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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:
- To determine the parameters that contribute to congestion of purposively selected roads in Akure .
- To develop models for representing and replicating the parameters.
- 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)
- 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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