ANALYSIS OF PRESENT TRAFFIC CONDITION WITHIN

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ANALYSIS OF PRESENT TRAFFIC CONDITION WITHIN

ABSTRACT

 Traffic congestion is a common feature on highways in many cities of the world, including Akure , Nigeria. Previous studies have shown that several mathematical traffic flow models developed to analyse congestion cannot be easily generalised or adapted to varying situations. In addition, validation errors of some models are as high as 60.0 %. In pursuit of the objective of minimising traffic congestion in parts of the Akure , headway simulation models were developed for the analysis of flow on some selected highways characterised by heavy traffic. 

 Traffic survey was conducted on three purposively selected heavily-trafficked highways  in the Akure . Headway modelling approach incorporating the prevailing roadway, traffic and control conditions was developed. Field data were captured on the three roads with a camcorder between 7.00 a.m. and 6.00 p.m. for a period of six months as specified in the Highway Capacity Manual. Comparison of the modelling result and field headway data were carried out using Kolmogorov-Smirnov (KS) test (p = 0.05). A traffic flow simulator was developed to simulate the different congestion scenarios by varying the minimum and maximum headways. Capacity analysis and validation of the results were carried out using ANOVA methods.

 Average vehicular flow of 715 ± 3, 970 ± 5 and 1118 ± 9 vph per lane on Total Garden-Agodi Gate, J Allen-Oke Bola and Odo Ona-Apata roads respectively. Eighteen hyperbolic headway scenarios were produced and the highest coefficient of correlation (R2 = 0.92) was recorded at 90 percentile while 0.18, 0.36, 0.50, 0.71, 0.82, and 0.79 were obtained at 1, 10, 30, 50, 70, and 100 percentiles respectively. There was no significant difference between theoretical and field data using KolmogorovSmirnov (KS) test (p < 0.05). Also, a total number of 171 congestion scenarios were generated using the traffic flow simulator. Traffic flow varied between 204 and 2376 pcu per lane while headways varied between 1 and 18 seconds. The capacity analysis produced approximated maximum flow rates of 1850, 2865 and 2881 pcu in the two directions of travel for Total Garden-Agodi Gate, J Allen-Oke Bola and Odo OnaApata roads respectively. The capacity of Total Garden-Agodi Gate was within the recommended maximum value of 2800 pcu in the two directions of travel for highways. The results for J Allen-Oke Bola and Odo Ona-Apata roads showed that an additional lane will be required in each direction of travel. The validation of the models on the dualised J Allen-Oke Bola road showed that congestion can be reduced by about 55.0 %. A maximum validation error of 35.0 % was obtained.

 The traffic flow simulator developed successfully simulated the traffic situations on the selected highways. The analysis of the flow yielded results that could ameliorate traffic congestion on the selected highways in the Akure .

Keywords: Traffic flow, Highways, Headway simulation models, Traffic           congestion, Capacity analysis.

Word Count: 469

TABLE OF CONTENTS

      Page

Certification                                                                                                                 ii

Dedication                                                                                                                   iii

Acknowledgements                                                                                                    iv

Abstract                                                                                                                       vi

Table of Contents                                                                                                      viii List of Figures                                                                                              xi List of Plates                                                                                               xiii

List of Tables                                                                                                            xiv

Notation                                                                                                                     xv

Chapter 1 INTRODUCTION                                                                                                          1

1.1       Background                                                                                                     1

1.2       Research Problem                                                                                           4

1.3       Study Area                                                                                                      5

1.4       Aim and Objectives                                                                                        5

1.5       Justification                                                                                                    10

Chapter 2 LITERATURE REVIEW                                                                                   

2.1        Traffic Flow                                                                                                   11

                  2.1.1  Traffic flow parameters                                                                      11

                 2.1.2  Measurement of Traffic Flow                                                             12

             2.1.3    Traffic Flow Regimes                                                                         12

2.2        Traffic congestion                                                                                          13

            2.2.1    Causes of traffic congestion                                                               13

            2.2.2    Negative Impacts of traffic congestion                                              13

            2.2.3    Congestion reduction strategies                                                          14

            2.2.4    Analysis of congested flow                                                                 16

2.3        Traffic flow modelling                                                                                   17

            2.3.1    Types of models                                                                                 17

             2.3.2  Criteria for model selection                                                                 18

                2.3.3    Traffic simulation                                                                               21

                2.3.4    Traffic simulation models                                                                   21

                                                                                                                                     Page

            2.3.5    Classification of traffic simulation models                                        22

             2.3.6  Traffic simulation model building                                                       24

            2.3.7    Vehicle generation algorithm                                                             25

2.4       Headway Distribution Models                                                                        26

             2.4.1   Headway distribution models for free flow                                        27

                2.4.2    Headway distribution models for constrained flow                            28

             2.4.3    Parameter estimation and calibration of headway models                 29

2.5       Highway Capacity                                                                                          32

            2.5.1    Factors affecting capacity                                                                   32

            2.5.2    Need for highway capacity analysis                                                   34

             2.5.3  Capacity analysis methods                                                                  35

            2.5.3.1 The Highway Capacity Manual method                                             35

            2.5.3.2 The British Standard Approach                                                          36

            2.5.3.3 Statistical method                                                                               38

            2.5.3.4 Dynamic highway capacity estimation method                                  38

            2.5.3.5 Safety-based capacity analysis                                                           38 

            2.5.4    Level of Service                                                                                 38

            2.5.5    Acceptable degrees of congestion                                                      40

            2.5.6    Design hourly volume                                                                        41

                     2.5.7  Capacity of highways                                                                   41

            2.5.8            Capacity analysis of highways                                                    42

Chapter 3 METHODOLOGY                                                                                             44

3.1       Traffic Survey                                                                                                44

3.2       Traffic Data collection                                                                                   44

            3.2.1    Headway data capturing and extraction                                             45

3.3       Headway Modelling Process                                                                          45

            3.3.1    Theoretical headway generation algorithm                                        45

            3.3.2    Hyperbolic headway distribution models                                           47

3.4       Traffic Flow Simulator (TRAFLOS)                                                             48

            3.4.1    TRAFLOS algorithm                                                                         48

3.5       Experimental Design of Congestion Scenarios                                              50

3.6       Practical Capacity of Selected Roads                                                             52                                                                                                                               Page

            3.6.1    Determination of adjustment factors                                                  52

3.7       Statistical Analysis                                                                                         53

Chapter 4 RESULTS AND DISCUSSIONS                                                                                  55

4.1       Traffic Survey Results                                                                                    55

               4.1.1  General summary                                                                                55

              4.1.2    Average traffic flow                                                                            57

              4.1.3    Field headways                                                                                   58

4.2       Headway Modelling Output                                                                           61

            4.2.1    Vehicular interaction                                                                          61

            4.2.2    Comparison of theoretical and field headways                                   67

4.3       Traffic Flow Analysis                                                                                     68

            4.3.1    Simulated traffic flows                                                                       68

            4.3.2    Congestion factors                                                                              72

            4.3.3    Capacity adjustment                                                                            75

            4.3.4    Capacity analysis for different congestion scenarios                         75

            4.3.5    Results of the analysis of variance test                                              82

            4.3.6    Validation of models for J Allen Oke-Bola road                                83

            4.3.7    Validation errors                                                                                 84

Chapter 5 CONCLUSION AND RECOMMENDATION                                               85

5.1 Conclusions                                                                                                          85

5.2   Recommendation                                                                                                85

REFERENCES                                                                                                         86 APPENDICES                                                                                           96

            Appendix A1: SONY camcorder operating guide                                          97

            Appendix A2: Extracted field headway data set                                           105

            Appendix B: Headway modelling output                                                      112

                Appendix C: Kolmogorov-Smirnov test                                                       135

                Appendix D: Traffic flow simulator output                                                   150

                Appendix E: One-way ANOVA test                                                             192

LIST OF FIGURES

                                                                                                                                     Page

Fig. 1.1: Nigeria’s road network                                                                                        6

Fig. 1.2: Akure  metropolitan area’s road network                                                           7

Fig. 1.3: Network of some principal roads in Akure                                                         8

Fig. 2.1: Model usage flow chart                                                                                     19

Fig. 3.1: Theoretical headway generation algorithm flowchart                                      46

Fig. 3.2: Traffic flow simulator flowchart                                                                       49

Fig. 4.1: Distribution of field headways for flows between 700 to 1200vph                  60

Fig. 4.2: Cumulative headway distribution for flows between 700 to 1200vph        66

Fig. 4.3: Distribution of simulated flows with minimum headway of 1 second             71

Fig. 4.4: Congestion factors for simulated flows                                                            74

Fig. B2.1: Hyperbolic model at 1 percentile vehicular interaction                            117 Fig. B2.2: Hyperbolic model at 2 percentile vehicular interaction                            118

Fig. B2.3: Hyperbolic model at 3 percentile vehicular interaction                               119

Fig. B2.4: Hyperbolic model at 4 percentile vehicular interaction                               120

Fig. B2.5: Hyperbolic model at 5 percentile vehicular interaction                               121

Fig. B2.6: Hyperbolic model at 10 percentile vehicular interaction                             122

Fig. B2.7: Hyperbolic model at 20 percentile vehicular interaction                             123

Fig. B2.8: Hyperbolic model at 30 percentile vehicular interaction                             124

Fig. B2.9: Hyperbolic model at 40 percentile vehicular interaction                             125

Fig. B2.10: Hyperbolic model at 50 percentile vehicular interaction                           126

Fig. B2.11: Hyperbolic model at 60 percentile vehicular interaction                           127

Fig. B2.12: Hyperbolic model at 70 percentile vehicular interaction                           128

Fig. B2.13: Hyperbolic model at 80 percentile vehicular interaction                           129

Fig. B2.14: Hyperbolic model at 90 percentile vehicular interaction                        129 Fig. B2.15: Hyperbolic model at 95 percentile vehicular interaction                          130

Fig. B2.16: Hyperbolic model at 98 percentile vehicular interaction                           131

Fig. B2.17: Hyperbolic model at 99 percentile vehicular interaction                           132

Fig. B2.18: Hyperbolic model at 100 percentile vehicular interaction                      133                                                                                                                               Page

Fig. C1.1: Comparison of field and simulated headways for flow rate of 700 vph     145

Fig. C1.2: Comparison of field and simulated headways for flow rate of 800 vph     146

Fig. C1.3: Comparison of field and simulated headways for flow rate of 900 vph     147

Fig. C1.4: Comparison of field and simulated headways for flow rate of 1000 vph   148 Fig. C1.5: Comparison of field and simulated headways for flow rate of 1100 vph   149

LIST OF PLATES

                                                                                                                                     Page

Plate 1.1: Traffic stream on Obafemi Awolowo road (before dualisation)                       9

Plate 1.2: Traffic stream on Odo Ona-Apata road                                                             9

Plate 3.1: Sony HDR-HC3 Camcorder                                                                            54

Plate 3.2: Traffic Flow Simulator screen                                                                     55 LIST OF TABLES

                                                                                                              Page
Table 2.1:   Overview of traffic flow models                                           20
Table 2.2:   Comparison of headway distribution models                        30
Table 2.3:   Recommended design flows for two-way urban roads      37
Table 2.4:   Level of service characteristics                                             39
Table 2.5:   Guide for selection of design levels of service                     41
Table 2.6:   Maximum service volumes under ideal conditions               43
Table 3.1:   Congestion scenarios design template                                  51
Table 4.1:   Summary of preliminary traffic study                                   56
Table 4.2:   Average traffic flow on selected roads                                  57   
Table 4.3:   Minimum and maximum values of headway                        58
Table 4.4:   Percentage composition of field headway per flow regime     59
Table 4.5: Hyperbolic headway simulation models                                 62
Table 4.6: Hyperbolic model adjustment factors                                 Table 4.7: Cumulative headway distribution spreadsheet      63
                 for flows between 700 to 1200 vph                                         64
Table 4.8: Kolmogorov-Smirnov test result                                             67
Table 4.9: Simulated traffic volume for different congestion scenarios     69
Table 4.10: Computed flow rates for different congestion scenarios      70
Table 4.11: Computed congestion factors for different congestion scenarios    73
Table 4.12: Congestion factors and equivalent level of service                          74
Table 4.13: Capacity adjustment factors                                                             75
Table 4.14: Capacity analysis of Total Garden-Agodi Gate road for kc=1         76
Table 4.15: Capacity analysis of J Allen-Oke Bola road for kc=1                      77
Table 4.16: Capacity analysis of Odo Ona-Apata road for kc=1                      Table 4.17: Simulated capacities at different congestion levels     78
                   for Total Garden-Agodi Gate road                                                Table 4.18: Simulated capacities at different congestion levels     79
                   for J Allen-Oke Bola road                                                             Table 4.19: Simulated capacities at different congestion levels     80
                   for Odo Ona-Apata road                                                                       81
Table 4.20: ANOVA test result for field and simulated capacities                                  82
Table 4.21: Capacity adjustment factors for dualised J Allen-Oke Bola road                  83
Table 4.22: Capacity analysis of dualised J Allen-Oke Bola Road                                84

NOTATION

A                                 =                      cumulative headway adjustment factor

C                                 =                      basic capacity

Cp                                =                      practical capacity

ei   =  lower boundary limit of headways in Gi di   =  upper boundary limit of headways in Gi f (x)   =  probability density function of x

fi                                  =                      adjustment factor Gi                            =                         headway group i

h                                  =                      headway

h1                                 =                      minimum headway h2                           =                      maximum headway H                                     =            cumulative headway  k                                   =                      traffic density kc                                 =                      congestion factor

ki                                            =                      headway group composition factor i q                                    =                      flow (vehicles arrival rate)

Rn                                =                      random number

R2                                =                      coefficient of correlation T                             =             total time/total headway v                              =                      mean (average) speed

V                                 =                      traffic volume

VR                                          =                      number of vehicles released per simulation run

Chapter 1

INTRODUCTION

     1.1       Background

The highway network is an important component of the transportation system. In Nigeria, it is the principal means of transportation facilitating the socioeconomic activities of the people. Highways (single carriageway) formed the main component of this system at the local, state and federal levels.  Efficient and effective flow of traffic is desirable for the highway system to operate optimally at designed capacity and for favourable level of service.

Traffic flow represents the interaction between vehicles, drivers and infrastructure. Traffic flow can be either free or constrained (Helbing, 2001; and Nagatani, 2002). In free flow conditions, drivers can choose their own speed or constrained to car-following system. Kerner (2004) classified the congestion regime into two distinct phases: synchronized flow and wide moving jams. In synchronized flow, the speeds of the vehicles are low and vary quite a lot between vehicles, but the traffic flow remains close to free flow. In wide moving jams, vehicle speeds are more equal and lower, and time delays can be quite large. Traffic congestion is a road condition characterised by speeds slower than free flow speeds, resulting in longer travel times and increased queuing (Aworemi et al., 2009; Hook 1995). It occurs when traffic demand is greater than the capacity of a road (Lee et al., 2008). Traffic jam is extreme traffic congestion where vehicles are fully stopped for periods of time (Abul-Magd, 2007).

Traffic congestion is considered one of the main urban transportation problems, particularly in developing countries where vehicle ownership is growing geometrically without corresponding sustainable land use patterns and transportation schemes (Tugbobo, 2009). Traffic congestion leads to increased travel time, air pollution and fuel consumption.  Providing additional lanes to existing highways and building new ones have been the traditional response to congestion (FHW  2005). However, the data collection effort for this exercise is great.  Consequently, transportation engineers and researchers are increasingly developing simulation models to analyse traffic flows on highways. 

Capacity expansion is one of the strategies usually adopted in both developed and developing countries to mitigate traffic congestion. Expanded highways improve traffic flow and reduce congestion. Capacity is the maximum number of vehicles that can pass a given point on a roadway or in a designated lane during one hour without the traffic density being so great as to cause unreasonable delay, hazard, or restriction to the drivers’ freedom to manoeuvre under the prevailing roadway and traffic conditions (TRB, 2000). Major attention has been given to capacity analysis methodology, because capacity estimates have a central role in the estimation of other highway performance measures (Luttinen, 2004). False estimation pollutes other reasonable traffic studies. Errors caused by inaccurate or wrong estimation of highway capacity can easily affect the results of other studies (Hwang et al., 2005).

Zang (2010) developed an improved highway capacity model that is feasible and can reflect the actual traffic flow characteristics; Yao et al. (2009) developed optimisation procedure that produced good estimates of the roadway capacity and other traffic stream parameters. Tanyel et al. (2005) showed that further studies should be made to develop a more reliable capacity and performance models for Turkey. Chang and Kim (2000) presented a quantitative method for highway capacity determination by evaluating alternative approaches in developing capacity from the statistical distribution of observed headways of traffic flow in Korea. Approximated headway distribution models of free-flowing traffic on Ohio Freeways was developed by Zwahlen et al. (2007) to simulate queue buildup and delay times under congested traffic conditions.  

 Traffic flow is a complex phenomenon and quite difficult to completely understand. Over the last fifty years, several modelling methods have been developed for vehicular traffic flow and categorised based on applicability, generability and accuracy (Hoogendoorn and Bovy, 2001).  Lu (1990) also emphasised the importance of the accuracy of models for traffic flow simulation. Brockfield et al. (2004) reported that the most difficult stage in the development and use of traffic flow models is the calibration and validation stage. Validation errors of some models are as high as 60 %. The difficulty is due to lack of suitable methods for adapting the models to empirical data.  

 Headway modelling is useful in the analysis of flow in a traffic stream (Chandra & Kumar, 2001). Highway capacity is usually determined by the minimum acceptable mean headway (Zhang et al., 2007 and Arasan and Koshy, 2003).

Headway is defined as the time between successive vehicles as they pass a point on a lane (Banks, 2003; Kyte & Teplay, 1999; Owolabi and Adebisi, 1996). It is usually measured in seconds. Headway measurement can be performed manually with a stopwatch and automatically with any presence-type detector or with video image processors (Salter, 1990). Headways are affected by such factors as traffic volume, ratio of large sized vehicles, road structure, daytime or night-time, and weather (Daisuke et al., 1999). 

Several studies have been carried out using headway modelling to analyse and solve specific traffic problems on highways (Akintayo and Agbede, 2009); Onibere et al. (1987); and Ovuworie (1980).  Hoogendoorn (2005) presented a new approach to estimating the distribution of free speeds using a composite time headway distribution model.  Haight et al. (1961) proposed a new statistical method for describing headway distribution of cars by classifying them as random, regular (equally spaced) or intermediate. Hossain and Iqbal (1999) found that in the flow range of 200-640 vph the exponential and log-normal distributions can best describe the headway pattern on two-lane, two-way highways. Owolabi and Adebisi (1993) found the composite exponential model to be a sound descriptor of observed headways along Zaria-Sokoto Road, Nigeria for flows ranging from 170 vph to 750 vph irrespective of whether or not motorcycles were in the traffic stream. Dawson and Chimini (1968) developed a generalised type headway model for single lane traffic flows on two-lane, single carriageways. Bham and Ancha (2006) proposed two shifted continuous distribution models, the lognormal and gamma models for preferred time headway and time headway of drivers in steady state car-following. Yuichi and Shizuma (1989) presented a practical method for estimating the headway distribution based on the experimentally observed data of the number of vehicles passing in a certain time interval theoretically as a general case of gamma-type headway distribution model. 

Parameters of headway distribution models are usually estimated from the field data. The field data must be reliable and the parameters must be properly estimated before the models can be applied.  Hagring (2000) highlighted three techniques usually employed in headway parameters estimation: the method of moments, the maximum-likelihood method and the least-squares method. As highlighted earlier, several researchers have used these techniques to develop simple mathematical models based on Poisson and Erlang distributions to estimate headway parameters for flows at low levels. Complex mathematical headway distribution models such as Lognormal, Pearson Type III and Hyperlang have been employed in parameter estimation of moderate and high traffic flow levels. However, for cases in which the random traffic-based Poisson does not hold or other mathematical headway distributions require great field measurements or do not fit the real-world data closely, researchers are increasingly developing simulation models to analyse and solve complex flow problems in engineering (Agbede, 1995; Kosonen, 1999).  Brockfield et al. (2007) reported that simulation models are becoming increasingly important tools in modelling transportation systems. Metcalfe (1997) explained that simulation techniques are useful in complex situations for which appropriate formulae are not known although they are far less convenient than mathematical models.  It is also important to apply appropriate and reasonable initial and boundary conditions to the simulation models to ensure reliability of output results (Agbede and Adegbola, 2003)

Lee et al. (2008) developed a simulation tool using stand-alone application which adopts object-oriented approach and JAVA as the main application programming interface (API) to forecast traffic congestion level. Zwahlen et al. (2007) suggested that it would be advantageous to convert hourly traffic counts into corresponding cumulative headway using the least-squares method. They employed this method to generate hyperbolic fit models to approximate headway distributions of free-flowing traffic on Ohio Freeways for work zone traffic simulations.  

     1.2       Research Problem

 In spite of the global economic recess, vehicle ownership is continuing to increase in cities of the world including Nigeria. The consequences of this  in Akure , where there is  no corresponding sustainable land use patterns and transportation schemes is traffic congestion. Dynamic traffic data capturing and analysis systems are necessary to assist the civil engineers on the improvement schemes to ameliorate the problem. However, the challenges and cost of these systems are enormous for Ondo  State and the eleven Local Government Areas constituting the Akure .

 Previous studies have shown that several mathematical traffic flow models developed to analyse congestion cannot be easily generalised or adapted to varying roadway, traffic and control conditions. In addition, validation errors of some models are as high as 60.0 %. In pursuit of the objective of minimising traffic congestion in parts of the Akure , headway simulation models were developed for the analysis of flow on some selected highways characterised by heavy traffic.

     1.3       Study Area

 Nigeria is connected by a network of roads as shown in Fig. 1.1. The two-lane roads form its major component particularly in Ondo  State.  Akure  is the capital of Ondo  State, one of the thirty-six states in Nigeria. The metropolitan area of Akure  is approximately on Latitudes 7o 15’and 7o 30′ North of the Equator; and Longitudes 3o 45′ and 4o 00′ East of the Greenwich Meridian (Ayeni, 2002).

 The road network connecting the eleven Local Government Areas in the metropolis (Fig. 1.2) is vast and central to the socioeconomic activities of the people. A network of the roads studied (Total Garden-Agodi Gate, J Allen-Oke Bola and Odo Ona-Apata) and some other principal roads in the metropolis are shown in Fig. 1.3.

Two of the roads studied, J Allen-Oke Bola and Odo Ona-Apata are sections of Obafemi Awolowo (formerly Lagos By-pass) and Akure -Abeokuta roads respectively. These roads are under the jurisdiction of the federal government. The Odo Ona-Apata road serves as a link to the Nigerian National Petroleum Corporation

(NNPC) depot in Akure .  The road also connects Akure  to Abeokuta, the Ogun State capital. 

 The J Allen-Oke Bola is a link road to the Central Business District (CBD) of the metropolis (Dugbe and environs). The third road, Total Garden-Agodi Gate is under the purview of the Ondo  State government. It links some areas in the metropolis with the University Teaching Hospital (UCH) and the Ondo  State Secretariat.  Traffic streams on J Allen-Oke Bola and Odo Ona-Apata roads are shown in Plates 1.1 and 1.2 respectively.

     1.4        Aim and Objectives

 The aim of this study is to formulate a rational procedure for minimising highway traffic congestion using germane traffic parameters such as headway and flow.

The objectives of this study are as follows:

  1. To determine the parameters that contribute to congestion of purposively selected roads in Akure .
  2. To develop models for representing and replicating the parameters.
  3. To evolve mechanisms for traffic flow enhancement and congestion reduction on the roads under study.

Fig. 1.1. Nigeria’s road network

Source: GEOATLAS (2011)

                 Fig. 1.2. Akure  metropolitan area’s road network

                 Source: Ayeni (2002)

Fig. 1.3. Network of some principal roads in Akure 

                 Source: Tele Atlas Africa (2007)   

  1. Total Garden-Agodi Gate road 
  • J Allen-Oke Bola road
  • Odo Ona-Apata road

 Plate 1.1. Traffic stream on J Allen-Oke Bola road 

 (14 January, 2009; before dualisation of the road)

 Plate 1.2. Traffic stream on Odo Ona-Apata road 

 (23 April, 2009; 10:12 a.m.)

     1.5       Justification  

 Traffic congestion is a common feature on highways in many cities of the world including Akure , Nigeria. Previous studies have shown that several mathematical traffic flow models developed to analyse congestion cannot be easily generalised or adapted to varying situations. In addition, validation errors of some models are as high as 60.0 %. There is therefore a need to formulate a rational procedure for minimising highway traffic congestion using germane traffic parameters such as headway and flow. The mechanisms should be able to enhance traffic flow and reduce congestion on the highways under study in Akure , Nigeria. The system should also be replicable and adaptable for efficient and effective management of other highways in many cities of the world.

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