers, hand carts, bicycles, and also for pedestrians with mobility problems who have trouble stepping up and down high curbs. Bus stops should be located at intervals that are convenient for passengers. The stops should be designed to provide safe and convenient access and should be comfortable places for people to wait; (iv) Provisions of Roadway lighting. Good placement of lighting can enhance an environment as well as increase comfort and safety. In commercial areas with night time pedestrian activity, street lights and building lights can enhance the ambiance of the area and the visibility of pedestrians by motorists; (v) Provisions of Pedestrian underpasses and overpasses Pedestrian overpasses and underpasses allow for the uninterrupted flow of pedestrian movement separate from the vehicle traffic. The environment for walking is all about safety and comfort of pedestrians in roadside environment. The results of the Descriptive Analysis, Logistic Regression Analysis, and Analytical Hierarchical Process on preferred nature of walkways show that safety and security on the walkways are factors that are most significant in respondents‟ decision to walk. There is also a general consensus that pedestrians‟ sense of safety (level of safety) or comfort within a roadway corridor is based on a 163 UNIVERSITY OF IBADAN LIBRARY complex variety of factors. These factors include personal safety, (i.e., the threat of crashes), personal security (i.e., the threat of assault), architectural interest, pathway or sidewalk shade, pedestrian-scale lighting and amenities, presence of other pedestrians, conditions at intersections, and so on ( Gaventa, 1980; Hass-Klau et al, 1994; Gehl, 1999; Smith, 1999; Pedestrian‟s Association,2000; Tight et al, 2004; Ovstedal and Ryeng, 2006; Rahaman, 2007 ; Landis et al, 2001 ; Raji, 2010). This section modelled pedestrian level of safety based on pedestrian threshold (Level of Service) along roadside environment in Ikeja. Developing a model of pedestrian level of safety in Ikeja involved real time observations of 56 road segments where pedestrian activities predominate. Six of the 56 road segments have walkways and field measurement along roadways or streets complemented the field observations. The section therefore, examines factors affecting pedestrian walking environment, develop and pedestrian level of safety model and discusses empirical findings of the model results 6.4.1 Factors Affecting Pedestrian Walking Environment The perceived safety or comfort (with respect to the presence of motor vehicle traffic) has not, until now), been quantified as a stand-alone performance measure (Landis et al, 2001), and this measure has also not been subjected to extensive research. It has been observed that the factors or variables that are significant in influencing pedestrians‟ level of safety or comfort include: 1. Presence of sidewalk 2. Lateral separation from motor vehicle traffic 3. Barriers and buffers between pedestrians and motor vehicle traffic 4. Motor vehicle volume and composition 5. Effects of motor vehicle traffic speed, and 6. Driveway frequency and volume. However, in a long list of independent variables thought to have been influencing pedestrians‟ level of safety within streets or roadways, the following factors are very essential in the explanation of pedestrians‟ level of safety, and they are: 164 UNIVERSITY OF IBADAN LIBRARY 1. Lateral separation - elements between pedestrians and motor vehicle traffic: These include (i) presence of sidewalks, (ii) width of sidewalk, (iii) buffers between sidewalk and motor vehicle travel lanes, (iv) presence of barriers within the buffer area, (v) presence of on-street parking, (vi) width of outside travel lane and presence and width of shoulder or bike lane 2. Motor vehicle traffic volume 3. Effect of (motor vehicle) speed 4. Motor vehicle mix (i.e., percentage of trucks) 5. Driveway access frequency and volume. In the quest of modelling pedestrian level of safety in the study area, these factors or variables were use in the development of the model as presented in section 6.4.2. 6.4.2 Development of Pedestrian Level of Safety Model in the Study Area Establishing pedestrian level of safety model in the study area involve obtaining the pedestrian level of service along fifty-six (56) road networks in the study area in Ikeja. Pedestrian level of service is a threshold for free flow of pedestrian movement on walkways and this serves as surrogate for determining pedestrian level of safety along roadside (James and Walton,2000;Henson,2000;Gallin,2001; Jasskiwicz, 2001). Fruin (1971) in an attempt to measure level of comfort of pedestrians in an urban setting used physical count and time lapse photographic images of pedestrian movements in Manhattan sidewalks. In the study of Fruin (1971), Pedestrian Level of Service (LOS) as recommended for available space per pedestrian in table 6.8 has been a reference point for many studies in developed and developing countries. Some of these studies include Pushkarev and Zupan (1975a), Pushkarev, Boris and Zupan (1975b), Brilon, Polus, Joseph and Ariela (1983), Tanaboriboon et al (1986) and Guyano, High Way Capacity Manual (1994), Milazzo et al (1999) (see Table 6.9). 165 UNIVERSITY OF IBADAN LIBRARY Table 6.8: Recommended Level of Service Threshold for Free Flow Movement on Pedestrian Walkways Level of Service Flow rate Available Space per Recommended Use 2 (Ped/minute/metre) pedestrian (m /ped) A <23 >3.3 Large scale public plazas B 23-33 2.3-3.3 Transportation terminals for routine low level flows C 33-49 1.4-2.3 Transportation terminals serving high volumes D 49-66 0.9-1.4 Highest tolerable flows for public spaces E 66-82 0.5-0.9 Threshold of intolerable operation F Var-82 <0.5 Queue formation Source: Pedestrian Planning and Design by Fruin (1971) 166 UNIVERSITY OF IBADAN LIBRARY Table 6.9: Walkway Level of Service (LOS) Thresholds by Available Space per 2 Pedestrian (m /Ped) Pushkarev Brilon Polus Tanaboriboon and LOS FRUIN *HCM and (Germany) (Israel) Guyano(Thailand) Zupan A >3.2 >12 >12 >10 n/a >2.38 B 2.3-3.2 3.7-12 4-12 3.3-10 n/a 1.6-2.38 C 1.4-2.3 2.2-3.7 2-4 2-3.3 1.67 0.98-1.60 D 0.9-1.4 1.4-2.2 1.5-2 1.4-2 1.33-1.66 0.65-0.98 E 0.5-0.9 0.6-1.4 1.0-1.5 0.6-1.4 0.5-0.8 0.37-0.65 F <0.5 <0.6 0.2-1 <0.6 n/a <0.37 Source: Platoon Pedestrian Movement Analysis: A Case Study Utilizing the Market Street Station in Denver, Colorado by James and Walton (2000). *HCM = Highway Capacity Manual 167 UNIVERSITY OF IBADAN LIBRARY In this study, pedestrian level of safety was based on variables listed in section 6.4.1.These variables were derived through measurement and real time observations rather than pedestrian perceptions of level of service. The factors considered the most probable variables affecting pedestrians‟ level of safety are based on Pedestrian Level of Service (PLOS) and they were model in line with hypothesis IV that states that: Most people walk in areas where their level of safety is higher, than in areas where their level of safety is lower and their trips safety is a function of lateral separation, traffic volume, vehicles’ speed and drive way access. The explanation of the explanatory variables is presented under the following subheadings. These include; . 6.4.2.1 Presence of a Sidewalk and Lateral Separation Having a safe, separate place to walk alongside the roadway is fundamental in pedestrians‟ sense of safety and comfort in the roadway environment. This sense of safety or comfort is strongly influenced by the presence of a sidewalk. Furthermore, the value of a sidewalk varies according to its location and buffering (i.e., the lateral separation) relative to the motor vehicle traffic (Landis et al, 2001).Where there are sidewalks in Ikeja, for instance, the values of lateral separation varies and buffer zones serves as drainage and on-street parking. In general, lateral separations as specified by Landis et al (2001) in figure 6.1a, figure 6.1b, figure 6.2a, figure 6.2b and 6.2c. Figure 6.1a and figure 6.1b showed the effect of lateral separation, which is the effect pedestrian distance from the moving traffic. Figure 6.2a, figure 6.2b and figure 6.2c showed typical barriers within the roadside buffer 168 UNIVERSITY OF IBADAN LIBRARY Figure 6.1a: Pedestrian Walking alongside Vehicle on the Outside Lane. Figure 6.1b: Pedestrian Separated from Outer Lane by Buffer Zone. 169 UNIVERSITY OF IBADAN LIBRARY Figure 6.2a: On-street parking within Roadside Buffer Zone. Figure 6.2b: Drainage or Swade within Roadside Buffer Zone. 170 UNIVERSITY OF IBADAN LIBRARY 2 Figure 6.2c: Trees within Roadside Buffer Zone . Figure 6.2d: An illustration of roadway a in Ikeja. 2 Figure 6.1a to figure 6.2c show the distance of pedestrian from moving vehicles. These figures also showed different buffer zones that are provided in order to protect pedestrians from moving vehicles. 171 UNIVERSITY OF IBADAN LIBRARY Plate 6.1 shows the situation of lateral separation (LS) in many of the road segments in Ikeja and Plate 6.2 shows a pictorial example of figure 6.2d in Ikeja roadways, but plate 6.2 lack buffer and walkways. Mathematical expression that explains elements of lateral separation, barriers, buffers, and presence of walkways as expressed by (Landis et al, 2001) and modified to reflect the situation in the study area is presented in equation 6.8: LS WOL WSBL  fOPC %OSP fBAC WBW  fSWCWWOS  6.8 Where: LS = Lateral separation WOI = Width of outside lane (metre) WSBL = Width of shoulder or bike lane (metre) fOPC = On-street parking effect coefficient %OSP = Percentage of segment with on-street parking f BAC = Buffer area barrier coefficients WBW = Buffer width (distance between edge of pavement and sidewalk, feet) f SWC = Sidewalk presence coefficient WWOS = Width of sidewalk (feet) Quantification of lateral separation elements in equation 6.8 is illustrated in figure 6.7. 172 UNIVERSITY OF IBADAN LIBRARY Plate 6.1: Pictorial Presentation of figure 6.1a in many road segments in Ikeja 173 UNIVERSITY OF IBADAN LIBRARY Plate 6.2: A roadway in Ikeja with a divide but Lack Buffer and Walkways 174 UNIVERSITY OF IBADAN LIBRARY Figure 6.3: Quantification of Lateral Separation Elements Source: Landis et al (2001) 175 UNIVERSITY OF IBADAN LIBRARY Where, WOI  Wol WSBI Wl WBW  Wb WWOS  Ws the explanations of , and , which were given in page 168. From equation 6.8, when there is no on-street parking, the % OSP becomes zero. Then the lateral separation equation becomes: LS W W  f W  f W  OL SBL BAC BW SWC WOS 6.9 In the case where there is on-street parking, its effect as a barrier would be quantified as in equation (6.9). But where there are no striped shoulders or landscape buffer, then the terms WSBL and WBW becomes zero. Then, the lateral separation equation is simplified to equation 6.10. LS WOL  fOPC %OSP fSWCWWOS  6.10 In the case where there is on-street parking, but there is no bike lane, WSBI equal zero then the lateral separation equation is simplified into equation 6.11. LS WOL  fOPC %OSP fBAC WBW  fSWCWWOS  6.11 In the case where there is no sidewalk, no bike lane, no striped shoulder or buffer, then lateral separation equation becomes equation 6.12. LS WOL  fOPC %OSP 6.12 In the case where there is no sidewalk, no bike lane, no striped shoulder or buffer, and no on street parking then lateral separation equation then change to equation 6.13. LS W OL 6.13 In the 56 road segments in Ikeja, many of the road networks or streets are characterised with no sidewalk, no bike lane, no striped shoulder or buffer and with the operation of Lagos State Traffic Management (LASTMA) officers in Ikeja roadways, most of the roadways and streets are free of on-street parking. 176 UNIVERSITY OF IBADAN LIBRARY In the road segments where there are sidewalks, equation 9.3 was used to compute the lateral separation. Where there are no sidewalks, no bike lane and no striped shoulder or buffer, equation 6.12 was used to obtain the values of the lateral separation. Furthermore, equation 6.13 was used where there are no on-street parking, no sidewalks, no bike lane and no striped shoulder or buffer. The equations used to compute lateral separation of the road segments in the study area are equations 6.10, 6.12 and 6.13 that is LS WOL  fOPC %OSP fSWCWWOS  LS WOL  fOPC %OSP LS W, and OL . 6.4.2.2 Motor Vehicle Volume The frequency of motor vehicles passing pedestrians is represented by the outside lane volume, and also found to be a factor or variable. As passing frequency of motor increases, pedestrians‟ feeling of safety may increase or decrease. So, the effect of traffic volume was calculated using the following: formula in equation 6.14: 6.14 Where: TV  Traffic Volume Vol15  Average traffic during a fifteen (15) minute period L  Total number of (through) lanes (for road or street) Equation (6.14) assumes a 50/50 directional distribution. In cases where the directional distribution is other than 50/50, equation (6.15) should be used. The difference between the two equations (i.e. 6.14 and 6.15) is that while equation 6.15 uses a directional factor with “Ld” (total number of directional through lanes), equation 6.14 uses “L” (totals number of thru lanes). 6.15 177 UNIVERSITY OF IBADAN LIBRARY Vol15  Average traffic during a fifteen (15) minute period Ld  Total number of directional (thru) lanes (for road or street) D  Directional factor In this study equation 6.14 that is . / was used to compute the values motor vehicle volume along each road segments selected in the study area (see Appendix VIII). 6.4.2.3 Effect of Speed Speed of motor vehicle traffic was confirmed as a variable or factor influencing pedestrians‟ level of safety. As speed increases, pedestrian discomfort may increase or decrease. The speeds of moving vehicles along 56 road segments were recorded using speed radar gun. 6.4.2.4 Driveway Access Frequency and Volume Along a roadway or street segment, uncontrolled vehicular accesses to adjoining properties (i.e., driveway cuts) have shown in many studies to have been influencing pedestrian level of safety. In this study, drive access frequency to adjoining properties was recorded through observations of passing motor vehicles along the road segments and the values are presented in appendix VIII. Based on the measurement of the discussed variables, Pedestrian Level of Safety (PLOS) in Ikeja is model in the mathematical expressions in equation 6.16. Pedestrian LOS = C + b1 f (lateral separation factors) + b2 f (traffic volume) + b3 f (speed, vehicle type) + b4 f (driveway access frequency and volume) + bn f xn  + α 6.16 The result of field measurements and observations using the model in equation 6.16 is discussed in section 6.4.3 below. 178 UNIVERSITY OF IBADAN LIBRARY 6.4.3 Pedestrian Level of Safety Model Results The results of the observations and measurements of 56 road segments in Ikeja using equation 9.8 is presented in table 6.10. Appendix IX provide values obtained for lateral separation, motor vehicle volume, speed of motor vehicles and drive way access frequency and volume during field observations. 179 UNIVERSITY OF IBADAN LIBRARY Table 6.10: Relationship of Pedestrian Level of Safety along Road Segments in Ikeja. Model Terms Coefficients t-statistics p>value Lateral Separation . lin (LS) 0.596 5.139 000 Motor Vehicle Volume . Vol 0.204 1.732 000 lin ( 15 ) L lin(Speed of Motor Vehicle) 0.072 0.756 0.089 lin (Drive way access frequency and volume - 0.067 - 0.745 0.453 Constant -2.355 - 4.363 0.460 2 Model Adjusted (R ) 0.581 Model F-Ratio 20.089 Source: Field Survey, 2009. 180 UNIVERSITY OF IBADAN LIBRARY Table 6.10, shows that all the independent variables used in the model are statistically significant at 95% confidence level except “driveway access frequency and volume”. The results of PLOS model of road segments in Ikeja area of Lagos agrees with the works of (Sarkar, 1993; 1996; Khisty, 1994; Dixon, 1996; Crider, 1998; Landis et al, 2001) on the statistical significance of lateral separation, motor vehicle volume and speed of motor vehicles. Thus, the model for Pedestrian Level of Service (PLOS) of road segments in Ikeja is given as: , ( ) ( )- ( ⁄ ) ( ) From the model, lateral separation is significant. The result shows that lateral separation increase pedestrian level of safety or comfort. As lateral separation increases, pedestrians‟ level of safety increases. This means that level of safety is strongly influenced by the presence of a sidewalk. For instance, when a barrier such as on-street parking, a line of trees, or a roadside swale (see Figures 6.2, 6.3, 6.4, and 6.5) is near or on the buffer area between motor vehicle traffic and the pedestrian, pedestrians‟ feel protected, hence safety, is enhanced. The frequency (volume) of motor vehicles passing pedestrians, represented by the outside lane in the results also found to be a significant variable. As volume of motor vehicles passing pedestrians along road segments in the study area increases the pedestrians‟ level of safety decreases. This often occurs when barriers at the buffer area are removed or there is no sidewalk and pedestrians shared width of outside lane ( ) with motor vehicles as shown in plates 6.1 and 6.2. Pedestrians‟ level of safety tends to decrease because pedestrians are exposed to road traffic accident. The results of the model also revealed that speed of motor vehicle traffic is significant in the model and this means that, as the speed of motor vehicle increases along roadside walking environment, pedestrian level of safety decreases. This implies that pedestrian level of safety increases with a lower speed of motor vehicle traffic along road environment in the study area. 181 UNIVERSITY OF IBADAN LIBRARY Regarding vehicular access to adjoining properties (that is driveway cuts), respondents were of the opinion that their level of safety declines with escalation of vehicular access to adjoining properties along roadside walking environment. However, the regression results showed that at 95% confidence level, vehicular access to adjoining properties of the 56 road segments along the roadside walking environment was not significant in the model. This means that an increase or decrease in vehicular access to adjoining properties along pedestrian roadside walking environment has little or no effect on the level of safety of pedestrians. The coefficient of determination of the model ( ) revealed a goodness fit of the explanatory variables to the level of safety along the sampled road segments in Ikeja. However, the explanatory variables (lateral separation, motor vehicle volume, speed of vehicle and driveway access frequency and volume) contributed (58.1%) to the explanation of pedestrian level safety along roadside walking environment in Ikeja. Similarly, the F-Ratio ( ) of the model is also significant at 95% confident level. The results of the model show that the level of explanations ( ) and variability and ( ) of pedestrian level of safety is high across road segments in Ikeja. 6.5 SUMMARY The chapter examines the decision of household heads and on- street persons‟ readiness to walk. The study showed that the number of respondents that are ready to walk is greater than the number of respondents that are not ready to walk. From the chapter it was observed that female respondents are more willing to walk than the male respondents and this is because women engage is certain activities such as going to the market, taking children to and from school, and going for shopping that involve walking. The study further showed that age group between 35 years and below is more likely to walk in Ikeja. From the study, it was equally discovered that the number of respondents that owned vehicles are more than the number of respondents without 182 UNIVERSITY OF IBADAN LIBRARY vehicle, and as expected, respondents without vehicle are more likely to walk than respondents with vehicles. Also, among respondents with vehicles, respondent that has one vehicle are more likely to walk than respondent with two or more vehicles. The study also revealed that respondents working within Ikeja are more likely to walk than respondents working outside Ikeja. The result of the study also showed that respondents that earn N 7,500:00 and more monthly are not likely to walk than respondents that earn less. This means that an increase in monthly income of respondents may discourage them from walking. On preferred nature of pedestrian walkways, respondents are more likely to walk if the walkway is safe, secured, clean, continue, spacious and not congested. On safety, the study affirmed the studies of (Hillman, Adams and Whitelegg 1990; DETR, 1997; Forward, 1998; Hamilton, 2000; Ackett, 2001; Living Streets, 2001; Dodd, Nicholas, Povey and Walker, 2004; Tight, Kelly, Hodgson and Page, 2004; Ovstedal and Ryeng, 2006) who observed that fears of about personal safety are one of the factors identified explicitly in empirical work as influencing both pedestrian route and mode choice. They further observed that some people do not walk because of fear of attack. They equally affirmed that the level of fear is greater in urban areas compare to rural areas and people avoid having to walk because of anxieties of personal safety and security. These scholars also observed that, parents and guardians have come to fear that their children will be attacked and abducted by strangers whilst in the street which has led to a restriction on children‟s freedom to play outside or walk. On road traffic situation, the study affirmed the study of (Appleyard and Lintell, 1972; Hillman, Adams and Whitelegg 1990; Bly, Dix, and Stephenson, 1999; Bradshaw and Jones, 2000) who discovered that parents restricted their children„s freedom to walk more because of their fears about road traffic than their fears about strangers assaulting their children. Road traffic type and volume is also given as a factor in choosing not to walk. They equally observed that emissions from traffic such as noise and air pollution also affect at the extremes the decision to walk. The survey further showed that respondents are likely to walk when the distance is less than 3.2km, weather being cool and not sunny, day time and during dry season. On distance, the study affirmed the study of (Forward, 1998; Hillman, 1999; Hodgson 183 UNIVERSITY OF IBADAN LIBRARY and Tight, 1999; Bradshaw and Jones, 2000; Goodman, 2001; IHT, 2000; Stradling, 2000; Living Streets, 2001; Mackett, 2001) who observed that distance between services have grown and this increase journey time. The convenience of motor trip due to increasing distance often discouraged individuals to make a walking trip. Based on season, the study also affirmed the study of (Forward, 1998; Hodgson, 2000; Tight et al, 2004).They observed that weather often comes up in the lists of factors that people find significant in the decision to walk. For short or long trips, dry weather had a positive impact on the decision to walk. It is not only the discomfort of walking in rainy weather that can deter people from walking but also the fact that one has to dress in the appropriate clothes for the weather. Regarding time, some studies relate it with season, distance or daytime. In this study it is a concept related to daytime and night, and it confirmed the study of (Virilo, 1986; Adams, 1995; Forward, 1998; McNaughten and Urry, 1998; Ackett, 2001; Living Streets, 2001; Hamilton, 2000; Burkit, 2000; Goodman, 2001; 2005).These scholars were of the opinion that because of personal security, people, women in particular organise journeys to avoid having to walk at night. They also observed that shift workers go to extra ordinary lengths to make sure that they are not walking or catching public transport at night. Hence, they argued that: lifecycle time; necessary time; (involve the complex scheduling of routine and domestic tasks); work time; and travel time are useful in understanding the motivation to walk. The results of regression analysis of pedestrian level of safety along road segments in the chapter shows that distance of pedestrian from moving vehicle (lateral separation), speed of moving vehicles, and volume of vehicles are significant at 95% confidence level except drive way access frequency and volume. The result further showed that pedestrian level of safety increases as the distance between vehicle and pedestrians increases. The study further revealed that pedestrians level of safety or comfort decreases with increasing volume of motor vehicles and motor vehicle‟s speed. The results of pedestrian level of safety along road segments in the study area in Ikeja is in line with the studies of (Forward, 1998a; Forward 1998b; Forward, 2001; Roberts, 1989; Zuckerman, 1993; Lee, 1984; Cassidy 1997; Appleyard and Lintell 1972) who observed that heavy presence of traffic do affect pedestrian level of safety and social network; thereby discourage people not to walk. Gunnarson (1999) study also showed 184 UNIVERSITY OF IBADAN LIBRARY that the outcome of an accident between a car and a pedestrian depends very much on the speed of the vehicles. Furthermore, he observed that the death rate quickly increases from 30 km/h to 70 km/h when basically nobody will survive. The coefficient of determination of the model showed that about 58.1% of pedestrian level of safety and comfort along roadside walking environment is provided by lateral separation, motor vehicle volume, speed of motor vehicle and drive way access and volume. About 41.9% are unexplained variation, which showed that there are other factors such as quality of walkway, noise, air pollution - emission from moving vehicle and so on that might contribute to pedestrian level of safety along roadside walking environment. Nevertheless, the model was able to give an insight into factors or variables that influence people level of safety and comfort along roadside walking environment. Pedestrian walkways or sidewalks are very significant in the design of road network. However, this aspect of our road is the most neglected. In any road network that involves human and vehicular movement, safety and security, comfort, quality of the footpath and other pedestrian facilities influence the decision to walk. Spacious walkways, continuity of walkways and decongested walkways affects pedestrian level of safety. In other to safe guard pedestrians, urban and transport planners need solid guidance on how to design roadside walking environments. Pedestrian Level of safety model in Ikeja thus provides a guide to urban and transport planners on (i) how far sidewalks should be placed from moving traffic; (ii) when, and what type of buffering or protective barriers are needed; (iii) how wide the sidewalk should be (iv) when and where to allow on-street parking or shoulder lane and (v) when and where to pedestrianized urban centre in order to discourage vehicular movement and encourage walking or pedestrian movement. At this juncture, the summary of findings, conceptual and theoretical contributions, implication of findings to planning, and further research needs of the study are presented in chapter seven. 185 UNIVERSITY OF IBADAN LIBRARY CHAPTER SEVEN CONCLUSION This study examined the fundamental developments that explain pedestrian movement in Ikeja area of Lagos State, Nigeria; with a view to identifying, understanding and explaining the processes and patterns associated with pedestrian trips in urban areas. The study considered walking distances of household heads and on-street persons (pedestrians), volume of pedestrian trips generated by household heads and on-street persons, factors influencing household heads and on-street persons‟ decision to walk and pedestrian level of safety along roadways in Ikeja. This chapter summarises the findings, examines conceptual and theoretical contributions by linking findings implication to planning, and as well discusses further research needs of the study. 7.1 SUMMARY OF FINDINGS OF THE STUDY The study showed that land use and economic activities are pull factors that enhanced pedestrian movement in the study area. Movement of people along the road network in Ikeja revealed that pedestrian activities are common, but there are no pedestrian facilities such as walkways, pedestrian shed and side road furniture along the road networks. Also, noticeable within the congested road, is competition between pedestrians and vehicles who try to claim right of way. Another important finding in the study area is indiscriminate parking of vehicles along the road corridors or walkways meant for pedestrians. Pedestrians therefore jaywalk or spread on the road networks. The result of the study showed that there was an average flow of 56,663 pedestrians along the streets between 7am and 7pm of the survey and the pattern of flow varies significantly ( ) across zones. The lowest average hourly pedestrian flow in the study area was recorded between 7am and 8am and the highest flow was recorded between 5pm and 6pm with flow rate of 1,788 and 6,313 pedestrians respectively. Between 7am and 8am, there is 54.5% increase in pedestrian flow. Between (9am and 10am) and (3pm and 4pm) 186 UNIVERSITY OF IBADAN LIBRARY pedestrian flow in the study area decreases by 25.6% and increases by 46.5% between (3pm and 4pm) and (4pm and 5pm).The study also revealed that between (4pm and 5pm) and (5pm and 6pm) pedestrian flow increases by 18.2% and there is a reduction of 41.1% in the average number of pedestrians flow between (5pm and 6pm) and (6pm and 7pm). Though, the study showed a 51.9% increase in pedestrian flow between (7am and 8am) and (6m and 7m), the results showed a pedestrian flow pattern of an increase as early as 7am in the morning a decrease around 7pm in the night. The result of the study shows that majority of both household heads (60.7%) and on- street persons (89.8%) do walk as pedestrian. The period between 5:30 am to 6:00 am, 94.6% of pedestrian trips originate from home and 86.7% of similar trips are home bound between 6:00 pm to 8:00 pm daily. The result of the study also revealed that nine categories {work (30.4%), recreation (6.7%), religious function (20.5%), business (9.2%), schooling (7.4%), shopping (17.4%), social function (3.3%), visiting friend (1.3%), exercising (3.6%)} of walk trip purposes were generated by household heads and on-street persons. On proportional basis, work (30.4%), accounted for the highest walk trip purpose and the least is friends‟ visiting (1.3%). Interestingly, religious function (20.5%) is next to work in order of magnitude. From the survey, it was also observed that female respondents have tendency (1.02 times) to walk more than their male respondents and this action may be attributed to the activities such as shopping, picking children from school, going to the market and other activities which women engaged in. On walking distance, the mean walking distances taken by household heads to bus stations ( ̅=195m, δ=31.37) and land use activities ( ̅=1.53km, δ=3.77) is lower than on-street persons‟ walking distances to bus stations ( ̅=292m, δ=33.78) and land use activities ( ̅=2.47km, δ=5.76). The mean walking distances by household heads to bus stations and landuse activities was ( ̅=0.244km, δ=0.02) while that of pedestrians was ( ̅=2.km, δ=0.10) which is significant at (t=71.01, p=≤0.05). The ANOVA results {( ) ( ); ( )} on maximum walking distance of respondents to bus station, land use activities and bus station and land use activities respectively do not 187 UNIVERSITY OF IBADAN LIBRARY support hypothesis one of the study that state that: the maximum distances people are ready to walk to bus stations, landuse activities and various functions and services do not vary across zones in the study area. The ANOVA results showed that the maximum distances people are ready to walk to bus stations, landuse activities and various functions and services vary across zones in the study area. Predicting pedestrian trips generated by household heads and on-street persons, explanatory variables such as trip characteristics {work (β = 0.354, t = 10.125), religious function (β = 0.320, t = 8.415), business (β = 0.074, t = 2.627), shopping (β = 0.258, t = 7.138), friends visiting (β = 0.076, t = 1.843), exercise (β = 0.082, t = 2.432)}; and the number of economic activities household heads and on-street persons engaged in { industries (β = 0.452, t = 2.726), hotel and restaurant (β = 0.345, t = 1.103), shopping mall (β = 0.047, t = 1.577), fast-food points (β = 0.288, t = 3.816)} are the significant variables that explains pedestrian trips in the study area. Based on decision to walk, the result from the study shows that respondents below 35years are more likely to walk than respondents whose age falls between 35years and above. It was equally observed that the number of respondents that owned vehicles are more than the number of respondents without vehicle, and as expected, respondents without vehicle are more likely to walk than respondents with vehicles. Among respondents with vehicles, respondents that have one vehicle are more likely to walk than respondents with two or more vehicles. The study also revealed that respondents working within Ikeja are more likely to walk than respondents working outside Ikeja. The result also showed that respondents that earn N 7,500:00 and more monthly are not likely to walk than respondents that earn less. This means that with increasing income of respondents, there is tendency of respondents not willing to walk. Another finding shows that people are likely to walk if the walkway is safe( ), secured( ), continue( ), clean( ), and spacious( ).The survey further showed that people are likely to walk when the distance is less than 3.2km ( ), weather being cool and not sunny ( ), during daylight ( ), and in dry season ( ). 188 UNIVERSITY OF IBADAN LIBRARY The study also provides empirical study of pedestrian level of safety in 56 road segments where pedestrian movement predominate. Of the 56 road segments sampled, only 6 have walkways. People are left with no other choice than sharing the available space (roadways) with motor vehicle, and each of the road users claim the right of way. However, the consequence is vehicular and pedestrians conflicts, which either result to lose of life or injury on the part of the pedestrian. Another major finding is that pedestrian level of safety variability ( 2 ) and explanation (R =0.581) is higher on roadside in the study area. Furthermore, pedestrian level of safety in the study area increases with increasing distance of sidewalks from moving vehicles (β1 = 0.596, t1 = 5.139), but decreases with increasing vehicles‟ volume (β2 = 0.204, t2 = 1.732), and vehicles speed (β3 = 0.0.072, t3 = 0.756) on roadside. However, at 95% confidence level drive way access frequency and volume (β4 = - 0.067, t4 = - 0.745) is not a significant variable in the prediction of pedestrian level of safety on road segment in the study area. The coefficient of determination of the model showed that about 58.1% of pedestrian level of safety and comfort along roadside walking environment is provided by lateral separation, motor vehicle volume, speed of motor vehicle and drive way access and volume. About 41.9% are unexplained variation, which showed that there are other variables such as quality of walkway, noise, air pollution - emission from moving vehicle and so on that might contribute to pedestrian level of safety along roadside walking environment. Nevertheless, the model was able to give an insight into factors or variables that influence people level of safety and comfort along roadside walking environment in the study area. A safe, secure and separate place to walk along roadways or streets is essential in rural, suburban and urban area of any country. Safe guarding pedestrians, road designers need solid guidance on how to design pedestrian environments, the study revealed that the LOS model of Ikeja is an understanding of roadway design of (i) how far sidewalks should be placed from moving traffic; (ii) when, and what type of buffering or protective barriers are needed; (iii) how wide the sidewalk should be (iv) 189 UNIVERSITY OF IBADAN LIBRARY when and where to allow on-street parking or shoulder lane and so on so as to protect pedestrian and as well encourage walking. 7.2 CONCEPTUAL AND THEORETICAL CONTRIBUTIONS This study looked at landuse and a number economic activities such as number of financial institutions; industries, fast food points, shopping malls, hotels and restaurants and so on, which are part of geographical phenomena represented by point pattern. The study used roads, streets as network which allowed common geometrical properties such as origins of pedestrian trips which is represented by nodes, routes or streets that pedestrians move is represented by links and various destinations of pedestrians is represented by nodes. Networks are structures designed to tie together nodes via routes, whether they are flows of people, goods, information, money, and so on. Pedestrian use different modes and transport facilities to achieve their trips from point of origin to their various points of destinations. Graph theory enhances network analysis in urban geography. Shimbel index, a technique in graph theory was used in this study to assess the level of accessibility of the street network in zones understudy. Level of accessibility in relation to land use activities contribute significantly to the observed volume pedestrians found along road network of interest in the zones understudy. Behavioural approach put greater emphasis on decision making processes that generate various kind of spatial pattern. The approach was introduced into urban geography through the study of movement patterns, especially those associated with intra-urban movement such as „journey to work and journey to shop‟ (Herbert and Johnson, 1978). Models describing individual decision making process at various kinds of choice situation are now common and we built on such behavioural concepts as place utility, stress and information space. The second stand within the behavioural approach relates to the notion of individual cognition of urban environment. Urban dwellers possess cognitive and mental maps of their environment, and the maps are far from being identical with the actual physical structure of the city. 190 UNIVERSITY OF IBADAN LIBRARY In the study the use of the model (Analytical Hierarchy Process), helps to capture both subjective and objective evaluation measures of pedestrian decision to walk on available pedestrian facilities. It also provides a useful mechanism for checking the consistency of the evaluation, measures and alternatives suggested by respondents and thus, reducing bias in their decision making. Analytical Hierarchical Process is significant to this study in that as a decision-support system (DSS), it helps using weight to explain significant factors affecting the decision of household heads and on street persons to walk on walkways and subsequently determine factors needed to model pedestrian level of safety in Ikeja. Geographers are equally interested in describing and explaining spatial interaction or movement pattern within cities. These movement patterns might be temporary in nature, such as journey to work, shops, recreation and so on, or might be permanent such as residential mobility or the changing of resident within the city. Daily movement pattern consists of trips involving activities as work, shopping, recreation and so on. Attempt to forecast daily movement pattern involve the construction of inter-related models: trip generation, trip distribution, modal split and traffic assignment (Stopher and Meyburg, 1975; Cadwallader, 1985). The study showed that issue of how movements take place involves travel choices of individuals. As earlier stated, movement issues are represented by “four step process”. These four steps are supposed to represent the thought process of individual, because, individual makes four travel decisions as follows: (i) the decision that a trip is necessary to fulfil some need or purpose (generation), (ii) the decision where that need or purpose is best fulfilled (distribution), (iii) the decision of which means is best to get there (mode choice) and (iv) the decision of which route to take (trip assignment). These basic analogies of the traditional 4-stage transport model which are trip generation, trip distribution, modal split and traffic assignment. Trip generation involves trip production and trip attractions for individual zones which are related primarily to the type of land use. The study has shown that human movements in urban centres are consequences of spatial imbalance created by urban land use types. Urban spatial structure therefore, emerged along two different lines of survey and the most relevant to this study, is classical theories which include 191 UNIVERSITY OF IBADAN LIBRARY Concentric Zone Theory (Burgess, 1925); Sector Theory (Hoyt, 1939) and Multiple Nuclei (Harris and Ullman, 1945). These theories examine the expansion of city by explaining the processes of urban metabolism and mobility that revolves around a single centre to multiple centres. These urban metabolisms help to generate trips within and outside the city. Theories of urban spatial structure provide basis for urban mobility in the city. They also provide explanation to location behaviour of households and group (Ayeni, 1979; Aluko, 2004). The relevance of these theories to pedestrian movements is their trip generating capability. City centres are focal point of socio-economic activities and because of their potential trip attractions, different land use types generate varying pedestrian traffic and trips within and outside residential, commercial, industrial, institutional and recreational zones. Studies have shown that residential, manufacturing, public lands appear to generate about the same pedestrian trips per squares mile, but number of pedestrian trips associated with commercial land use are often considerably higher as observed in this study. As one would expect, the higher the land use density, the greater the trip attraction zones. The subunits into which the study area was divided show the characteristics of sector model of axial growth along transport routes with few income groups tend to live in distinct area. Areas where such „distinct area‟ can be found in the subunits are Allen Area (13), Alabi Area (15) and Kudeti Area (4). Subunits that show similar characteristics of multiple nuclei model include Otigba Area (1) , Governor Area (7), Awosika Area (2), Obanta Area (3), Akeem Balogun Area (5), Ajanaku Area (6), Kadiri Area (8), Olanrewaju Area (9), Secretariat Area (10), Kasumu Aleshinloye Area (11), Morrison Area (12), and Unity Area (14), Community Area (16) and Acme (17). In these subunits, a Central Business Districts (CBD) exists in each of the zones. Furthermore, many traditional and indigenous centres reside in the core part of the city. For instance, core areas of the study area still retained the Yoruba traditional compound structure.Although, some of the residential buildings built by the Government are meant for low income people, but in reality residential districts in the 192 UNIVERSITY OF IBADAN LIBRARY study area do not follow social class differentiation. In other words, the study area departs remarkably from the classical pre-industrial urban pattern of distinctive residential differentiation and social region typical of United State of American cities discussed earlier in chapter two. Commercial and business activities are concentrated in the Central Business District where jobs, office buildings and especially stores are located as observed in the classical models. However, observe business activities involving petty traders that are found along the streets of the Central Business District. Petty traders activities found along the streets of the Central Business District in the study area is in variance with one of the characteristics of multiple nuclei model that state that „certain activities unable to generate enough income to pay for high rent in particular location are forced to locate at the site with low rents’ Batty (2001) acknowledged that the internal structure of cities prior to industrial age was largely determined by economic exchange and social interaction based on walking. Even today, the configuration analysis of old cities and older part of modern cities requires some basic appreciation of the limits on scale posed by movement pattern that were largely conditioned by how far people could walk. 7.3 IMPLICATION OF FINDINGS TO PLANNING Planning in any environment should be problem oriented. This is to say that for any planning process to be successful, it must address itself to providing immediate and long term solution to various problems of the city. It is in this regard that a strong methodology becomes a prerequisite for comprehensive planning (Ayeni, 1974). Many pedestrian-related plans, programs, projects and policies in cities evolved over time, generally in response to very specific objectives and often singular divisional needs. Analysing the implication of the findings of this study for planning requires inventory of all existing policies, programs and projects in Lagos State and the country in general in order to integrate them and to identify gaps to be addressed by new initiatives. 193 UNIVERSITY OF IBADAN LIBRARY In Lagos state where the study area is located for example; there is no transport policy that discusses the exclusive right of pedestrian in roadways. However, there are pedestrian overpasses constructed on highways in some part of the state. In Nigeria for example, the National Transport Policy, Federal Ministry of Transport main document of May 1993 only discussed other modes of transport without including walking as a mode of transport or discussed pedestrians in details. This showed that the rights of pedestrians are not incorporated in Nigerian transport policy and it is not a surprise why the results of the study showed the neglect of this mode of transport. In the study, it was observed that there are no walkways in many road networks in the city understudy and where they are available in form of covered drainage, people have complained that vehicles are parked on the walkways and this usually constitute nuisance and impediment to their mobility. They believed that other road users (drivers, motorcyclists, tricyclists) demonstrate a lack of concern about them as potential users of the transport system. The study also revealed that greater numbers of people do walk frequently and their walking distances to bus stops or transit points and landuse and places of economic activities lies between 0.244km and 2km. The study further revealed that factors related to safety, security, cleanliness, continuity, distance, time, weather, accessibility, aesthetic, speed of vehicles, heavy traffic do influence people‟s willingness to walk. Recognizing these factors there is the need to modify land use patterns in order to make urban environments less car dependent and more oriented towards walking, cycling and public transport. These efforts include the new urbanism, compact city, smart growth, transit-oriented development and urban villages‟ movements, all of which promote some combination of concentrated development, interconnected streets, mixed land uses and proximity to public transport nodes. These approaches help to make walking a more viable travel option by decreasing distances between the origins and destinations of many trips (Southworth, 1997; Southworth and Ben- Joseph, 2003; Thompson-Fawcett and Bond, 2003). The integration of land use and transport planning is a key element in creating more sustainable cities. 194 UNIVERSITY OF IBADAN LIBRARY Urban planning and land organization must be directed towards preserving and providing more foot space and proximity of work places and services should be improved. Public spaces must be made liveable with attractive and comfortable furniture, trees, devices for weather protection, benches, kiosks etc. In short term planning, essential aspect of pedestrian program for sustainable development will be to organize landuse and design the city space so that walking is promoted such that: (i) Walkways and pedestrian areas are continuously connected, comfortable, and free from hazards and risks, as well as free from air pollution. (ii) Pedestrian areas are made interesting and attractive by varying the environment, places where people can meet on foot and enjoy the city atmosphere. (iii) Safe and comfortable walkways should be offered to efficient, environment- friendly and affordable public transport service should be offered. (iv) Street furniture should be available to disabled persons, e.g. seats for resting. (v) Information should be given at key points in an easily understandable way. (vi) Walkways and streets are maintained and well illuminated for safe and secure walking. In the long run, formulating policies for sustainable development, environmental improvement, public health, urban and traffic renewal is significant. The goal is to balance the use of urban space and reduction of automobile dependency and encourage safe and comfortable walking environment. Promotion of urban traffic planning; road construction and management and public transport services that can enhance pedestrian-friendly city. City design and towns landscaping must take into account the city‟s historical background. Architects must 195 UNIVERSITY OF IBADAN LIBRARY form attractive and interesting walkways and public spaces that are full of variety of trees, arcades and provision and maintenance of adequate lighting in public areas. Indoor walkways should offer weather and climate protection and visible landmarks are necessary for effective orientation. Urban planning and land use organization: Organization of a variety of functions, all over the city, by obtain proximity to work and service places Building arranged to avoid twist tunnels. Pedestrian zones are becoming more and more popular although some traders are still worried about loss of trade. However, the experiences from cities that have pedestrianized large areas of their city centres are generally been very positive (Zuckerman, 1993). Traffic planning and transport management: Urban form adapted to efficient public transport system and traffic reduction is required. Increase speed management and number of car-free zones must be achieved. These zones need to be planned with great care of preventing them from becoming isolated islands in a sea of cars. Speed reduction walkable communities support public transit because most transit trips begin and end in a walking trip. The most vibrant, economically successful commercial streets depend on high levels of foot traffic. Cities which have dynamic, interesting walking environments attract millions of visitors every year. To compete globally, there is the need to invest in becoming a great walking city. Information technology can be used to compel drivers to lower their speed to the pedestrian conditions. It could also detect pedestrians at signalized crossings or give pre-warnings. Police supervision directed to the enforcement of speed limits and control of other driver behaviour as well as protecting of people from criminal action along walkways is necessary. Cities built for people rather than cars can be more aesthetically pleasing to the eye. There is the need to put aesthetics high on the agenda when planning a walkable environment because aesthetic environments enhance the experience of walking and time becomes less of an issue. Unfortunately the experience of walking for many people is taking themselves from the underground parking garage to the nearest shop 196 UNIVERSITY OF IBADAN LIBRARY which is not particularly rewarding when it comes to aesthetic and will certainly not give a taste for more (Gunnarsson, 1995). The result from the study showed that on-street persons walk more distances than the household respondents. This situation may be attributed to the fact that household respondents take pleasure in the comfort of their vehicles, which signifies that household respondents are likely to bring their vehicles as close as possible to activity centres. The implication is that where there are no parking facilities at these centres, as observed in the study, people may continue to park along the streets or walkways meant for pedestrians thereby not only creating traffic congestion along the streets but also expose pedestrians to danger. Where there is enforcement regarding on-street parking, this may result to underutilisation of such facilities. Walking distances that are longer than acceptable limit, may not create avenue for an efficient utilization of public transport facilities and services. Where residential or commuter population or job density is great, short walking distances to public transport pick-up points or bus stops must be widely spaced because when bus stops are too close, to each other, the journey time lengthens. Long walking distances could only be acceptable if public transport bus stops are easily accessible, that is, if they are located at sites where passengers do not have to cross heavy traffic before getting to the bus stops. Longer walking distances could also be tolerated if the bus stops are made attractive by provision of such facilities as walkways, pedestrian crossings, sheltered seats, shopping facilities and so on. In planning of housing estates or any other major urban expansion schemes, attention should be paid to maximum distance people are ready to walk. Based on the results of the study, average walking distance between 300m and 900m to public transport pick-up points may be considered for journey from home to bus stations and local facilities in the study area. Also, an average walkable maximum distance between 2.1km and 3km from home to various economic and landuse activities may be considered in and around the study area in Ikeja. 197 UNIVERSITY OF IBADAN LIBRARY People use their cars for a variety of different purposes. Measures aiming to assist reduction in private car use will need to be carefully targeted at these purposes, providing viable alternatives to meeting those individual patterns of obligation. Urban and transport planners, architects and engineers must cooperate in order to achieve an attractive environment for pedestrians and all citizens, and they must strive to bring back proximity of work and service places. Attention must also be given to methods of reducing the increasing dependency on private cars. Multidisciplinary research will be necessary to increase knowledge of the behaviour of pedestrians and to promote walking as an important way to enjoy city life and keep healthy. Designing a pedestrian-friendly city will be great challenge in a program for developing sustainable cities. Walking freely and safely must be regarded as a human right. 7.4 FURTHER RESEARCH NEEDS There is no doubt that urban travel and particularly pedestrian trips, is a function of complex socio-economic characteristics of urban centre and urban residents on one hand, and pedestrian trips with respect to the nature of roadside walking environment and pedestrian needs on the other hand. The possibility of making generalisation on the basis of the results of this has some degree of limitations, since there is always room for improvements. Because of the difficulties in capturing all variables required for the explanation of pedestrian trips in this study. The conclusions reached for the study are based on data used. Pedestrians can be divided into many types depending on age, physical and mental capacity, type of equipment used, occupation of hands, and group size. These characteristics influence whether one can walk freely and comfortably without strain and hindrance. Persons with disabilities have special problems that vary depending on the type of handicap, e.g. moving quickly, walking longer distances, using steps, reading traffic signs and other information, avoiding obstacles, and hearing approaching cars (Gunnarsson, 1995). . 198 UNIVERSITY OF IBADAN LIBRARY Mentally disabled people have difficulty in orienting themselves, perceiving risks, and understanding signs and traffic information. Smaller children may also be handicapped to some extent, and Sandels (1968) has clearly demonstrated that most of the children up to an age of 8 to 9 years are unable to understand traffic rules, and they may not yet know the difference between right and left. Another group affected by traffic emissions is allergic and asthmatic persons, who get breathing problems and eye irritations. In this study, pedestrians are not separated into different group. They are generalized on the definition of Association of Pedestrian Council (2001), as those who travel on foot and Risser (2003) as transport mode and only when it is done in public place where those who travel by foot interact with other people as road user. This study therefore, proposed that pedestrian trips based on categories of pedestrian as highlighted in table 7.1 is an area for further research. 199 UNIVERSITY OF IBADAN LIBRARY Table 7.1: Classification of Pedestrian Types. Classification Pedestrian Type Age Toddlers School children, Teenagers Adults and Elderly Capacity Full capacity Type of handicap Handicapped (physically, mentally) Crutch, cane or walker Equipment used Wheelchair or rollator Roller-skates, skateboards Hands free Occupation of hands Guiding children, disabled persons Carrying toys, shopping or luggage Pushing a pram, shopping trolley Walking an animal. Group size Single A couple or a family Procession, parade Source: Gunnarsson 1995 200 UNIVERSITY OF IBADAN LIBRARY There are studies that argued that the form, that is the structure and the shape of the urban environment, can have impact on the decision to walk (Hass-Klau., Dowland, and Nold, 1994; Living Streets, 2001). The variables of urban form are not considered in the study and this is an avenue for inclusion of urban form variables into successive study of pedestrian trips. The result of pedestrian preferred safety in Ikeja showed that variables (such as lateral separation, motor vehicle volume, speed of motor vehicle, and drive way access and volume) contributed 58.1% explanation to pedestrian level of safety along walking environment in the study area. Variance of 41.9% - unexplained variation showed that there are other variables that contribute to pedestrian preferred safety in the study area. Therefore, this study suggests that variables such as quality of walkway, noise, air pollution - emission from moving vehicle and others can be included into future research in the area of preferred pedestrian safety. Behavioural change strategies have worked in many fields of study regarding traffic problem and the problems of reducing car use and advocating walking. A study of people‟s attitude and change in behaviour in the use of car for short distance trips instead of walking is another viable area for further research in pedestrian trips. 7.5 CONCLUSION As the most basic form of mobility, walking has become increasingly marginalised in many cities as traffic congestion and automobile-oriented design have driven walkers from the streets (Frank, Andresen and Schmid, .2004; Giles-Corti and Donovan, 2003; Saelens, Sallis and Frank, 2003). This situation exacerbates reliance on the private motor vehicle for everyday activities. Yet there is growing concern about the unsustainability of urban environments and a related acknowledgement of the need to reduce vehicle dependence and encourage active travel, which most obviously encompasses walking (Bean, Kearns and Collins, 2008). Various social problems are attributed to increased automobile dependency in cities, including social exclusion for those without access to a car, and a loss of 201 UNIVERSITY OF IBADAN LIBRARY community and street life (Hine and Mitchell, 2003; Sheller, 2004; Sheller and Urry, 2000; Southworth, 2003; Bean, Kearns and Collins, 2008). These problems are accompanied by public health concerns such as automobile accidents (Feyer and Langley, 2000; Tobias and Turley, 2005) and the disease burden associated with declining physical activity and increasing obesity (Mackett, Lucas, Paskins and Turbin, 2005). The increasing motor traffic in cities negatively affects the safety and environment for pedestrians. About 15-20% of persons killed in road traffic accidents in industrialized countries are pedestrians; this figure is 40-50% in developing countries. Another problem of pedestrians is that they have to walk in the roadway due to lack of walkways and that more walking space has been given to motor vehicles through wider streets and more space for parking, even on sidewalks. They also have to walk in mud and water when maintenance is inadequate, and there is the danger of falling into holes and pits, as well as the difficulties of walking on slopes and steps Information about the shortest route to popular destinations or points of interests is often lacking, pedestrians often have to make detours to avoid obstacles or parked cars and when they have to pass a motorway or a heavily loaded road. Heavy traffic on a street hinders and impedes social contacts. Although, Bean et al (2008) observed that the auto mobility literature emphasises the emergence of new sets of social activities and expectations in cities which are associated with growing dependence on the automobile. Yet, pedestrians have not disappeared from city streets and particular forms of sociality continue to be associated with the act of walking. Problems of pedestrians must not be regarded as a serious traffic safety problem only, but also a question of well-being, health and security. Walking will play a greater role in the coming years both as a means of transport and as a way to experience city life. Efforts have to be made to design a safer and more comfortable environment for pedestrians by establishing more car-free areas and zones where speed, volume and type of vehicle are closely adapted to the conditions of pedestrians 202 UNIVERSITY OF IBADAN LIBRARY The city has historically been devoted to pedestrians. Space for pedestrians needs not only to be defended but also extended and developed for the benefit of this very important mode. The rights of the pedestrians as adopted by the European Parliament (1988) are that: (i) The pedestrian has the right to live in a healthy environment and freely enjoy the amenities offered by public areas under conditions that adequately safeguard his physical and psychological well-being. (ii) The pedestrian has the right to live in urban or village centres tailored to the needs of human beings and not the needs of the motor car, and to have amenities within walking or cycling distance. (iii) Children, the elderly and the disabled have the right to expect towns to be places of each social contact and not places that aggravate their inherent weakness. (iv) The disabled have the right to specific measures to maximize their independent mobility, including adjustments in public areas, transport systems and public transport (guidelines, warning signs, acoustic signals, accessible buses, trams and trains). (v) The pedestrian has the right to urban areas which are intended exclusively for his use, are as extensive as possible and are not mere “pedestrian precincts” but are in harmony with the overall organization of the town, and also the exclusive right to connecting short, logical and safe routes. There was a limited distance over which household heads can walk to facilities making them drive close to such facilities thereby, creating congestion and parking problems. Walking is the most ancient and universal form of travel. It is the first kind of travel we learn and the one that is most accessible to all. Every journey begins and ends on foot. Therefore, educating and advocating should be used to promote walking as a healthy means of transport, and also as a way to avoid shorter trips by car. Increased investment on pedestrian facilities and pedestrianizing central business districts of urban centres will enhance pedestrians‟ mobility and safety. 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Wiley, New York. 220 UNIVERSITY OF IBADAN LIBRARY APPENDIX I HOUSEHOLD TRIP DIARY IN IKEJA AREA OF LAGOS Department of Geography University of Ibadan, Ibadan For Official Use Only Enumerator: ……………………. House No: ……………………… Zone No: ………………………. Street Name: …………………… Date: …………………………… Dear Sir/Ma, DATA COLLECTION FOR DOCTORAL THESIS This survey is for a Doctoral thesis. It has nothing to do with tax collection, property assessment or reassessment, increase in fuel pump price, transport or any other levy whatsoever. The information provided here will be used only for academic purpose and strictly kept confidential. HOUSEHOLD MOVEMENT RECORD IN AND AROUND IKEJA 1. Location of Respondent Street: ……………………………… Area ……………… Zone: ……………….. SECTION A: HOUSEHOLD COMPOSITION AND SOCIO-ECONOMIC CHARACTERISTICS Note: This information is meant for the household head. A household is the number of people who live in a house including the head. 1. Age of Respondent . (Less than 20) yr -- 0 (20 – 30) yrs – 1 (31 – 40) yrs – 2 (41 – 50) yrs – 3 (51 – 60) yrs – 4 (60 and above) yrs - 5 2. Sex of Respondent Female – 0 Male – 1 3. Marital Status of Respondent 221 UNIVERSITY OF IBADAN LIBRARY Single – 0 Married – 1 Divorced – 2 Widowed- 3 4. Level of Education of Respondent Primary – 1 Secondary- 2 Post Secondary – 3 5. Work Status of Respondent Not working – 0 Working - 1 6. Employment status of Respondent Student – 0 Public sectors – 1 Private sector – 2 Self-employed – 3 Retired – 4 Unemployed – 5 7. Occupational Status of Respondent Self employed – 1 Public sectors – 1 Private sector – 2 Student – 3 Retired – 4 Unemployed – 5 8. Occupation category of Respondent Farming /agriculture – 1 Industrial/Manufacturing – 2 Commerce/Trading – 3 Administration – 4 Construction – 5 Teaching/Lecturing – 6 Schooling – 7 Others (specify) – 8……………………………………………………………………. 9. Religion Affiliation of Respondent Muslim – 1 Christian – 2 Traditional – 3 10. Are you a native of Ikeja? Yes - 1 No – 0 11. How long have you being staying in Ikeja. 1 – 5 years – 0 6 – 10 years -1 11 – 15 years -3 16 – 20 years – 4 Above 20 years -5 12a. Where is your work Location within Ikeja? Central Business District – 1 Industrial Layout – 2 Commercial Layout – 3 Shopping Malls – 4 Secretariat – 5 Residential Layout – 6 Others - 7 12b. Where is your work location outside Ikeja? 222 UNIVERSITY OF IBADAN LIBRARY Central Business District – 1 Industrial Layout – 2 Commercial Layout – 3 Shopping Malls – 4 Secretariat – 5 Residential Layout – 6 Others - 7 13. What is your monthly income? Less than N7, 500 – 1 7,500 – 20,000 – 2 21,000 – 30,000 – 3 31,000 -- 40,000 – 4 41,000- 50,000 – 5 Above N50, 000 – 6 14. What is your estimated annual income in naira? Less than N90, 000 – 1 N90,000 – 2 Greater than N90, 000 – 3 15. What is your estimated annual rent paid in Naira. Less than N78, 000 – 1 N78, 000 – 2 Greater than N78, 000 – 3 16. Do you have a vehicle? Yes - 1 No - 0 17a. If no to 16, indicate your household usual mode of inter-city travel. Private owned car – 1 Official vehicle – 2 Public transport – 3 Motor cycle/bicycle - 4 Walking – 5 Others (specify)………………… 6 17b. If no to 16, indicate your household usual mode of intra -city travel. Private owned car – 1 Official vehicle – 2 Public transport – 3 Motor cycle/bicycle - 4 Walking – 5 Others (specify)………………… 18. If yes, how many vehicles are available for use by your household? Less than two - 1 Two vehicles - 2 Three vehicles - 3 Four vehicles – 4 Five or more vehicles - 5 19. How many people live in your apartment? One – 0 Two – 1 Three -2 Four – 3 Five -4 More than 5--- 5 20. How many of those living with you are adult? One – 1 Two – 2 Three -3 Four – 4 Five -5 More than 5--- 6 21. How many of those living with you can drive? One – 0 Two – 1 Three -2 Four – 3 Five -4 More than 5--- 5 223 UNIVERSITY OF IBADAN LIBRARY 22. How many of those living with you are adult driver? One – 0 Two – 1 Three -2 Four – 3 Five -4 More than 5--- 5 23. How many drivers do you employ? One – 0 Two – 1 Three -2 Four – 3 Five -4 More than 5--- 5 SECTIONB: ABOUT ORIGIN, DESTINATION AND PURPOSE OF TRIPS In this section we are asking you to provide information about the journey made last week as pedestrian in and around Ikeja. JOURNEY 25. Where did you start your daily journey? (Origin of trip) Home –1 Work --2 Shop --3 Recreation -- 4 Mosques 5 Business--6 Exercising --7 School –8 Visiting Friends -9 Social function –10 Days of the Origin of Trips as Routine Week Home Work Shopping Recreation Church/Mosque Take Exercising Save Social Transit Money Function Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday 26. Where was your daily destination? (Destination of trip) Home –1 Work --2 Shop --3 Recreation -- 4 Mosques 5 Business--6 Exercising --7 School –8 Visiting Friends -9 Social function –10 224 UNIVERSITY OF IBADAN LIBRARY Days of the Destination of Trips as Routine Week Home Work Shopping Recreation Church/Mosque Take Exercising Save Social Transit Money Function Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday 27. What is the purpose of your daily trips? Home –1 Work --2 Shop --3 Recreation -- 4 Mosques 5 Business--6 Exercising --7 School –8 Visiting Friends -9 Social function –10 Days of the Purpose of Trips as Routine Week Home Work Shopping Recreation Church/Mosque Take Exercising Save Social Transit Money Function Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday 28. Do you make these daily trips weekly? Yes –1 No --2 29. If yes to 28, how many times did you make these daily trips in a week? Once in a week –1 2times in a week –2 3times in a week –3 4 times in a week 5 times in a week –5 More than five times in a week 30. What combination of modes of transport did you use for your daily trips from origin to destination? Walk- Public Transport-Walk 1 Walk- Official Vehicle –Walk 2 225 UNIVERSITY OF IBADAN LIBRARY Walk- Private Vehicle –Walk 3 Walk- Motor cycle –Walk 4 Motor cycle –Motor cycle- Walk 5 Walk- Walk –Walk 6 Private Vehicle- Private Vehicle-Walk 7 Official Vehicle- Official Vehicle-Walk 8 Walk- Public Transport-Train-Walk 9 31. Where did you start your daily trips as Pedestrian? Home –1 Work --2 Shop --3 Recreation -- 4 Mosques 5 Business--6 Exercising --7 School –8 Visiting Friends -9 Social function –10 Days of the Origin of Trips as Pedestrian Week Home Work Shopping Recreation Church/Mosque Take Exercising Save Social Transit Money Function Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday 32. What is the purpose of your daily trips as pedestrian? Home –1 Work --2 Shop --3 Recreation -- 4 Mosques 5 Business--6 Exercising --7 School –8 Visiting Friends -9 Social function –10 Days of the Purpose of Trips as Pedestrian Week Home Work Shopping Recreation Church/Mosque Take Exercising Save Social Transit Money Function Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday 226 UNIVERSITY OF IBADAN LIBRARY 33. Are their pedestrian facilities to use when embarking on trips as pedestrian? Yes -1 No -2 34 . What type of pedestrian facilities is available for use along this street? Sidewalk/Pavement -1 Street crossing -2 Zebra Crossing -3 Lighting signal -4 Pedestrian bridges -5 None - 4 35. What is the nature of side walk along this street (if any)? + There is no sidewalk + There are sidewalks, but they are not continuous + Sidewalks are broken, cracked making then unsafe and difficult to walk + There is not enough room for two people to walk side by side +Sidewalks do not have ramps, curb cut, for wheel chairs, and strollers +Car, Trucks are blocking the sidewalks + Others, please specify Overall rating of sidewalks. (a) Excellent (b) Good (c) Fair (d) Poor 36. What is the nature of Street crossings along this street (if any)?? +Roads are too wide to cross safely +Need traffic signal +Traffic signal makes pedestrians wait too long before crossing +Need pedestrian crossing signal/audible signal. +There are zebra crossings +Motorists do not stop at the zebra crossing making it unsafe for pedestrians Overall rating of Street crossings. (a) Excellent (b) Good (c) Fair (d) Poor 37. What are the natures of available pedestrian facilities in and around Ikeja that may or discourage your usage or walkability? Nature of Pedestrian Walkways/Available Characteristics Pedestrian Facilities Traffic Situation Not Congested- -0 Congested – 1 Safety Not Safe - 0 Safe – 1 Security Not Secure - 0 Secure - 1 Convenience Not Convenient - 0 Convenient - 1 Continuity Not Continue - 0 Continue - 1 Cleanliness Not Clean - 0 Clean - 1 Cohesiveness Not Connected - 0 Connected - 1 Spacious Not Spacious - 0 Spacious – 1 227 UNIVERSITY OF IBADAN LIBRARY Distance Trip length  3.2km - 0 Trip length  3.2km - 1 OTHERS Characteristics Time Of The Day Night/Darkness – 0 Daylight - 1 Season Dry Season - 0 Rainy Season - 1 Weather Not Sunny - 0 Sunny - 1 38. What is your walking distance from to the bus stops or bus stations ? (0 - 100) m -1 (100 - 200) m -2 (200 - 300) m -3 (300 - 400) m -4 (400 - 500) m -5 (Above 500) m -6 39. What is the distance you are likely to walk or trek to Land use and places of economic activities in your area? (0 - 1) km -1 (1 - 2) km -2 (2 - 3) km -3 (3 - 4) km -4 (Above 4) km -5 40. Where do you usually end your daily journey as pedestrian? Home –1 Work --2 Shop --3 Recreation -- 4 Mosques 5 Business--6 Exercising --7 School –8 Visiting Friends -9 Social function –10 Days of the Destination of Trips as Pedestrian Week Home Work Shopping Recreation Church/Mosque Take Exercising Save Social Transit Money Function Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday 41. How do you see walking? Necessity -1 Hobby -2 Exercise -3 Routine -4 Improve health -5 Alternative mode of transport -6 228 UNIVERSITY OF IBADAN LIBRARY 42. From a pair wise comparison matrix below, use a 1–9 scale, with: aij  1 if the two factors are equal in importance; aij  3 if Oi is weakly more important than O j ; aij  5 if Oi is strongly more important than O j ; aij  7 if Oi is very strongly more important than O j ; aij  9 if Oi is absolutely more important than O j ; aij  1/3 if O j is weakly more important than Oi ; aij  1/5 ifO j is very strongly more important than Oi ; aij  1/9 if O j is absolutely more important than Oi in your decision to walk. Where the number in the ith row and jth column gives the relative importance of Oi Oi as compared withO j . O FACTORS i O j Traffic Situation Safety Security Continuity Cleanliness Spacious Traffic Situation Safety Security Continuity Cleanliness Spacious 43. What other factors can you consider as important in discouraging you to walk? 1. ………………………………………………………………………………………………………... 2. ………………………………………………………………………………………………………... 3. ………………………………………………………………………………………………………... 4. ………………………………………………………………………………………………………... 5. ………………………………………………………………………………………………………… 6. ………………………………………………………………………………………………………… 7. ………………………………………………………………………………………………………… 8. ………………………………………………………………………………………………………… 229 UNIVERSITY OF IBADAN LIBRARY 44. Give any other information relating to Pedestrian activities along this street. …………………………………………………………………………………………………………….. …………………………………………………………………………………………………………… …………………………………………………………………………………………………………….. …………………………………………………………………………………………………………….. 45. Suggest measures to improving Pedestrian movement in this urban centre …………………………………………………………………………………………………………… …………………………………………………………………………………………………………… …………………………………………………………………………………………………………… …………………………………………………………………………………………………………… …………………………………………………………………………………………………………… …………………………………………………………………………………………………………… …………………………………………………………………………………………………………… 46. Suggest measures to improving transportation system in this urban centre. …………………………………………………………………………………………………………… ……………………………………………………………………………………...................................... …………………………………………………………………………………………………………….. …………………………………………………………………………………………………………… …………………………………………………………………………………………………………… …………………………………………………………………………………………………………… ……………………………………………………………………………………………………………. 230 UNIVERSITY OF IBADAN LIBRARY APPENDIX II ON-STREET PERSONS’ TRIP DIARY IN IKEJA AREA OF LAGOS HOUSEHOLD TRIP Department of Geography University of Ibadan, Ibadan For Official Use Only Enumerator: ……………………. House No: ……………………… Zone No: ………………………. Street Name: …………………… Date: …………………………… Dear Sir/Ma, DATA COLLECTION FOR DOCTORAL THESIS This survey is for a Doctoral thesis. It has nothing to do with tax collection, property assessment or reassessment, increase in fuel pump price, transport or any other levy whatsoever. The information provided here will be used only for academic purpose and strictly kept confidential. NON-HOUSEHOLD MOVEMENT RECORD IN AND AROUND IKEJA 1. Location of Respondent Street: ……………………………… Area ……………… Zone: ……………….. SECTION A: HOUSEHOLD COMPOSITION AND SOCIO-ECONOMIC CHARACTERISTICS Note: This information is meant for the household head. A household is the number of people who live in a house including the head. 1. Age of Respondent . (Less than 20) yr -- 0 (20 – 30) yrs – 1 (31 – 40) yrs – 2 (41 – 50) yrs – 3 (51 – 60) yrs – 4 (60 and above) yrs - 5 2. Sex of Respondent Female – 0 Male – 1 3. Marital Status of Respondent Single – 0 Married – 1 Divorced – 2 Widowed- 3 231 UNIVERSITY OF IBADAN LIBRARY 4. Level of Education of Respondent Primary – 1 Secondary- 2 Post Secondary – 3 5. Work Status of Respondent Not working – 0 Working - 1 6. Employment status of Respondent Student – 0 Public sectors – 1 Private sector – 2 Self-employed – 3 Retired – 4 Unemployed – 5 7. Occupational Status of Respondent Self employed – 1 Public sectors – 1 Private sector – 2 Student – 3 Retired – 4 Unemployed – 5 8. Occupation category of Respondent Farming /agriculture – 1 Industrial/Manufacturing – 2 Commerce/Trading – 3 Administration – 4 Construction – 5 Teaching/Lecturing – 6 Schooling – 7 Others (specify) – 8……………………………………………………………………. 9. Religion Affiliation of Respondent Muslim – 1 Christian – 2 Traditional – 3 10. Are you a native of Ikeja? Yes - 1 No – 0 11. How long have you being staying in Ikeja. 1 – 5 years – 0 6 – 10 years -1 11 – 15 years -3 16 – 20 years – 4 Above 20 years -5 12a. Where is your work Location within Ikeja? Central Business District – 1 Industrial Layout – 2 Commercial Layout – 3 Shopping Malls – 4 Secretariat – 5 Residential Layout – 6 Others - 7 12b. Where is your work location outside Ikeja? Central Business District – 1 Industrial Layout – 2 Commercial Layout – 3 232 UNIVERSITY OF IBADAN LIBRARY Shopping Malls – 4 Secretariat – 5 Residential Layout – 6 Others - 7 13. What is your monthly income? Less than N7, 500 – 1 7,500 – 20,000 – 2 21,000 – 30,000 – 3 31,000 -- 40,000 – 4 41,000- 50,000 – 5 Above N50, 000 – 6 14. What is your estimated annual income in naira? Less than N90, 000 – 1 N90,000 – 2 Greater than N90, 000 – 3 15. What is your estimated annual rent paid in Naira. Less than N78, 000 – 1 N78, 000 – 2 Greater than N78, 000 – 3 16. Do you have a vehicle? Yes - 1 No - 0 17a. If no to 16, indicate your household usual mode of inter-city travel. Private owned car – 1 Official vehicle – 2 Public transport – 3 Motor cycle/bicycle - 4 Walking – 5 Others (specify)………………… 6 17b. If no to 16, indicate your household usual mode of intra -city travel. Private owned car – 1 Official vehicle – 2 Public transport – 3 Motor cycle/bicycle - 4 Walking – 5 Others (specify)………………… 18. If yes, how many vehicles are available for use by your household? Less than two - 1 Two vehicles - 2 Three vehicles - 3 Four vehicles – 4 Five or more vehicles - 5 19. How many people live in your apartment? One – 0 Two – 1 Three -2 Four – 3 Five -4 More than 5--- 5 20. How many of those living with you are adult? One – 1 Two – 2 Three -3 Four – 4 Five -5 More than 5--- 6 21. How many of those living with you can drive? One – 0 Two – 1 Three -2 Four – 3 Five -4 More than 5--- 5 233 UNIVERSITY OF IBADAN LIBRARY 22. How many of those living with you are adult driver? One – 0 Two – 1 Three -2 Four – 3 Five -4 More than 5--- 5 23. How many drivers do you employ? One – 0 Two – 1 Three -2 Four – 3 Five -4 More than 5--- 5 SECTIONB: ABOUT ORIGIN, DESTINATION AND PURPOSE OF TRIPS In this section we are asking you to provide information about the journey made last week as pedestrian in and around Ikeja. JOURNEY 25. Where did you start your daily journey? (Origin of trip) Home –1 Work --2 Shop --3 Recreation -- 4 Mosques 5 Business--6 Exercising --7 School –8 Visiting Friends -9 Social function –10 Days of the Origin of Trips as Routine Week Home Work Shopping Recreation Church/Mosque Take Exercising Save Social Transit Money Function Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday 26. Where was your daily destination? (Destination of trip) Home –1 Work --2 Shop --3 Recreation -- 4 Mosques 5 Business--6 Exercising --7 School –8 Visiting Friends -9 Social function –10 234 UNIVERSITY OF IBADAN LIBRARY Days of the Destination of Trips as Routine Week Home Work Shopping Recreation Church/Mosque Take Exercising Save Social Transit Money Function Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday 27. What is the purpose of your daily trips? Home –1 Work --2 Shop --3 Recreation -- 4 Mosques 5 Business--6 Exercising --7 School –8 Visiting Friends -9 Social function –10 Days of the Purpose of Trips as Routine Week Home Work Shopping Recreation Church/Mosque Take Exercising Save Social Transit Money Function Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday 28. Do you make these daily trips weekly? Yes –1 No --2 29. If yes to 28, how many times did you make these daily trips in a week? Once in a week –1 2times in a week –2 3times in a week –3 4 times in a week 5 times in a week –5 More than five times in a week 30. What combination of modes of transport did you use for your daily trips from origin to destination? Walk- Public Transport-Walk 1 Walk- Official Vehicle –Walk 2 Walk- Private Vehicle –Walk 3 Walk- Motor cycle –Walk 4 235 UNIVERSITY OF IBADAN LIBRARY Motor cycle –Motor cycle- Walk 5 Walk- Walk –Walk 6 Private Vehicle- Private Vehicle-Walk 7 Official Vehicle- Official Vehicle-Walk 8 Walk- Public Transport-Train-Walk 9 32. Where did you start your daily trips as Pedestrian? Home –1 Work --2 Shop --3 Recreation -- 4 Mosques 5 Business--6 Exercising --7 School –8 Visiting Friends -9 Social function –10 Days of the Origin of Trips as Pedestrian Week Home Work Shopping Recreation Church/Mosque Take Exercising Save Social Transit Money Function Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday 32. What is the purpose of your daily trips as pedestrian? Home –1 Work --2 Shop --3 Recreation -- 4 Mosques 5 Business--6 Exercising --7 School –8 Visiting Friends -9 Social function –10 Days of the Purpose of Trips as Pedestrian Week Home Work Shopping Recreation Church/Mosque Take Exercising Save Social Transit Money Function Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday 236 UNIVERSITY OF IBADAN LIBRARY 33. Are their pedestrian facilities to use when embarking on trips as pedestrian? Yes -1 No -2 34 . What type of pedestrian facilities is available for use along this street? Sidewalk/Pavement -1 Street crossing -2 Zebra Crossing -3 Lighting signal -4 Pedestrian bridges -5 None - 4 35. What is the nature of side walk along this street (if any)? + There is no sidewalk + There are sidewalks, but they are not continuous + Sidewalks are broken, cracked making then unsafe and difficult to walk + There is not enough room for two people to walk side by side +Sidewalks do not have ramps, curb cut, for wheel chairs, and strollers +Car, Trucks are blocking the sidewalks + Others, please specify Overall rating of sidewalks. (a) Excellent (b) Good (c) Fair (d) Poor 36. What is the nature of Street crossings along this street (if any)?? +Roads are too wide to cross safely +Need traffic signal +Traffic signal makes pedestrians wait too long before crossing +Need pedestrian crossing signal/audible signal. +There are zebra crossings +Motorists do not stop at the zebra crossing making it unsafe for pedestrians Overall rating of Street crossings. (a) Excellent (b) Good (c) Fair (d) Poor 37. What are the natures of available pedestrian facilities in and around Ikeja that may or discourage your usage or walkability? Nature of Pedestrian Walkways/Available Characteristics Pedestrian Facilities Traffic Situation Not Congested- -0 Congested – 1 Safety Not Safe - 0 Safe – 1 Security Not Secure - 0 Secure - 1 Convenience Not Convenient - 0 Convenient - 1 Continuity Not Continue - 0 Continue - 1 Cleanliness Not Clean - 0 Clean - 1 Cohesiveness Not Connected - 0 Connected - 1 Spacious Not Spacious - 0 Spacious – 1 237 UNIVERSITY OF IBADAN LIBRARY Distance Trip length  3.2km - 0 Trip length  3.2km - 1 OTHERS Characteristics Time Of The Day Night/Darkness – 0 Daylight - 1 Season Dry Season - 0 Rainy Season - 1 Weather Not Sunny - 0 Sunny - 1 38. What is your walking distance from to the bus stops or bus stations ? (0 - 100) m -1 (100 - 200) m -2 (200 - 300) m -3 (300 - 400) m -4 (400 - 500) m -5 (Above 500) m -6 39. What is the distance you are likely to walk or trek to Land use and places of economic activities in your area? (0 - 1) km -1 (1 - 2) km -2 (2 - 3) km -3 (3 - 4) km -4 (Above 4) km -5 40. Where do you usually end your daily journey as pedestrian? Home –1 Work --2 Shop --3 Recreation -- 4 Mosques 5 Business--6 Exercising --7 School –8 Visiting Friends -9 Social function –10 Days of the Destination of Trips as Pedestrian Week Home Work Shopping Recreation Church/Mosque Take Exercising Save Social Transit Money Function Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday 41. How do you see walking? Necessity -1 Hobby -2 Exercise -3 Routine -4 Improve health -5 Alternative mode of transport -6 238 UNIVERSITY OF IBADAN LIBRARY 42. From a pair wise comparison matrix below, use a 1–9 scale, with: aij  1 if the two factors are equal in importance; aij  3 if Oi is weakly more important than O j ; aij  5 if Oi is strongly more important than O j ; aij  7 if Oi is very strongly more important than O j ; aij  9 if Oi is absolutely more important than O j ; aij  1/3 if O j is weakly more important than Oi ; aij  1/5 ifO j is very strongly more important than Oi ; aij  1/9 if O j is absolutely more important than Oi in your decision to walk. Where the number in the ith row and jth column gives the relative importance of Oi Oi as compared withO j . O FACTORS i O j Traffic Situation Safety Security Continuity Cleanliness Spacious Traffic Situation Safety Security Continuity Cleanliness Spacious 43. What other factors can you consider as important in discouraging you to walk? 1. ………………………………………………………………………………………………………... 2. ………………………………………………………………………………………………………... 3. ………………………………………………………………………………………………………... 4. ………………………………………………………………………………………………………... 5. ………………………………………………………………………………………………………… 6. ………………………………………………………………………………………………………… 7. ………………………………………………………………………………………………………… 8. ………………………………………………………………………………………………………… 239 UNIVERSITY OF IBADAN LIBRARY 44. Give any other information relating to Pedestrian activities along this street. …………………………………………………………………………………………………………….. …………………………………………………………………………………………………………… …………………………………………………………………………………………………………….. …………………………………………………………………………………………………………….. 45. Suggest measures to improving Pedestrian movement in this urban centre …………………………………………………………………………………………………………… …………………………………………………………………………………………………………… …………………………………………………………………………………………………………… …………………………………………………………………………………………………………… …………………………………………………………………………………………………………… …………………………………………………………………………………………………………… …………………………………………………………………………………………………………… 46. Suggest measures to improving transportation system in this urban centre. …………………………………………………………………………………………………………… ……………………………………………………………………………………...................................... …………………………………………………………………………………………………………….. …………………………………………………………………………………………………………… …………………………………………………………………………………………………………… …………………………………………………………………………………………………………… ……………………………………………………………………………………………………………. 240 UNIVERSITY OF IBADAN LIBRARY APPENDIX III: Pedestrians Circulation on Road networks in the Study Area OTIGBA AREA – ZONE 1 Street Names 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm- 6pm - Total 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pm 7pm Awolowo 1123 2010 2098 1870 2070 2311 1467 1234 2641 3123 3143 2113 25203 Otigba 621 1712 2412 2212 2012 1121 1231 2010 2113 1121 2114 1213 19892 Ola-Ayeni 644 1113 2212 2141 2111 2112 1300 2123 2021 3013 2121 1303 22214 Oba Kodesoh 1050 1214 1321 800 712 862 463 481 1213 2867 3112 2162 16257 Oba Akron 1003 1211 2131 2115 831 713 612 812 1216 2814 3200 2113 18771 Adepele 160 703 268 1231 712 813 1731 1041 1021 2131 2111 1312 13234 Oshitelu 168 812 1011 1312 781 681 1121 1021 1211 2142 2131 2113 14504 Somoye Osundairo 130 600 914 1234 1478 1634 1211 1121 2412 2164 2314 2144 17356 Francis Oremeji 423 1211 1321 1102 1010 1121 1020 1121 1131 1234 2012 1031 13737 Pebble 203 800 1246 1132 621 463 631 812 1031 917 1811 832 10499 Simbiat Abiola 903 1413 2112 1231 1121 1132 1061 937 2012 2112 2134 2034 18202 Mobolaji Bank Anthony 1861 2106 1862 1781 1113 1214 1136 2131 2014 2113 2010 2014 21355 Idowu Lane 40 86 83 68 71 73 66 81 34 46 39 28 715 Adegbola 103 161 102 86 121 132 156 137 148 192 304 413 2055 Oduyemi 50 30 40 30 42 36 27 81 86 52 74 86 634 Akinremi 146 171 182 131 102 134 103 124 86 41 43 103 1366 Araromi 86 50 45 41 43 40 41 45 43 25 66 54 579 Oyelola 40 48 67 41 26 21 15 43 51 46 100 63 561 Shodipo 23 46 42 53 61 36 25 45 41 42 52 25 491 Omobitayo 53 56 81 47 31 32 23 41 36 48 64 73 585 Olawaiye 23 36 35 41 48 53 60 43 48 51 54 61 553 Babatola 36 25 46 38 48 56 55 63 41 56 41 51 556 Nurudeen 55 45 61 54 41 34 46 47 51 43 46 56 579 Balogun 26 36 37 48 39 43 47 37 51 56 61 41 522 Independence 30 23 48 46 36 28 21 53 41 55 56 51 488 Ogunsefunmi 35 46 31 36 41 31 26 56 51 46 48 36 483 Abeokuta 24 43 46 53 41 43 56 51 40 58 63 100 618 Obasa 15 10 20 25 30 15 10 15 75 80 72 25 392 Bashiru 45 50 60 50 60 75 80 60 80 84 86 100 830 Planking 15 56 67 76 51 36 31 51 58 60 41 23 565 241 UNIVERSITY OF IBADAN LIBRARY Olaide Tomori 21 54 68 61 48 56 54 54 54 53 46 21 590 Aro Omoeba 11 48 74 82 38 48 43 56 46 45 58 18 567 Adoni Ewa 12 56 81 47 54 34 36 41 57 58 43 25 544 Total 9178 16081 20224 19315 15644 15233 14005 16068 21254 26988 29670 21837 225497 Street Names AWOSIKA AREA – ZONE 2 Total 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm- 6pm - 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pm 7pm Oba Akran 2126 2347 1132 600 473 784 123 128 216 1321 3121 1204 13575 Ladipo Oluwole 1123 1875 50 60 78 600 800 60 167 2134 1341 89 8377 Gemco 200 600 712 70 45 50 55 45 56 81 678 63 2655 Sapara 1631 2921 100 500 781 856 786 600 160 2134 2040 314 12823 Awosika 25 56 86 50 45 60 78 89 160 45 56 50 800 Akinola 26 25 56 56 65 78 67 55 83 42 48 46 647 Ayodele Odiyan 15 35 44 30 85 60 45 25 43 45 34 45 506 Adeniyi 26 46 54 36 28 36 41 36 45 55 60 60 523 Total 5172 7905 2234 1402 1600 2524 1995 1038 930 5857 7378 1871 39906 Street Names OBANTA AREA – ZONE 3 Total 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm- 6pm - 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pm 7pm Ashogbon 20 45 68 69 33 86 28 31 45 83 92 24 624 Ali-Balogun 25 67 84 91 44 102 113 46 65 231 267 66 1201 Ogunlowo 15 35 26 48 36 21 25 26 37 38 29 32 368 Alhaji Kofowora 21 46 48 49 55 63 32 31 33 34 25 46 483 Modupe 28 45 33 21 26 38 21 23 35 18 36 28 352 Obafemi Awolowo 260 325 126 145 180 260 195 165 185 621 638 269 3369 Olu Akerele 23 45 65 36 39 42 46 36 58 54 65 86 595 Ojora 15 53 58 46 37 46 38 42 54 52 46 36 523 Olorunmbe 26 46 56 42 43 45 56 54 58 56 49 52 583 Eleruwa 35 40 45 43 54 53 46 62 48 49 56 61 592 Ajao 29 46 42 46 54 52 52 56 42 55 64 54 592 242 UNIVERSITY OF IBADAN LIBRARY Seriki Aro 80 160 234 150 100 250 260 180 60 286 320 180 2260 Obe 35 45 56 58 49 56 44 42 46 48 54 58 591 Ajasa 28 35 46 25 48 36 42 43 44 48 56 52 503 Obanta 36 48 65 54 56 57 68 59 46 55 48 62 654 Adeojo 26 35 26 56 72 43 25 42 44 59 62 63 553 Eric Moore 38 36 37 48 46 43 34 25 26 48 29 36 446 Labande 27 28 43 46 47 54 56 54 65 55 46 54 575 Afariogun 80 800 600 250 260 180 140 127 1123 2124 1021 278 6983 Shanu 42 21 23 46 34 48 28 24 46 36 25 446 819 Total 889 2001 1781 1369 1313 1575 1349 1168 2160 4050 3028 1983 22666 KUDETI AREA – ZONE 4 Total Street Names 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm- 6pm - 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pm 7pm Adeniyi Jones 212 453 234 126 148 261 134 108 126 321 486 179 2788 Aromire 65 124 98 106 78 64 85 52 121 148 236 98 1275 Wemabod 27 20 24 18 26 28 31 18 21 31 46 42 332 Kudeti 32 25 38 26 34 22 35 31 21 36 43 36 379 Obafemi Awolowo 647 2132 1245 684 821 1812 623 721 821 1713 3214 2169 16602 Siyanbola 23 34 55 56 48 56 50 45 56 71 41 36 571 Adebowale 20 43 46 75 52 32 33 24 36 27 40 28 456 Orimolade 18 21 22 23 16 24 32 21 27 18 22 32 276 Ikare 26 18 12 21 23 32 16 23 26 32 16 24 269 Fafowori 28 24 18 21 16 18 22 10 12 22 28 18 237 Badagry 36 28 24 18 21 29 18 16 16 18 21 16 261 Ladipupo Oluwole 174 213 341 36 45 89 57 47 121 186 249 107 1665 Odanye 25 16 12 10 10 11 10 12 9 18 22 19 174 Aba Johnson 28 19 20 8 9 10 12 16 11 21 26 16 196 James Olaleye 15 18 12 6 4 8 9 18 14 18 16 14 152 Odegbami 19 10 9 14 12 10 13 14 16 18 21 11 167 Alh. Duro Dania 27 21 16 13 11 9 11 14 12 11 12 14 171 Molade Okoya 16 18 12 14 10 15 20 16 24 16 24 31 216 Orimolade 12 10 11 12 18 16 18 12 10 11 12 24 166 Ikare 10 12 15 12 13 14 16 15 16 18 21 13 175 243 UNIVERSITY OF IBADAN LIBRARY Oyero 16 18 21 9 11 12 14 13 16 10 11 12 163 Alh. Bankole 18 24 35 21 12 14 16 18 16 12 36 23 245 Talabi 17 31 35 21 16 18 21 26 28 46 55 48 362 Akin Laguda 21 21 34 18 9 11 10 13 11 36 41 28 253 Total 526 3353 2389 1368 1463 2615 1306 1303 1587 2858 4739 3038 27551 Street Names AKEEM BALOGUN – ZONE 5 Total 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm- 6pm - 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pm 7pm Akeem Balogun 21 46 36 30 29 35 46 31 28 55 60 48 465 Abiodun Shobajo 26 41 24 41 46 49 50 32 26 60 48 36 479 Obafemi Awolowo 326 465 1121 1210 700 534 136 146 321 2136 2445 676 10216 Lateef Jakande 68 160 256 188 216 189 121 146 168 464 362 160 2498 Lagos –Ibadan 251 362 10 15 11 08 14 21 35 562 678 1132 3099 Total 692 1074 1447 1484 1002 815 367 376 578 3277 3593 2052 16757 AJANAKU AREA – ZONE 6 Total Street Names 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm- 6pm - 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pm 7pm Opebi-Oregun 66 126 102 60 101 68 46 55 214 361 378 136 1713 Opebi 86 236 210 87 126 62 63 58 264 382 321 268 2163 Irewole 18 35 36 24 38 16 12 11 24 46 53 38 351 Idowu 9 21 26 35 21 9 8 10 26 33 43 26 267 Church 12 46 36 27 19 12 11 13 14 36 46 33 305 Gafaru Balogun 21 36 39 20 21 14 10 16 21 31 36 28 293 Olayemi Abiola 22 41 23 18 12 11 10 18 16 21 28 19 239 Osho 11 21 24 33 26 11 10 18 16 21 26 30 247 Salvation 16 35 28 36 24 32 11 9 8 11 48 56 314 Methodist 21 35 46 21 26 24 18 21 26 55 48 38 379 244 UNIVERSITY OF IBADAN LIBRARY Oyetola 22 21 34 26 21 16 21 18 21 25 46 23 294 Aderoju Adewunmi 18 20 11 18 14 12 13 18 21 26 31 16 218 Folusho Alade 21 22 18 16 21 10 12 16 14 21 28 22 221 Awose 16 12 16 18 23 12 16 11 16 31 26 24 221 Aberoreniyi 21 24 31 21 16 10 11 9 16 32 36 26 253 Igbasa 23 26 24 32 27 12 13 10 21 36 39 28 291 Dalago 21 48 56 38 46 53 21 36 34 65 54 38 510 Alfred Olaiga 8 56 34 26 43 26 36 19 16 35 58 52 409 Ajanaku 18 56 34 26 43 26 36 19 16 35 58 52 419 Obafemi Awolowo 126 326 451 214 184 106 156 124 241 478 514 186 3106 Thomas Olaiya 25 46 55 62 43 35 21 16 18 43 55 62 481 Total 601 1289 1334 858 895 577 555 525 1063 1824 1972 1201 12694 Street Names GOVERNOR AREA – ZONE 7 Total 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm- 6pm - 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pm 7pm Obafemi- Awolowo 631 2146 2139 821 624 321 361 289 432 931 3167 2121 13983 Governor 42 86 88 64 48 62 35 46 33 126 287 324 1241 Kafi 35 67 123 121 102 86 74 67 62 214 312 307 1570 House of assembly 148 1241 1246 607 732 726 623 321 164 2123 1674 146 9751 Total 856 3540 3596 1613 1506 1195 1093 723 691 3394 5440 2898 26545 Street Names KADIRI AREA – ZONE 8 Total 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm- 6pm - 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pm 7pm Kudirat Abiola 123 1021 861 433 124 86 45 36 48 867 1841 931 6416 Jogunosimi 35 48 54 48 34 26 18 21 16 86 124 108 618 Obafemi Awolowo 143 281 342 446 214 123 64 43 24 1121 2113 1411 6325 Bamgbose 26 46 32 28 32 26 28 35 46 58 54 21 432 Odewale 23 35 38 21 22 18 21 16 32 46 58 19 349 Kadiri 1 16 26 29 18 16 26 34 26 18 42 38 26 315 Kadiri 2 23 31 38 28 27 36 26 28 11 53 45 31 377 245 UNIVERSITY OF IBADAN LIBRARY Makinde 21 22 35 26 18 27 19 16 21 46 32 19 302 Oshin 21 31 26 31 21 26 48 57 18 46 48 21 394 Oyeleke 18 26 32 17 18 21 18 12 16 24 36 18 256 Olaiya 15 18 16 14 19 26 12 11 09 26 37 16 219 Sunday Adigun 25 225 123 102 64 52 46 45 26 124 216 213 1261 Iyala 16 23 18 24 46 53 42 26 37 55 46 26 412 Kafi 35 67 123 121 102 86 74 67 62 214 312 307 1570 Total 540 1900 1767 1357 757 632 495 439 384 2808 5000 3167 19246 OLANREWAJU – ZONE 9 Total Street Names 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm- 6pm - 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pm 7pm Oyeleke 18 26 32 17 18 21 18 12 16 24 36 18 256 Kudirat Abiola 124 261 341 286 316 214 134 107 186 324 432 261 2986 Oregun 86 126 247 167 184 102 132 86 73 146 231 102 1682 Olayiwola 26 33 26 27 23 36 28 16 17 26 38 22 318 Liwa 24 26 32 24 36 16 24 12 10 32 43 24 303 Sanyaolu 22 32 36 26 34 18 16 23 24 28 36 21 316 Olanrewaju 18 21 41 32 34 21 36 24 16 42 56 43 384 Abayomi 16 32 26 34 36 28 38 42 21 36 42 39 390 Bamidele 14 26 31 29 36 27 43 31 16 44 26 38 361 Alh. Bakson 20 36 24 28 27 33 48 21 27 31 28 26 349 Fashade 25 26 36 33 35 46 39 27 38 21 48 36 410 Oregunwa 26 48 30 26 47 38 22 56 37 48 36 29 443 Ikosi 18 24 26 32 33 28 36 21 19 24 13 26 300 Mobolaji Johnson 15 36 24 21 20 16 17 18 21 36 21 18 263 Secretariat 35 42 55 62 30 21 14 26 54 36 46 62 483 Adeniji 20 26 32 27 38 23 26 31 18 19 55 36 351 Total 507 821 1039 871 947 688 671 553 593 917 1187 801 9595 MOBOLAJI JOHNSON – ZONE 10 Total Street Names 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm- 6pm - 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pm 7pm 246 UNIVERSITY OF IBADAN LIBRARY Oregun 86 126 247 167 184 102 132 86 73 146 231 102 1682 Billingsway 26 121 136 214 206 121 106 121 86 214 204 132 1687 Olanrewaju 18 21 41 32 34 21 36 24 16 42 56 43 384 De Iyamu 14 26 23 18 20 23 18 10 11 9 26 29 227 Kaf 35 67 123 121 102 86 74 67 62 214 312 307 1570 Ikosi 18 24 26 32 33 28 36 21 19 24 13 26 300 Mobolaji Johnson 15 36 24 21 20 16 17 18 21 36 21 18 263 Total 212 421 620 605 599 397 419 347 288 685 863 657 6113 Street Names KASUMU ALELSHINLOYE – ZONE 11 Total 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm- 6pm - 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pm 7pm Kasumu Aleshinloye 26 43 32 24 36 26 21 19 11 55 48 32 373 Obafemi Awolowo 86 123 79 89 74 68 55 48 73 194 187 164 1240 Acme 126 2123 624 563 1146 1612 106 178 214 1132 2416 178 10418 Vori 26 36 46 55 34 24 36 46 86 24 79 28 520 Acme Crescent 36 64 56 42 31 21 35 21 19 18 55 43 441 Ajobiewe 15 25 16 34 43 11 12 10 09 26 38 43 282 Fagba 10 12 15 10 09 12 14 10 11 11 36 43 193 Ajumobi Olorunojo 18 10 18 11 12 10 18 21 10 09 42 33 212 Kasumu Aleshinloye 18 23 16 24 28 27 16 11 16 9 26 35 249 MBH Power Ltd 26 13 14 9 8 11 12 10 8 21 16 18 166 Lateef Jakande 126 137 67 56 46 76 47 101 42 32 213 173 1116 Total 513 2609 983 917 1467 1898 372 475 499 1531 3156 790 21057 Street Names MORRISON – ZONE 12 Total 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm- 6pm - 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pm 7pm Obafemi Awolowo 143 281 342 446 214 123 64 43 24 1121 2113 1411 6325 Oregun 86 126 247 167 184 102 132 86 73 146 231 102 1682 Industrial Estate 48 213 247 365 124 123 136 66 78 268 432 276 2376 247 UNIVERSITY OF IBADAN LIBRARY Morrison 29 42 36 24 48 37 19 16 26 11 52 46 386 Ogundana 18 23 12 26 34 21 12 11 12 09 26 20 224 Billy Odumala 25 36 47 56 32 24 26 18 19 21 46 57 407 Imayan 18 22 23 17 26 30 21 24 18 20 23 36 278 Olaniyi 28 24 18 21 16 19 20 11 12 20 29 20 238 Lawal 22 18 12 21 23 12 16 09 10 18 21 26 208 Sadatu 20 44 47 65 42 22 23 14 26 37 40 29 409 Olasumbo 21 21 34 19 11 21 20 23 21 46 51 38 326 Aly. Muri 19 34 45 31 12 24 16 18 16 22 46 33 316 Tairu Olugbami 11 13 16 13 14 15 17 16 17 19 22 14 187 Oremeta 17 19 13 15 11 16 21 17 25 17 25 32 228 Odunkan 27 26 14 23 32 18 24 28 34 30 20 20 296 Adebayo Banjo 23 34 56 47 38 56 40 35 46 61 31 26 493 Afolabi Awosanya 26 20 24 20 28 30 33 20 23 33 48 44 349 Irewole 36 28 26 20 23 32 20 18 20 23 20 18 284 Opebi 86 236 210 87 126 62 63 58 164 382 321 268 2063 Osho 21 21 36 20 11 13 12 15 13 38 43 30 273 Adenuga 14 16 21 16 17 19 18 19 21 25 18 20 224 Olayinka 20 21 22 20 20 21 22 19 28 32 29 18 272 Kosebinu 15 35 26 38 26 21 15 16 27 28 30 33 310 Felicia Koleoso 18 20 23 11 13 14 16 15 18 12 13 15 188 Total 791 1373 1597 1588 1125 875 806 615 771 2439 3730 2632 18342 Street Names ALLEN – ZONE 13 Total 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm- 6pm - 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pm 7pm Allen 128 1214 2136 324 365 429 123 106 246 1471 1234 268 8044 Obafemi Awolowo 647 2132 1245 684 812 1812 623 721 821 1712 3214 2164 24587 Afolabi Aina 78 124 261 361 161 214 86 77 126 326 385 272 2471 Orija 39 69 164 219 186 274 108 121 74 216 184 127 1781 Balogun 25 36 58 49 40 58 42 37 48 63 33 29 518 Adepeju 30 42 62 37 31 33 25 24 27 30 46 57 444 Abajobi 25 26 42 35 16 18 18 21 18 43 48 35 345 Alade avenue 28 24 18 24 20 22 23 16 18 23 26 38 280 248 UNIVERSITY OF IBADAN LIBRARY Alade close 22 20 12 20 12 19 16 23 27 42 36 24 273 Fadeyi 26 21 25 21 29 32 22 35 22 25 35 50 343 Soji Adepegba 86 126 124 68 102 63 48 41 56 126 187 193 1220 Adeleke 36 28 26 20 23 32 20 23 33 26 43 35 345 Adeboye Solanke 66 89 76 43 58 33 27 21 33 61 83 96 686 Akinosiyemi 43 56 48 36 39 42 31 26 19 59 62 47 508 Ajayi 25 25 28 22 31 35 26 29 23 25 28 41 338 Hilton 18 44 47 26 23 24 32 26 28 46 51 35 400 Musa Ake 30 26 20 26 24 30 35 38 25 48 62 33 397 Ogunsiji 25 20 23 26 33 28 19 23 27 40 36 44 344 Olori Monisola 23 37 28 37 26 21 18 19 30 48 35 21 343 Olaribiro 24 22 25 16 19 23 20 23 17 35 33 35 292 Bolanle 19 35 26 38 26 21 15 16 27 28 30 29 310 Dipeolu 14 16 21 15 17 19 16 21 24 39 32 25 259 Adegbeyemi 22 21 36 20 13 15 18 16 21 18 36 43 279 Total 1479 4253 4551 2167 2106 3297 1411 1503 1790 4550 5959 3741 44807 Street Names LINITY AREA – ZONE 14 Total 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm- 6pm - 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pm 7pm Obafemi Awolowo 1213 2010 1870 2070 2311 1467 1234 2641 3123 3142 3143 2113 26337 Oriyomi 146 703 368 1113 836 874 649 136 1123 2143 2143 1102 11336 Kodesoh 1050 1241 1331 800 712 862 463 481 1213 2867 3112 2162 16294 Ilo 15 21 13 12 14 21 12 10 21 31 23 16 209 Olowu 146 513 486 236 321 141 123 421 623 1123 2131 324 6588 Adesina 21 36 22 43 46 51 20 15 18 35 46 53 406 Falola 15 10 22 25 30 16 10 25 27 38 42 36 296 Orishe 136 346 260 155 140 136 140 140 147 355 343 158 2456 Linity 148 181 112 96 131 142 166 147 158 202 314 423 2220 Ezekiel 45 52 64 50 61 76 82 55 84 80 86 96 831 Yinusa Adeni 20 31 34 40 46 50 55 43 45 56 41 52 513 Mojidi 23 37 38 40 48 50 55 40 42 50 43 46 512 Funmilola Okikiolu 55 46 67 54 53 41 43 57 58 62 46 58 640 Adeleye 24 43 46 56 42 32 36 56 48 40 48 49 520 249 UNIVERSITY OF IBADAN LIBRARY Alh. Tokunbo 20 27 38 40 48 50 61 40 41 42 51 31 489 Toyin 184 142 124 206 247 213 104 124 111 312 247 219 2233 Amope 15 25 25 40 45 25 20 25 35 80 26 71 432 John Olugbon 20 30 35 31 38 43 50 33 38 41 44 51 454 Oluyide 16 12 21 20 31 28 10 12 23 15 26 28 242 Owodunni 26 43 48 34 36 25 21 40 33 58 63 56 483 Adenubi 15 12 21 26 31 18 10 14 25 46 38 48 304 Majekodunmi 25 43 22 16 19 10 09 08 24 36 48 32 292 Emina 10 25 15 16 18 10 21 09 15 12 23 28 202 Allen 128 1214 2136 324 365 429 123 106 246 1471 1234 268 8044 Oladipupo Kuku 36 44 55 43 31 48 36 32 21 31 28 43 448 Wemi Akinsola 28 36 42 37 22 42 29 28 16 33 21 55 389 Ladipupo Kasiumu 20 28 32 28 18 54 26 33 24 42 36 46 387 Ogun 33 46 50 41 43 46 23 24 19 48 29 58 460 Morenikeji 27 38 41 52 32 49 26 18 22 37 20 47 409 Bisi Ogabi 27 20 25 20 12 29 19 16 23 28 46 37 302 Oluwaleyimi 36 29 22 20 24 30 21 26 30 26 48 42 354 Bayode Oluwole 23 22 37 21 14 16 19 17 22 19 39 40 289 Atinuke Olabamiji 25 20 24 26 32 27 20 74 30 43 36 44 401 Akintoye Sogunle 28 24 20 23 18 21 10 18 28 39 56 21 306 Baloogun 26 18 14 21 21 17 12 11 17 44 28 47 276 Ipodo 346 1621 1781 1976 1362 1113 968 824 1034 1672 1891 578 15166 Total 4171 8789 9361 7851 7298 6302 4726 5799 8607 14399 15639 8578 101520 Street Names ALABI AREA – ZONE 15 Total 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm- 6pm - 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pm 7pm Kodesoh 1050 1214 1321 800 712 862 463 481 1213 2867 3112 2162 16257 Tonade 27 35 40 42 22 39 16 18 22 27 40 27 355 Ipodo 346 1621 1781 1976 1362 1113 968 824 1034 1672 1891 578 15166 Olowu 146 513 486 236 321 141 123 421 623 1123 2131 324 6588 250 UNIVERSITY OF IBADAN LIBRARY Oladosu 23 30 40 31 23 34 18 22 31 43 28 37 360 Unity 148 181 112 96 131 142 166 147 158 202 213 423 2119 Ola Ayinde 64 162 134 87 106 172 87 134 178 192 127 148 1591 Alabi 54 68 82 45 55 36 33 21 102 100 148 116 860 Moshood Abiola 25 88 43 24 48 62 23 36 38 46 74 58 565 Mobolaji Bank Anthony 23 26 25 32 28 22 13 10 21 36 39 28 303 Ilori Moses 25 46 55 64 40 38 20 16 19 46 40 50 459 Christland 24 36 42 25 43 52 23 18 23 48 33 49 416 Adedeji 22 43 40 30 35 28 18 18 20 38 55 38 385 Adesina 20 36 42 25 33 29 21 19 21 20 46 40 352 Adebanjo 23 39 45 36 29 32 19 20 24 35 62 42 406 Adeyeri 25 42 52 28 31 28 20 22 35 20 35 56 394 Shogunle 15 25 38 30 28 33 22 18 29 19 39 58 354 Oredugba 18 28 44 29 32 43 21 19 35 20 44 56 389 Olaniyan 28 35 57 32 25 38 20 21 40 18 46 41 401 Opebi 86 236 210 87 126 62 63 53 264 382 321 268 2158 Opebi link 66 126 102 60 101 68 46 55 214 361 378 136 1713 Toyin 184 424 124 206 247 213 104 124 111 312 247 219 2515 Total 2442 5054 4915 4021 3578 3287 2307 2517 4255 7627 9149 4954 54106 COMMUNITY AREA ZONE 16 Total Street Names 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm- 6pm - 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pm 7pm Ajayi 125 255 102 86 36 121 36 48 76 88 245 106 1324 Allen lane 25 46 36 48 22 54 21 18 17 43 56 59 445 Allen Ave 128 1214 2136 324 365 429 123 106 246 1471 1234 268 8044 Community 25 36 26 24 20 31 20 21 43 54 42 26 368 Bola Ajibola 28 28 32 30 24 28 18 22 26 48 38 21 343 Ondo 32 43 28 36 28 36 16 31 54 42 34 20 400 Bamishile 30 50 24 38 31 42 21 21 46 39 30 21 393 Tiwalode 38 46 33 42 34 43 32 18 50 40 28 27 431 Adefolu 27 38 28 37 29 37 28 19 39 32 25 20 359 Agbarebo 33 28 24 21 11 30 21 12 41 25 14 18 278 Oyediran 26 32 28 33 14 35 19 21 38 27 22 15 310 251 UNIVERSITY OF IBADAN LIBRARY Anuoluwapo 29 42 31 24 17 44 32 20 40 31 23 17 350 Regina Omolara 20 35 21 20 19 32 70 15 18 25 33 26 334 Tolawewo 21 46 37 47 21 56 15 21 16 32 48 37 397 Total 587 1939 2586 810 671 1018 472 393 750 1997 1872 681 13776 ACME AREA – ZONE 17 Total Street Names 7am – 8am – 9am- 10am- 11am- 12pm- 1pm- 2pm- 3pm- 4pm- 5pm- 6pm - 8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pm 7pm Ogba 246 456 231 121 127 148 76 54 38 213 543 217 2470 Ijaye 28 54 42 33 28 48 27 20 11 54 48 40 433 Damson 15 25 36 16 18 28 11 15 17 28 35 23 267 Alh. Umar Sanni 18 21 10 11 17 23 19 10 12 43 38 42 264 Pivot Co Ltd 15 22 11 15 21 43 11 21 10 32 26 27 254 Cocoa Industrial 15 45 36 28 43 54 18 10 12 37 48 54 400 Wemco 45 68 84 113 78 136 48 36 24 178 204 172 1186 Ladipupo Oluwole 175 213 341 36 45 89 61 47 121 186 249 107 1670 Israel Adebanjo 20 35 21 10 9 12 15 11 10 21 15 32 211 Vanni Close 15 55 48 35 21 65 18 11 28 43 56 43 438 Surulere Industrial 128 436 238 121 211 315 87 66 52 126 345 316 2441 Akin Lalcome 18 28 35 21 10 21 22 15 17 18 20 11 236 Olutoye 18 23 15 26 10 18 10 11 13 15 20 21 200 Talabi 21 32 26 38 28 32 18 15 15 25 35 43 328 Guinness 15 37 26 18 29 36 48 54 10 56 48 59 436 Lateef Jakande 145 356 132 102 131 143 82 46 54 86 264 207 1748 Meta box 128 346 215 102 87 178 38 43 52 214 306 186 1895 Akilo 43 58 43 35 46 68 43 40 32 84 235 578 1305 ACME 126 2133 624 563 1146 1612 106 178 214 1132 2416 178 10428 Total 1234 4443 2214 1444 2105 3069 758 703 742 2591 4951 2356 26610 Source: Field Survey, 2009. 252 UNIVERSITY OF IBADAN LIBRARY APPENDIX IV: Household Heads and On-Street Persons Walking Distances to Bus Stations in the Study Area. Zones 0m- 100m 100m-200m 200m-300m 300m-400m 400m-500m Hshld Onsp Hshld Onsp Hshld Onsp Hshld Onsp Hshld Onsp Z1 7 0 16 2 39 2 6 11 11 1 Z2 10 2 8 0 22 2 5 5 4 2 Z3 6 1 16 1 33 4 4 9 10 11 Z4 19 3 21 2 28 2 8 11 9 2 Z5 15 1 4 1 22 2 5 5 6 2 Z6 32 1 14 1 8 2 2 6 2 4 Z7 21 0 8 1 4 1 4 2 4 5 Z8 26 1 6 1 3 1 2 4 3 3 Z9 2 0 10 1 24 1 4 5 6 2 Z10 4 1 10 0 9 1 3 2 3 2 Z11 8 0 8 1 27 2 8 2 4 5 253 UNIVERSITY OF IBADAN LIBRARY Z12 15 1 30 1 12 1 8 3 6 9 Z13 11 2 28 2 10 3 4 4 12 7 Z14 19 1 27 1 6 2 6 5 8 9 Z15 8 1 27 1 5 1 8 3 4 8 Z16 16 1 25 1 9 1 6 2 5 8 Z17 28 1 11 1 1 3 6 3 5 6 Source: Field Survey, 2009. 254 UNIVERSITY OF IBADAN LIBRARY APPENDIX V: Households Heads and On-Street Persons Walking Distances to Landuse Activities in the Study Area. Zones (0 – 1)km (1 – 2)km (2 – 3)km (3 – 4)km (4 – 5)km Hshld Onsp Hshld Onsp Hshld Onsp Hshld Onsp Hshld Onsp Z1 25 7 20 2 7 6 6 11 3 4 Z2 22 2 18 0 5 2 1 5 4 1 Z3 36 1 26 1 6 4 4 6 10 1 Z4 22 2 21 2 9 6 8 11 11 2 Z5 30 1 9 3 3 2 2 5 6 2 Z6 32 2 9 1 8 1 4 5 6 0 Z7 20 1 8 3 4 1 4 5 4 1 Z8 25 2 6 1 3 1 4 5 3 1 Z9 23 9 13 1 2 2 1 5 6 2 Z10 10 2 10 0 3 2 3 2 4 2 Z11 15 1 14 0 0 2 0 5 5 2 Z12 36 2 15 1 8 1 6 3 12 3 255 UNIVERSITY OF IBADAN LIBRARY Z13 26 1 11 3 10 4 4 2 7 4 Z14 32 0 19 1 6 2 6 4 12 2 Z15 33 0 8 0 5 2 8 2 4 2 Z16 31 0 16 1 9 1 6 3 5 2 Z17 28 1 18 1 8 3 6 8 10 4 Source: Field Survey, 2009. 256 UNIVERSITY OF IBADAN LIBRARY APPENDIX VI: Regression Analysis Results of the Relationship between Household Heads and On-street Persons Pedestrian Trips Generated. Variables Variables Regression Standard Error t-values Acronym Coefficients Work WORK 0.353 0.101 10.125 Religion RELI 0.320 0.161 8.415 Business BNES 0.074 0.165 2.627 Schooling SCHL -0.038 0.276 -0.758 Shopping SHOP 0.258 0.146 7.138 Social Function SOFU -0.004 0.449 -0.221 Visiting Friend VIST 0.076 2.324 1.843 Exercising EXER 0.082 1.172 2.432 Industries NINDU 0.061 0.166 2.726 Hotels and NHRES 0.034 0.313 1.103 Restaurants Financial NFINI -0.171 0.500 -3.223 Institution Shopping Mall NSHPM 0.047 1.836 1.577 Fast Food NFAST 0.288 1.241 3.816 Points Accessibility ACCESS -0.010 0.081 -0.435 Constant 0.735 6.512 0.113 N 1,182 Source: Field Survey, 2009 257 UNIVERSITY OF IBADAN LIBRARY APPENDIX VII: Software Procedure of CGI Analytical Hierarchical Process (AHP) Procedure Results Input Size of pairwise comparison matrix Input The values of pairwise comparison matrix Display Weight ( Eigen vector), Eigen value, and Consistency Index Output Text file Source: takahagi@isc_senshu-u.ac.jp 258 UNIVERSITY OF IBADAN LIBRARY APPENDIX VIII: Road segment number, level of service, pedestrian volume, lateral separation, motor vehicle volume, speed of motor vehicle and driveway access volume. Road Level Pedestrian Lateral Motor Speed of Driveway Segment Of Volume Separation Vehicle Motor Access Number Service (Feet) Volume Vehicle Volume -1 (Km ) 1 4 25 28.245 34 45 2 2 4 30 28.245 41 56 1 3 1 65 12.705 7 35 0 4 1 48 12.705 8 46 2 5 2 30 26.69 10 10 1 6 2 26 26.69 18 12 0 7 1 56 13.913 6 10 0 8 1 48 13.913 8 10 0 9 1 27 12.443 10 25 0 10 1 69 12.443 5 26 1 11 1 44 8.138 7 10 1 12 1 48 5.915 6 30 2 13 1 39 10.08 8 35 3 14 1 26 16.555 14 28 0 15 1 44 12.548 7 36 0 16 1 56 17.29 8 34 3 17 1 35 11.9 12 38 2 18 1 28 13.615 11 18 0 19 1 36 14.998 13 36 4 20 1 54 8.138 6 38 2 21 1 48 5.915 9 40 1 22 1 36 10.08 8 42 0 23 1 34 16.555 10 30 0 24 1 29 12.548 18 35 0 25 1 35 17.29 14 10 0 26 1 26 11.9 11 20 0 27 1 27 13.615 10 30 3 28 1 32 14.988 12 20 2 29 2 23 22.19 18 31 4 30 2 27 12.233 11 10 0 31 2 31 22.19 10 26 0 32 2 57 14.998 6 15 1 33 1 23 12.233 14 10 2 34 1 43 13.913 8 30 0 35 1 14 10.08 15 26 0 36 1 22 5.915 7 10 3 37 1 15 10.08 10 30 1 38 1 36 13.913 8 24 1 39 4 18 53.305 56 45 0 40 2 11 35.63 34 41 1 41 2 26 25.655 28 32 0 42 2 21 35.613 41 35 1 43 2 16 25.655 18 31 0 259 UNIVERSITY OF IBADAN LIBRARY 44 4 36 53.305 42 56 1 45 1 21 14.998 10 10 2 46 1 38 11.9 9 16 3 47 1 29 12.233 10 30 2 48 1 32 13.913 18 20 2 49 1 23 5.915 10 12 0 50 1 26 10.08 24 15 0 51 1 16 12.705 24 35 0 52 1 8 12.443 31 31 1 53 1 21 12.548 26 26 2 54 1 11 11.9 11 40 3 55 1 9 12.705 18 45 4 56 1 18 12.443 30 28 2 Source: Field Survey, 2009. 260 UNIVERSITY OF IBADAN LIBRARY APPENDIX IX: Logarithm of pedestrian volume, lateral separation, motor vehicle Volume, speed of motor vehicle and driveway access volume. Road Level Pedestrian Lateral Motor Speed of Driveway Segment Of Volume Separation Vehicle Motor Access Number Service Volume Vehicle Volume 1 4 1.39794 1.450942 1.531479 1.653213 0.30103 2 4 1.477121 1.450942 1.612784 1.748188 0.00000 3 1 1.812913 1.103975 0.845098 1.544068 0.00000 4 1 1.681241 1.103975 0.90309 1.662758 0.30103 5 2 1.477121 1.426349 1.000000 1.000000 0.00000 6 2 1.414973 1.426349 1.255273 1.079181 0.00000 7 1 1.748188 1.143421 0.69897 1.000000 0.00000 8 1 1.681241 1.143421 0.90309 1.000000 0.00000 9 1 1.431364 1.094925 1.000000 1.39794 0.00000 10 1 1.838849 1.094925 0.69897 1.414973 0.00000 11 1 1.643453 0.910518 0.845098 1.000000 0.00000 12 1 1.681241 0.771955 0.778151 1.477121 0.30103 13 1 1.591065 1.003461 0.90309 1.544068 0.477121 14 1 1.414973 1.218929 1.146128 1.447158 0.00000 15 1 1.643453 1.098575 0.845098 1.556303 0.00000 16 1 1.748188 1.237795 0.90309 1.531479 0.477121 17 1 1.544068 1.075547 1.079181 1.579784 0.30103 18 1 1.447158 1.134018 1.041393 1.255273 0.00000 19 1 1.556303 1.176033 1.113943 1.556303 0.60206 20 1 1.732394 0.910518 0.778151 1.579784 0.30103 21 1 1.681241 0.771955 0.954243 1.60206 0.00000 22 1 1.556303 1.003461 0.90309 1.623249 0.00000 23 1 1.531479 1.218929 1.000000 1.477121 0.00000 24 1 1.462398 1.098575 1.255273 1.544068 0.00000 25 1 1.544068 1.237795 1.146128 1.000000 0.00000 26 1 1.414973 1.075547 1.041393 1.30103 0.00000 27 1 1.431364 1.134018 1.000000 1.477121 0.477121 28 1 1.50515 1.175744 1.079181 1.30103 0.30103 261 UNIVERSITY OF IBADAN LIBRARY 29 2 1.361728 1.346157 1.255273 1.491362 0.60206 30 2 1.431364 1.091069 1.041393 1.000000 0.00000 31 2 1.491362 1.346157 1.000000 1.414973 0.00000 32 2 1.755875 1.175744 0.778151 1.176091 0.00000 33 1 1.361728 1.091069 1.146128 1.000000 0.30103 34 1 1.633468 1.143421 0.90309 1.477121 0.00000 35 1 1.146128 1.003461 1.176091 1.414973 0.00000 36 1 1.342423 0.771955 0.845098 1.000000 0.477121 37 1 1.176091 1.003461 1.000000 1.477121 0.00000 38 1 1.556303 1.143421 0.90309 1.380211 0.00000 39 4 1.255273 1.726768 1.748188 1.653213 0.00000 40 2 1.041393 1.551816 1.531479 1.612784 0.00000 41 2 1.414973 1.409172 1.447158 1.50515 0.00000 42 2 1.322219 1.551596 1.612784 1.544068 0.00000 43 2 1.20412 1.409172 1.255273 1.491362 0.00000 44 4 1.556303 1.726768 1.623249 1.748188 0.00000 45 1 1.322219 1.175946 1 1 0.30103 46 1 1.579784 1.075547 0.954243 1.20412 0.477121 47 1 1.462398 1.087533 1 1.477121 0.30103 48 1 1.50515 1.143421 1.255273 1.30103 0.30103 49 1 1.361728 0.700271 1 1.079181 0.00000 50 1 1.414973 1.003461 1.380211 1.176091 0.00000 51 1 1.20412 1.103975 1.380211 1.544068 0.00000 52 1 0.90309 1.094925 1.491362 1.491362 0.00000 53 1 1.322219 1.098575 1.414973 1.414973 0.30103 54 1 1.041393 1.075547 1.041393 1.60206 0.477121 55 1 0.954243 1.103975 1.255273 1.653213 0.60206 56 1 1.255273 1.094925 1.477121 1.447158 0.30103 Source: Field Survey, 2009. 262 UNIVERSITY OF IBADAN LIBRARY