Vehicle-Pedestrian crashes at Intersections in Dhaka city



Shakil Mohammad Rifaat1, Richard Tay2, *, Shariar Mohammad Raihan1, Abrar Fahim1, Shah Mostofa Touhidduzzaman1
1 Department of Civil Engineering, Islamic University of Technology, Gazipur-1704, Bangladesh
2 School of Business IT and Logistics RMIT University, Melbourne, Victoria, Australia 3000


Article Metrics

CrossRef Citations:
0
Total Statistics:

Full-Text HTML Views: 844
Abstract HTML Views: 439
PDF Downloads: 535
ePub Downloads: 376
Total Views/Downloads: 2194
Unique Statistics:

Full-Text HTML Views: 547
Abstract HTML Views: 253
PDF Downloads: 247
ePub Downloads: 140
Total Views/Downloads: 1187



© 2017 Rifaat et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the School of Business IT and Logistics RMIT University Melbourne, Victoria, Australia 3000, Tel: +61 3 9925 1438; Email: rtay888@gmail.com


Abstract

Background:

Pedestrians are some of the most vulnerable road users, especially in large congested cities in developing countries. In order to develop appropriate countermeasures to improve safety, research has to be conducted to understand the factors contributing to vehicle-pedestrian collisions.

Objective:

This study aims to identify the factors contributing to intersection crashes in a developing country context.

Method:

A Poisson regression model was applied to police reported crash data from the capital of Bangladesh, Dhaka.

Results:

This study finds that an increase in vehicle traffic and the presence of police officer, footbridge, bus stop, solar panel and waste deposit facility were associated with an increase in the number of vehicle-pedestrian crashes, whereas an increase in pedestrian volume, roads with the same number of inbound and outbound lanes, roads with greater number of lanes, and the presence of traffic signal, commercial area or offices, speed breaker and rail crossing were associated with a reduction in the number of vehicle-pedestrian crashes.

Conclusion:

While the results of most traffic and engineering factors are consistent with those obtained in previous studies in developed countries, some of the results on human related factors and unusual road furniture are atypical and require more locally targeted countermeasures.

Keywords: Pedestrian crashes, Intersection crashes, Developing country, Poisson regression.



INTRODUCTION

Background and Rationale

Around the world, more than 1.2 million people are killed on the roads every year and as many as 50 million others are injured, and over 90% of the deaths occur in the low and middle income countries [1]. Low and middle income countries also have a higher road traffic fatality rate (20.2 deaths per 100,000 population) than high income countries (12.6 deaths per 100,000 population). Moreover, the total number of road traffic deaths and injuries is forecasted to rise by 80% in low-income and middle-income countries between 2000 and 2020 compared to 65% worldwide. Also, the majority of traffic deaths in developing countries are vulnerable road users (pedestrians, cyclists and motorcyclists). Therefore, it is imperative that more effort be devoted to understanding the challenges and reducing the traffic injuries in developing countries, especially among the vulnerable road users.

Vehicle-pedestrian crash is a major road safety problem in many developing countries, including Bangladesh [2]. Dhaka, the capital of Bangladesh, is one of the most densely populated cities in the world and the state of pedestrian safety can be considered as alarming [3]. In one study, it is observed that pedestrians represent up to 72 percent of road traffic fatalities in the Dhaka Metropolitan area [4]. In Dhaka, pedestrians at intersections are more likely to be involved in these fatal crashes since a significant share of the total crashes occurred near the intersections [5-7]. It is observed that intersection crashes account for around 40 percent of total accidents occurring in the Metropolitan City of Dhaka [8]. Therefore, it is important to explore the factors affecting vehicle-pedestrian crashes to provide evidence based recommendations for improving the safety of these vulnerable road users.

Objective and Scope of Study

The objective of this study is to identify the factors contributing to the number of vehicle-pedestrian crashes at intersections in a developing country. This study will also contribute to advancing knowledge in the field because relatively few studies have been conducted in the developing country context, compared to developed countries [9]. Since the road environment and user behavior are very different, some of the risks factors identified in developed countries may have different effects in a developing country context.

In addition, this study will also explore many factors that have thus far received relatively little attention in the literature, including manual traffic controls and the presence of waste disposal facilities, speed breakers, solar panels, etc. Many of these features are quite unique to developing countries and are not found in most developed countries.

Literature Review

The literature on vehicle-pedestrian crashes is extensive. For example, one study collected pedestrian safety statistics at the global, regional and national levels, and studied the effect of several factors related to driver, roadway, vehicle and demography [10]. Another study examined the monthly pedestrian crashes patterns on urban roads in the American state of Connecticut [11]. Cottrill and Thakuriah [12] found that pedestrian crash frequency in the city of Chicago was related to exposure, crime rates and general population demographics. Aziz et al. [13] and Yasmin et al. [14] found road characteristics, traffic attributes and land use to be statistically significant in determining the severity of pedestrian crashes in New York City.

Several studies have specifically examined vehicle-pedestrian crashes at intersections. For example, Schneider et al. [15] examined the association between roadway characteristics and pedestrian crash risk in Alameda County in California while Pulugurtha and Sambhara [16] examined the influences of demographic, socioeconomic and land use characteristics on the frequency of pedestrian crashes at intersections in the American city of Charlotte. Femandes et al. [17] and Miranda-Moreno et al. [18] examined the frequency of vehicle-pedestrian crashes at intersections in the city of Montreal while Lyon and Persaud [19] examined the pedestrian crashes in the city of Toronto. Kim et al. [20] examined the factors determining pedestrian injury severity in the American state of North Carolina while Lee and Abdel-Aty [21] examined the severity of pedestrian crashes in Florida.

Although many studies have been conducted to understand the factors influencing vehicle-pedestrian crashes at intersections, most of these studies are conducted in developed countries where the roads are better designed, the traffic is better separated and controlled, and traffic rules are generally obeyed. On the contrary, road environment and user behaviors are very different in many cities in the developing countries [22-28].

Road Environment and User behavior in Dhaka City

Like many large cities in the developing world, the roadway and traffic conditions in Dhaka city are significantly different from many cities in the developed world. For example, there is a much higher share of non-motorized traffic (pedal bicycles, rickshaws, pushcarts, animals, etc.) on many roads (Fig. 1a) and restrictions on non-motorized traffic have to be imposed on some of the intersections. Most of the pedestrian footpaths are occupied by vendors and extension of roadside shops (Fig. 1b), illegally parked vehicles, refuse, other roadside furniture and motorcycles. Thus, pedestrians are often forced to use the road instead of the allotted pedestrian pathways, which will increase the chances of conflict with vehicles. Pedestrian crossing facilities (e.g., zebra crossing, underpass and overpass) are inadequate, and when available, rarely used due to inconvenience and poor law enforcement.

Fig. (1). Road and traffic conditions in Dhaka city.

Also, the sight distance at the approach roads are often restricted by sculptures, advertisement billboards, solar panels (Fig. 1c) and waste disposal facilities (Fig. 1d). Moreover, side roads and gas stations are often present close to intersections, and many of them carry heavy traffic, which contribute to significant traffic jams and congestion. Since traffic congestion is a common phenomenon in Dhaka, drivers often have to take more risks due to insufficient clearance time at intersections. Interestingly, the traffic flow in many intersections at Dhaka city, even signalized intersections, is manually controlled by traffic police (Fig. 1e). It should also be noted that many of the drivers are not educated, not properly trained and are often ignorant about the road rules and regulations [29]. Similarly, many pedestrians often ignore safety rules and exhibit risky walking and crossing behaviors (Fig. 1f).

MATERIALS AND METHODS

Statistical Method

The aim of this study is to explore the factors contributing to the frequency of crashes at intersections in a developing country. Researchers have used various statistical techniques to model accident frequencies (Lord and Mannering [30] for a review of the various techniques). Since the number of crashes is a count variable, the Poisson regression model is an appropriate technique to use [31-37]. However, the Poisson model, assumes that the mean is equal to its variance and this assumption is often violated in practice. Hence, a more general model, namely the negative binomial model, has been employed by many researchers to account for over-dispersion in the data [18, 38-41] while some researchers have used the Sichel model [42]. In this study, the Poisson regression model was used because the dependent variable was a count variable. The Poisson model was selected over the negative binomial model because over-dispersion was found to be statistically insignificant (p=0.500) in the data.

Let 'y' be the number of crashes at an intersection, then the Poisson regression model is given by:

(1)
(2)

where P is the probability of observing y crashes at an intersection

λ is the mean of the distribution

β 0, β1,..., βk are parameters to be estimated

x1,..., xk are explanatory variables

The above model was estimated using the maximum likelihood method. It should be noted that several mixed-Poisson and random parameters Poisson models have recently been applied to account for heterogeneity [42-46]. The random parameters model assumed that some or all of the parameters may be random because of unobserved variations or unobserved heterogeneity [43, 44]. However, preliminary analyses found no significant unobserved heterogeneity (no statistically significant scale parameter or variance for all coefficients). Hence, the fixed parameters Poisson regression model was used to estimate the final model.

Data

Vehicle-pedestrian crash data from 2011 to 2013 at 45 intersections in Dhaka city were provided by the Accident Research Institute at the Bangladesh University of Engineering and Technology. These intersections have been termed as the hazardous intersections by traffic police in Dhaka city corporation road network. In this study, an intersection crash included all crashes within 20m of an intersection. In order to collect relevant information on geometric features, roadside environment, traffic operations and neighborhood land use, a thorough in-situ investigation was performed by the research team at every intersection.

The descriptive statistics of the 13 variables used in the final model were summarized and presented in Table 1. Correlations between variables have been tested and no significant relationship was observed between the independent variables. In this study, any variable with a 90% confidence level was considered to be marginally significant and retained in the models.

Table 1. Descriptive statistics.
Variables Mean SD Min Max
Number of pedestrian crashes 1.600 2.168 0 10
Traffic volume per hour
  Vehicle volume 1451.111 777.128 180.9 3540
  Pedestrian volume 374.207 202.439 27 894
Traffic control
Signal 0.800 0.405 0 1
Police 0.867 0.344 0 1
Number of lanes
  Maximum number 3.222 0.735 2 5
  Same inbound-outbound 0.311 0.468 0 1
Nearby land use
  Office area 0.444 0.503 0 1
Presence of infrastructure and furniture
  Foot over bridge 0.511 0.506 0 1
  No bus stop 0.622 0.490 0 1
  Speed breaker 0.222 0.420 0 1
  Rail crossing 0.111 0.318 0 1
  Solar power 0.489 0.506 0 1
  Waste deposit facility 0.267 0.447 0 1

RESULTS

The estimation results from the final model were summarized and are reported in Table 2. In general, the model fitted the data fairly well, with large chi-square statistics for goodness of fit, fairly high McFadden pseudo R-square and very small p-values.

Table 2. Estimation results of final model.
Number of Observations = 45
Log likelihood function = -60.28
P-value < 0.001
Chi-squared statistic = 63.66
McFadden Pseudo R-squared = 0.35
Variables Coefficient Std. Err. t-statistic p-value
Traffic volume
  Vehicle volume * 0.000786 0.000281 2.79 0.005
  Pedestrian volume -0.001537 0.000845 -1.82 0.069
Traffic control
Signal * -2.180 0.579 -3.77 0.000
Police * 2.038 0.777 2.62 0.009
Number of lanes
  Maximum number of lanes -0.451 0.254 -1.78 0.075
Same inbound-outbound * -1.053 0.386 -2.73 0.006
Nearby land use
  Office area -0.683 0.393 -1.74 0.082
Presence of road infrastructure in or near intersection
Foot over bridge * 1.227 0.432 2.84 0.004
No bus stop * -0.609 0.296 -2.06 0.039
Speed breaker -0.655 0.408 -1.60 0.109
  Rail crossing * -1.087 0.484 -2.25 0.025
Solar power * 1.125 0.343 3.28 0.001
  Waste deposit facility * 0.887 0.2895 3.07 0.002

DISCUSSION

Consistent with previous studies [15-19], an increase in the vehicle traffic was found to be associated with an increase in crashes. However, in contrast to previous studies [15-18], an increase in pedestrian volume was found to be associated with a reduction in crashes. The difference in our result might be due to the safety in number effect [47] and the difference in behaviors between pedestrians in developed and developing countries, with higher prevalence of pedestrian jaywalking. Drivers might be induced to drive more carefully in areas with high pedestrian volume.

As expected, this study found that signalization was negatively associated with the number of vehicle-pedestrian crashes. This result was consistent with Garder [48] who found that the installation of traffic signals was associated with a reduction in vehicle-pedestrian conflicts at 115 urban intersections in Sweden, and Lee and Abdel-Aty [21] who found that signal control was associated with a reduction in vehicle-pedestrian crashes in Florida.

Interestingly, we found that intersections controlled by police were associated with an increase in crashes. One possible reason might be police presence would be more likely at intersections with high crash risks. Another possible reason might be the poor signal timings in developing counties, including those in Dhaka city, which would result in traffic police officers controlling traffic at intersections, including signalized intersections and roundabouts (Fig. 1e). This multiple controls at the same intersection might lead to an increase in congestion as well as confusion and risk-taking behaviors by both drivers and pedestrians [49, 50].

Our results showed that having the same number of in-bound and out-bound lanes was associated with fewer crashes. Similarities of approach roads would have beneficial effects from a pedestrian safety perspective. With the same number of lanes for both the inbound and outbound traffic at an intersection, drivers might have a less need to change lanes. This reduction in weaving activities near the intersection would reduce the potential for both vehicle-vehicle and vehicle-pedestrian conflicts.

An increase in the maximum number of lanes in any of the approach roads at an intersection was found to be associated with a reduction in vehicle-pedestrian crash frequency. This result was in contrast to Femandes et al. [17] who found that an increase in the total number of lanes was associated with an increase in the frequency of vehicle-pedestrian crashes. Although an increase in the number of lanes would increase the distance the pedestrians have to walk to cross the intersection, they might compensate for the increased risk by being more cautious about their safety and reduce their tendency to jaywalk.

With regards to nearby land use, office buildings were found to be associated with a decrease in the number of vehicle-pedestrian crashes. This result was in contrast to Schneider et al. [15] and Femandes et al. [17] who found the presence of commercial land use was associated with an increase in the frequency of vehicle-pedestrian crashes. The presence of office buildings near intersections might indicate an increase in the pedestrian traffic streams and result in a greater awareness by drivers of the presence of pedestrians. This result reinforced our previous finding that an increase in pedestrian volume was negatively associated with vehicle-pedestrian crashes.

As expected, the presence of a speed breaker (road hump) near an intersection was found to be associated with a decrease in the number of pedestrian-vehicle crashes. Theoretically, a speed breaker would reduce the speed of vehicles and thus, reduce the likelihood and frequency of crashes.

The presence of overhead bridge was found to be associated with an increase in the number of vehicle-pedestrian crashes at an intersection. This finding was in contrast to previous research from Japan that found a decrease in vehicle-pedestrian crashes [51]. However, it was consistent with Mutto et al. [52] who found an increase in vehicle-pedestrian crashes in Kampala, Uganda. The differences in the results between developed and developing countries might be due to the differences in pedestrian behaviors (not using the overhead bridge in developing countries) and/or differences in driver behaviors (not slowing down because of the presence of overhead bridge in developing countries).

In this study, we found that the presence of railway was associated with a decrease in pedestrian-vehicle crashes. This result was in contrast to previous studies [53, 54] that found the presence of railway crossing was a contributing factor towards vehicle-pedestrian crashes. Although railway crossings might be hazardous locations, they would also induce drivers to slow down and drive more carefully.

In contrast to most developed countries, in Dhaka city, waste disposal facilities would often be placed on the roads (Fig. 1d) and this study found that its presence was associated with an increase in vehicle-pedestrian crashes. This result was expected because the placement of large waste bins on the outer lane of a road would pose a safety threat, especially to pedestrians who might be forced further into the roads. Also, the waste disposal facility might not be properly maintained and serviced, and the waste would often spill and scatter onto the roads. This might further narrow the effective lane width. Moreover, drivers might increase their speed to avoid this eyesore and accompanying odor, which might elevate the crash risks. In addition, the waste disposal facility and any waste collection truck servicing them would often restrict the drivers’ vision and reduce their perception reaction times.

Solar panel would be another unusual on-road furniture that could be found in some developing countries (Fig. 1c) but not in most developed countries. As expected, the presence of solar system was found to be associated with an increase in the number of pedestrian crashes at intersections. These hazards would reduce the sight distance and vision of drivers, as well impede pedestrian movements and forced pedestrians to walk around them.

The absence (presence) of bus stop near an intersection was found to be associated with a decrease (increase) in vehicle-pedestrian crashes. This result was in consistent with Diogenes and Lindau [55] who found that crossings located close to bus stops experienced higher pedestrian crash rates.

CONCLUSION

The objective of the study was to identify the factors affecting vehicle-pedestrian crashes at intersections in Dhaka city. Our study found that an increase in vehicle traffic and the presence of police officer, footbridge, bus stop, solar panel and waste deposit facility were associated with an increase in the number of vehicle-pedestrian crashes, whereas an increase in pedestrian volume, roads with the same number of inbound and outbound lanes, roads with greater number of lanes, and the presence of traffic signal, commercial area or offices, speed breaker and rail crossing were associated with a reduction in the number of vehicle-pedestrian crashes.

Some countermeasures that may be effective in reducing vehicle-pedestrian crashes at urban intersections in Dhaka and other large cities in developing countries include the installation and proper optimization of traffic signal, and relying on automated signaling rather than manual signaling controlled by traffic police officers. Other potential countermeasures include equalizing the number of in-bound and out-bound lanes, installing marked pedestrian crossings at intersections located near large commercial or office buildings, removing of waste deposit facility and solar panel from the road to safer locations, installation of speed breakers and other traffic calming devices, and on site communication or education campaigns to encourage the use of footbridges and increase the awareness of safety hazards around bus stops.

CONFLICT OF INTEREST

The authors confirm that this article content has no conflict of interest.

ACKNOWLEDGEMENTS

The authors would like to thank the Accident Research Institute in Bangladesh University of Engineering and Technology for providing the intersection crash data.

REFERENCES

[1] World Report on Road Traffic Injury Prevention. Geneva: World Health Organization 2004.
[2] Sarkar S, Tay R, Hunt J. Pedestrian crashes on national highways in Bangladesh: causative factors and trends. Transp Res Rec 2011; 2264: 128-37.
[3] Barua U, Tay R, Hoque M, Mamun M. Analysis of pedestrian safety on five major arterial roads in Dhaka In: Can. Transp. Res. Forum; Bangladesh. 2007.
[4] Rahman R, Mahmud S, Hoque M, Ahmed S. Pedestrian Accident Characteristics in Dhaka: A Comparative Investigation In: Proceedings of the International Conference on Road Safety in Developing Countries. 2006; pp. 84-90.
[5] Anowar S, Yasmin S, Tay R. Factors Influencing the Severity of Intersection Crashes in Bangladesh. Asian Transp Stud 2014; 3(2): 1-12.
[6] Barua U, Tay R. Severity of intersection crashes in Bangladesh, Road Safety in Four Continents Conference.
[7] Sarkar S, Tay R. Modeling of accidents for in-service review of intersections of national highways in Bangladesh, International Co-operation on Theories and Concepts in Traffic Safety.
[8] Rahman S. Development of an accident prediction model for intersections of Dhaka city, Bangladesh. Int J Comput Appl 2012; 47(16): 10-6.
[9] Barua U, Tay R. Severity of urban transit bus crashes in Bangladesh. J Adv Transp 2010; 44(1): 34-41.
[10] Zegeer CV, Bushell M. Pedestrian crash trends and potential countermeasures from around the world. Accid Anal Prev 2012; 44(1): 3-11.
[11] Serhiyenko V, Ivan JN, Ravishanker N, Islam MS. Dynamic compositional modeling of pedestrian crash counts on urban roads in Connecticut. Accid Anal Prev 2014; 64: 78-85.
[12] Cottrill CD, Thakuriah PV. Evaluating pedestrian crashes in areas with high low-income or minority populations. Accid Anal Prev 2010; 42(6): 1718-28.
[13] Aziz HM, Ukkusuri SV, Hasan S. Exploring the determinants of pedestrian-vehicle crash severity in New York City. Accid Anal Prev 2013; 50: 1298-309.
[14] Yasmin S, Eluru N, Ukkusuri S. Alternative ordered response frameworks for examining pedestrian injury severity in New York City Journal of Transportation Safety and Security 2013; 6(4): 275-300.
[15] Schneider R, Diogenes M, Arnold L, Attaset V, Griswold J, Ragland D. Association between roadway intersection characteristics and pedestrian crash risk in Alameda County. California: Annual Meeting of the Transportation Research Board 2010.
[16] Pulugurtha SS, Sambhara VR. Pedestrian crash estimation models for signalized intersections. Accid Anal Prev 2011; 43(1): 439-46.
[17] Femandes D, Miranda-Moreno L, Morency P. P. Vehicle-pedestrian accidents at signalized intersections: exposure measures and geometric designs. 92nd Annual Meeting of the Transportation Research Board.
[18] Miranda-Moreno LF, Morency P, El-Geneidy AM. The link between built environment, pedestrian activity and pedestrian-vehicle collision occurrence at signalized intersections. Accid Anal Prev 2011; 43(5): 1624-34.
[19] Lyon C, Persaud B. Pedestrian collision prediction models for urban intersections. Transp Res Rec 2002; 1818: 102-7.
[20] Kim JK, Ulfarsson GF, Shankar VN, Kim S. Age and pedestrian injury severity in motor-vehicle crashes: a heteroskedastic logit analysis. Accid Anal Prev 2008; 40(5): 1695-702.
[21] Lee C, Abdel-Aty M. Comprehensive analysis of vehicle-pedestrian crashes at intersections in Florida. Accid Anal Prev 2005; 37(4): 775-86.
[22] Sarkar S, Hoque S, Tay R. Development of road safety audit review prompt list for intersections of national highways in Bangladesh using severity reduction principle, . Transportation Research Board Annual Meeting; 2010.
[23] Anowar S, Yasmin S, Tay R. Traffic accidents and injuries: Turning the festivities of Eids into mourning. International Co-operation on Theories and Concepts in Traffic Safety 2009.
[24] Anowar S, Barua U, Yasmin S, Tay R. Fatality analysis of intersection accidents in Bangladesh. Abu Dhabi: Road Safety in Four Continents 2010.
[25] Yasmin S, Anowar S, Tay R. Pedestrian accidents and injuries: The context of Dhaka City. International Co-operation on Theories and Concepts in Traffic Safety 2009.
[26] Yasmin S, Barua U, Anowar S, Tay R. Fatality analysis of heavy vehicle accidents in Bangladesh. Abu Dhabi: Road Safety in Four Continents 2010.
[27] Barua U, Tay R. Crash severity of motorized three wheelers in Bangladesh Australia Road Research Board Conference.
[28] Sarkar S, Tay R. Prioritizing road safety funding among geographic regions using data envelopment analysis. Transportation Research Board Annual Meeting; 2009.
[29] Leema F, Tay R. Efficacy of mandatory driver testing in Bangladesh Canadian Multidisciplinary Road Safety Conference.
[30] Lord D, Mannering F. The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transp Res Part A Policy Pract 2010; 44: 291-305.
[31] Jovanis P, Chang H. Modelling the relationship of accidents to miles travelled. Transp Res Rec 1986; 1068: 42-51.
[32] Wong SC, Sze NN, Li YC. Contributory factors to traffic crashes at signalized intersections in Hong Kong. Accid Anal Prev 2007; 39(6): 1107-13.
[33] Tay R. Deterrent effects of drink driving enforcement: Some evidence from New Zealand. Int J Transp Econ 2005; 32(1): 103-9.
[34] Tay R. Ageing drivers: storm in a teacup? Accid Anal Prev 2006; 38(1): 112-21.
[35] Tay R. Marginal effect of increasing ageing drivers on injury crashes. Accid Anal Prev 2008; 40(6): 2065-8.
[36] Tay R. Speed cameras: Improving safety or raising revenue? J Transp Econ Policy 2010; 44(2): 247-57.
[37] Tay R. Fatal crashes involving young male drivers: a continuous time Poisson change-point analysis. Aust N Z J Public Health 2001; 25(1): 21-3.
[38] Miaou SP. The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. Accid Anal Prev 1994; 26(4): 471-82.
[39] Rifaat S, Tay R, de Barros A. Effect of street pattern on road safety: Are policy recommendations sensitive to different aggregations of crashes by severity? Transp Res Rec 2010; 2147: 58-65.
[40] Tay R. Drink driving enforcement and publicity campaigns: are the policy recommendations sensitive to model specification? Accid Anal Prev 2005; 37(2): 259-66.
[41] Tay R. The relationship between public education and enforcement campaigns and their effectiveness in reducing speed related serious crashes. Int J Transp Econ 2004; 31(2): 251-5.
[42] Zou Y, Lord D, Zhang Y, Peng Y. Comparison of Sichel and Negative Binomial Models in Estimating Empirical Bayes Estimates. Transp Res Rec 2013; 2392: 11-21.
[43] Anastasopoulos PCh, Mannering FL. A note on modeling vehicle accident frequencies with random-parameters count models. Accid Anal Prev 2009; 41(1): 153-9.
[44] El-Basyouny K, Sayed T. Accident prediction models with random corridor parameters. Accid Anal Prev 2009; 41(5): 1118-23.
[45] Zou Y, Zhang Y, Lord D. Application of finite mixture of negative binomial regression models with varying weight parameters for vehicle crash data analysis. Accid Anal Prev 2013; 50: 1042-51.
[46] Mannering FL, Shankar V, Bhat CR. Unobserved heterogeneity and the statistical analysis of highway accident data. Anal Methods Accid Res 2016; 11: 1-16.
[47] Jacobsen PL. Safety in numbers: more walkers and bicyclists, safer walking and bicycling. Inj Prev 2003; 9(3): 205-9.
[48] Gårder P. Pedestrian safety at traffic signals: a study carried out with the help of a traffic conflicts technique. Accid Anal Prev 1989; 21(5): 435-44.
[49] Laskar M. Traffic signal system in hopeless mess 2008.21 December; Bangladesh 2day. 2008.
[50] Mahmud A. Digital traffic control clashes with police!, Dhaka Tribune: Bangladesh, 3 April, 2014.
[51] Accident Prevention Effects of Road Safety Devices. Tokyo: Japan Road Association 1969.
[52] Mutto M, Kobusingye OC, Lett RR. The effect of an overpass on pedestrian injuries on a major highway in Kampala - Uganda. Afr Health Sci 2002; 2(3): 89-93.
[53] Read G, Salmon P, Lenne M, Stanton N. Walking the line: Understanding pedestrian behaviour and risk at rail level crossings with cognitive work analysis In: Applied Ergonomics [Online] Available:. 2015. assessed Oct. 27, 2016
[54] ATC. National Railway Level Crossing Strategy 2010–2020. Canberra: Australian Transport Council 2010.
[55] Diogenes M, Lindau L. Evaluation of Pedestrian Safety at Midblock Crossings, Porto Alegre, Brazil. Transp Res Rec 2010; 2193: 37-43.