Identifying the Factors and Locations of Traffic Crash Severity of Dhaka

A Thesis

entitled

Metropolitan Area, Bangladesh, 2007-2011.

Panini Amin Chowdhury

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the

Master of Arts Degree in Geography

______Bhuiyan M. Alam, Committee Chair

______Daniel J. Hammel, Committee Member ______Yanqing Xu, Committee Member

______Amanda C. Bryant-Friedrich, Ph.D. Dean, College of Graduate Studies

The University of Toledo

December 2018

This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author.

An Abstract entitled Identifying the Factors and Locations of Traffic Crash Severity of Dhaka Metropolitan Area, Bangladesh, 2007-2011. by Panini Amin Chowdhury

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Arts Degree in Geography

The University of Toledo December 2018

This study aims at exploring and analyzing different roadway, environmental and traffic factors that influence traffic crash severity and their spatial distributions in Dhaka city,

Bangladesh. Using a multinomial logit model, the author regressed 12 roadways, environmental, and traffic factors on traffic crash severity. The study uses 2716 crash records that occurred over a period of 2007-2011. The study collected the data from Dhaka

Metropolitan Police Department. The method of the study includes three steps. In the first step, this study presents a general descriptive statistic of the variables. The second step introduces the results of multinomial logistic regression model to help understand the impacts of different variables on traffic crash severity. Finally, this study developed a spatial analysis for the identifications of the vulnerable crash points and their relationship with the land use and population of those wards (Township equivalent). The study finds that 69% of the crashes were fatal. Seventy one percent of the crashes occurred at non- intersection locations, 63% occurred at uncontrolled or not prior supervised traffic intersections, 73% occurred on one-way or single direction roads, and 80% took place at

iii

road segments with presence of a road divider. The study also found pedestrian (60%) and rear-end (25%) collisions to be the most common types of collision. Weather and daylight conditions do not play significant role on reducing crashes. The author also finds that 96% of the crashes took place on straight roads, of which 67% occurred on the national highways. The majority (98%) of the crashes took place on roads that were not in poor condition. The most crash-prone period of the year is shown to be March–June. Variance

Inflation Factors (VIF) and correlation coefficients were performed to test if there was multicollinearity among the explanatory variables. The overall model is statistically significant with the log likelihood value of 1044.67 and pseudo r-squared value of .37. The no intersection or general road segments and 3-way intersection points are more susceptible to fatal crashes than extensive injuries. Police controlled intersections have a positive impact in reducing crashes. Pedestrian, rear end and head-on collisions are found to be the most significant collision type. One-way roads are more vulnerable to crashes. Road dividers have a positive impact on crash reduction. Night time is more vulnerable to fatal crashes while extensive or minor injuries are more common during day time. National and regional highways are more vulnerable to crashes than the city or feeder roads. Speed breakers have an impact on reducing the fatal crashes. From the spatial hotspot analysis, it is found that there is no significant cold spot in the whole DMA with respect to traffic crashes. Crash points are highly clustered. The north, east and south side of the metropolitan area are more vulnerable to road traffic crashes than the western part of the city. Wards with moderate-high population and mixed (residential-commercial, commercial- industrial) land use are more crash-prone than the dedicated residential or educational zones. iv

Acknowledgements

I am deeply grateful to my supervisor Dr. Bhuiyan M. Alam for his dedication and support throughout my research. This study would not have been possible without his guidance and help. This journey allows me to learn his ways of thinking and working and I am often impressed by his logical thoughts and wise solutions to difficult research questions. I also want to thank him for his useful comments and remarks on my writings and formats. I am honored to have Dr. Daniel J. Hammel and Dr. Yanqing Xu to be my dissertation committee members. I also want to thank Dr. Bayes Ahmed for sharing his database with me. I am thankful to my father and sister because without their continuous support, it would not be possible. I thank you all for your time and effort for my dissertation. Finally, I would like to thank my Beautiful wife “Daizy” for making my life and my works more eloquent.

Thank you, my “Wonder Woman.”

Table of Content

Abstract ...... Error! Bookmark not defined.

Acknowledgement ...... v

Introduction ...... 1

1.1 Background of the Study ...... 1

1.2 Rationale of the Study ...... 5

1.3 Goal & Objective ...... 6

1.5 Limitation of the Study ...... 6

Literature Review...... 8

Research Design & Data Management ...... 16

3.1 Introduction ...... 16

3.2 Background of the study ...... 16

3.3 Study Area ...... 17

3.4 Data Collection & Processing ...... 19

3.4.1 Crash Data Collection (2007-2011) ...... 19

3.4.2 Data Classification ...... 20

3.4.3 Spatial Data Collection ...... 24

3.5 Techniques of Research ...... 26

3.5.1 Multinomial Logistic Regression Analysis ...... 26

3.5.2 Predicting & Outcome Variables ...... 28

3.5.3 Network-Based Kernel Density Estimation ...... 29

3.5.4 Optimized Hotspot Analysis ...... 31

3.5.5 Spatial Autocorrelation Analysis ...... 32

Results ...... 34

4.1 Descriptive Statistics ...... 34

4.1.1 Road Crashes in the Major Cities of Bangladesh ...... 34

4.1.2 Crash Trend of Dhaka Metropolitan Area...... 35

4.1.3 Crash Statistics of Dhaka Metropolitan Area 2007-2011 ...... 36

4.1.4 Crash Severity...... 38

4.1.5 Intersection Type of the Crashes ...... 39

4.1.6 Traffic control system of the Crash Locations ...... 40

4.1.7 Collision Type ...... 41

4.1.8 Traffic Flow Direction in the Location of the Crashes ...... 42

4.1.9 Impact of Road Divider ...... 43

4.1.10 Weather Condition of the Crash Time ...... 44

4.1.11 Lighting Condition ...... 45

4.1.12 Road Geometry ...... 46

4.1.13 Temporal Trend of the Crashes ...... 47

vii

4.2 Multinomial Logistic Regression ...... 49

4.2.1 Traffic Factors ...... 53

4.2.2. Lighting & Environmental Factors ...... 55

4.2.3. Roadway Factors ...... 56

4.3 Spatial Analysis of the Traffic Crash Severity of Dhaka Metropolitan Area (2007-

2011)...... 57

4.3.1 Fatal Crashes...... 57

4.3.2 Extensive or Minor Injury Related Crashes ...... 59

4.3.3 Motor Vehicle Collision ...... 61

4.3.4 Hotspot Analysis of the Traffic Crash Locations ...... 63

4.3.5 Spatial Auto Correlation Analysis ...... 64

4.3.6 Network Based Kernel Density Estimation ...... 65

4.3.7 Relationship among the Land use pattern, population, area, and crash

frequencies ...... 68

Conclusions & Policy Implications ...... 71

5.1 Recommendations and Policy Implications ...... 75

5.1.1 Design and Infrastructural Level ...... 75

5.1.2 Policy Level ...... 76

5.1.3 Educational Level ...... 77

References ...... 80 viii

List of Tables

3. 1 Summary of the Variables in Model ...... 21

4. 1 Summary table of the overall Crash situation of Dhaka Metropolitan Area 2007-

2011...... 37

4. 2 Temporal Trend of the pedestrian crashes ...... 48

4. 3 Estimation of Parameters for Crash Severity Through Multinomial Logistic

Regression ...... 50

4. 4 Spatial autocorrelation analysis ...... 65

4. 5 Correlation matrix ...... 68

4. 6 Land use pattern of the vulnerable wards ...... 69

List of Figures

1. 1 Predicted Percentage of Change of the traffic crash related victims in different part of the World ...... 2

1. 2 Percentage of Fatal crash victims by different mode of transportation in South

Asia,2013 ...... 3

1. 3 Vehicle registration vs. accident,casualites and vehicle ownership change trend for

Bangladesh ...... 4

3. 1 Study Area ...... 18

4. 1 Crash rates in the important cities of Bangladesh ...... 39

4. 2 Crash trend comparison between Bangladesh and Dhaka metropolitan area ...... 40

4. 3 Crash Severity of Dhaka Metropolitan Area (Per 10,000 crashes) ...... 42

4. 4 Percentage of the Crash intensities ...... 42

4. 5 Intersection types of the crashes ...... 43

4. 6 Traffic control system ...... 44

4. 7 Collision types ...... 44

4. 8 Traffic flow movement ...... 45

4. 9 Presence of road dividers ...... 46

4. 10 Weather condition ...... 46

4. 11 Lighting condition ...... 47

4. 12 Road geometries...... 48

4. 13 Temporal trends of the crashes ...... 48

4. 14 Fatal Crash Locations of Dhaka Metropolitan Area,2007-2011 ...... 57

4. 15 Extensive or minor injury related crashes of Dhaka Metropolitan Area 2007-2011 58

4. 16 Motor Collision related crashes of Dhaka Metropolitan Area, 2007-2011 ...... 59

4. 17 Hotspot analyses of the crash points ...... 60

4. 18 Network based kernel density estimation (Linear representation) ...... 62

4. 19 Network based kernel density estimation (Point representation) ------63

Chapter 1

Introduction

1.1 Background of the Study

On a global scale, more than 5 % of deaths and a substantial proportion of injuries are due to road crashes (Huda, 2010). Among the age group of 5-29 years, it is considered the second largest cause of death while in the age of 30-44 it is the third (World Report, 2004).

Yearly, over 6 million traffic crashes with a mortality number of 40,000 occur in the United

States alone (GES, 2005). The most common of these crash points are the intersections. In the USA, around 40 % of the crashes take place in an intersection while in Canada and

Singapore the percentage is 30 (Tay & Rifaat 2007). In Netherlands, 44 % of the registered crash casualties occurred in an intersection (IRSR,2010). According to the figure 1.1, on a global scale, 90% of crashes occur in the low and middle-income countries which have half of the world's total registered vehicles (WHO,2011). South Asia is predicted to have a

144% increase in traffic crash victims by year 2020 while North America will be in a decreasing trend and Europe will have a slight increase of 18% (WHO,2015).

PREDICTED PERCENTAGE OF CHANGE OF THE TRAFFIC CRASH VICTIMS IN DIFFERENT

REGIONS OF THE WORLD (2000-2020)

144%

80% 80%

VICTIMS

68%

48%

18% PERCENTAGE OF CHANGE ACCIDENT CHANGE OF IN PERCENTAGE

S O U T H E A S T A S I A AFRICA M I D D L E L A T I N E U R O P E N O R T H

ASIA E A S T A M E R I C A AMERICA 28% TRAFFIC ACCIDENT REGIONS -

Figure- 1. 1 Predicted Percentage of Change of the traffic crash related victims in various

parts of the World

For a developing and overpopulated country like Bangladesh, road crashes and injuries are very common causes of death, with a rate of 100 deaths per 10,000 vehicles. This is an alarming number in comparison to its neighboring countries like India (25.3) or Srilanka

(16) (WHO, 2007; Ahsan,2012). Among the South Asian countries, Bangladesh has a very high percentage of 4-wheeled vehicle and pedestrian crashes.

Percentage of fatal crash victims By Different Mode of Transportation In South Asia 100

0 Bangladesh India Srilanka Bhutan Maldives Percentage Percentage of Victims Modewise South Asian Countries

4 Wheeled Vehicles 2 or 3 wheelers Cyclists Pedestrians Others or unspecified

Figure- 1. 2 Percentage of Fatal crash victims by different mode of transportation in

South Asia,2013

Source (WHO,2015)

Urbanization and presence of motorized vehicles are two main reasons behind this situation

(Rahman, Et. Al,1998). However, the number of car owners is still not high. In fact, only

3 people per 1000 now avail personal car in Bangladesh (World Bank, 2016). Yet it has one of the most congested road networks in Asia (Ministry of Communication and planning commission, 2008). The majority of the population is still relying on walking and rickshaw

(a three-wheeled human-pulled non-motorized vehicle) as their daily commute mode

(Ministry of Forest & Communication, 2010). These modes, however, are the most vulnerable to road traffic crashes here (Status Paper WHO, 2007). In this study, Dhaka is selected as a study area because of its high and unique rate of crash rates in comparison to

other developing and developed countries (Kamruzzaman et al., 2014). In this same study, it was found that 61% of the crashes in this locality occurs due to the pedestrian-vehicle collision. It is a mega city with a population of 14 million and is projected to touch 22-25 million in the next three years (Ahmed, 2013).

Vehicle Registration Vs. Accident, Casualities, and Vehicular Ownership Change trend for Bangladesh 700 14 No. Of Accidents Per 10000 600 12 New vehicle Registration 500 10 400 8 Casualities Per 10000 New vehicle Registration 300 6 200 4 Population No. of register owner for new

vehicle vehicle Registration 100 2 car per 10000 Population 0 0 2007 2008 2009 2010 2011 Expon. (No. Of Accidents Per 10000 New vehicle Years of the Study Period No.of Registered Vehicle Owners Per 10000 No.of Accidents and Casualities per 10000 New Registration)

Figure- 1. 3 Vehicle registration vs. accident,casualites and vehicle ownership change

trend for Bangladesh

Source- BBS, BRTC, ARI,2013

Though the number of registered owners of new cars per 10000 population in Bangladesh is increasing in every year, the casualties and number of crashes are in a descending trend.

But these increased number of vehicles are the principal cause of traffic congestion and disrupts the mobility comfort of the city dwellers. This increasing trend of vehicular ownership and decreasing trend of casualties creates a complex situation about the future mobility of this country.

Dhaka, the capital of Bangladesh, has more than 14 million people, and it has the highest level of car ownership in the country. As non- motorized modes are predominant here, pedestrians and users of nonmotorized vehicles are the most vulnerable groups on the road network of Dhaka Metropolitan Area. About 61% of crash victims in this locality are those pedestrians (UNES,2007). These crashes, both pedestrian and non-pedestrian, are dependent on various spatial, roadway and environmental factors. This study aims to discover some of the traffic, road way and environmental factors which play a key role in the crash frequencies, intensities and how they affect one another through descriptive analysis and econometric modeling. The study also carries out an analysis of the spatial orientation of these crashes and the spatial relationship among these crash locations. As a study area, this study selected Dhaka Metropolitan Area with a timeline from 2007-2011.

1.2 Rationale of the Study

Crash studies are mostly focused on the developed countries for a prolonged period. There have been a very negligible amount of studies that are available from the developing and underdeveloped countries. The motivation of this study lies in that issue. This study also plays a significant role in the future infrastructural and transportation development of

Dhaka Metropolitan Area. With respect to Bangladesh, it is vital because of its centralized government system. The whole administrative and financial system of this country is concentrated in its capital Dhaka alone, which amplifies the necessity even more.

1.3 Goal & Objective

The primary goal of this study is to understand the impact of different factors causing traffic crashes. Apart from that this research also concentrates on the spatial orientation of these crash locations in the study area. The specific objectives of this study are:

1. Develop a descriptive study of the traffic crash situation of the study area in a period

2007-2011.

2. Develop a multinomial logistic regression model to understand the relationship between different traffic, road ways and environmental factors with crash intensity.

3. Develop a spatial analysis for the identifications of the vulnerable crash points and its relationship with the land use and population of those wards.

1.5 Limitation of the Study

One of the main limitation of this study is the data set. The data are collected from the

Crash Reporting Form of the police department in which they did not follow any

Abbreviated Injury Scale (AIS). Moreover, no demographic information was found on the drivers and the victims. Some of the data are locally compromised due to the negligence of the reporting officer. Thus, this research lacks the demographic analysis of the incidents.

The database is from 2007-2011 which is old. So, the prediction model of this study may not be as accurate as it could be if the recent data could be found. Another shortfall of this study is lack of information about speed, road width, and average annual daily traffic of those roads due to data constraints of the database. For the spatial analysis, there were no shapefiles, and so the author developed the shapefiles from the available address 6

information within the database, which may not be as accurate as the definitive latitudinal and longitudinal values. In the regression model, some of the insignificant variables are understated because of the lack of data. As the primary data source, the police were not trained properly for collecting crash data and they failed to fill up many of the roadway and traffic factors properly. As a result, they were absent in the database or lack of adequate frequency, which results in understatement of those independent variables in the model.

Chapter 2

Literature Review

The idea of the crash severity analysis is a new idea for the South Asian countries. Barua and Tay, 2010 in their study of the timeline 1998-2005 saw that weekend, off-peak hours, a two-way street, and single vehicle incidents are the standard feature of the crashes in

Dhaka Metropolitan Area. On the other hand, Stephan et. al,2011 performed a study on

Thailand to understand the primary reasons behind their crash severity. They concluded that motorcycles are one the main reasons behind the severe injuries in Thailand.

Schmucker et al.,2011 found in their study that single vehicle collisions with objects beside the road are the most common form of a crash in India. In another study of Moradabad of

India, Huda et al.,2010, concluded that professional drivers tend to have more severe crashes than the unprofessional. In contrast, Al Eassa et. Al,2013 found that there is no significant difference between these two group for the United Arab Emirates. In another demographic study of the crash data, Gini et al, 2009 found that males in the age group 21 to 30 are more involved in crashes. Head injuries were perceived as the most common injury type in a study on Nigeria by Adesunkanmi et al.,2010. In another study on

Singapore, Quddus et al.,2002 found that motorcycles with larger engines are a reason behind the higher crash rates of motor vehicles. Atkins et al.,1988 in their study of injury severity found that with the increase of vehicle weight, the severity increase. They also found no significant relationship with the alcohol use of pedestrians and injury severity.

Conversely, Jehle and Cottington,1998 proclaimed that it has a meaningful relationship especially in the age group of 25-34. Through a chi-square analysis, Holubowyez,1995 concluded that male elder populations are the most crash-prone in Pittsburgh, Philadelphia.

In another study of Stone & Broughton,2003 the crash-prone age group was found to be over 50. But Pitt et al.,1990 found no significant difference between the genders. High- speed light trucks, buses, and vans are seen to the reason for a considerable proportion of crashes (Lee and Abdel aty,2005). Exploring the relationship between crash severity and its contributing factors has long been an essential problem in traffic safety analysis which attracts many researchers’ attention and efforts. Due to the complicated nature of traffic crashes involving interactions among driver, vehicle, roadway, traffic and environmental components, unobserved heterogeneity cannot be ignored when addressing the relationship between the crash severity and its contributing factors. Many methodological techniques have been developed to account for unobserved heterogeneity by avoiding the restriction that the observable variables must be the same across all observations, which may result in biased parameter estimates and erroneous inferences and predictions (Zeng et al., 2016).

In a study by Geedipally et al. (2011), crash data from police-reported motorcycle crashes in Texas were used to estimate MNL models to identify differences in factors likely to affect the severity of crash injuries of motorcyclists. The reason this research preferred the multinomial logistic model over ordinal models because such models are not afflicted with some of the restrictions imposed by traditional ordered probit and logit models (Ye and

Lord, 2014). To address the unobserved heterogeneity that conventional modeling methods cannot account for, some newer methodological approaches have been employed for crash severity analysis. Milton et al. (2008) demonstrated that the mixed logit model could better 9

understand the injury-severity distributions of crashes on highway segments in the presence of unobserved heterogeneity. Malyshkina and Mannering (2009) proposed two-state

Markov, switching MNL models for statistical modeling of crash-injury severities which can account for time-varying heterogeneity. Xiong and Mannering (2013) showed that the latent class models with random parameters within classes have the advantage of both the semi-parametric latent classes and fully parametric arbitrary settings when addressing unobserved heterogeneity.

Earlier works related to crash severities and built environments have studied the link between the built environment and pedestrian collision frequency without specifying whether the built environment influenced pedestrian safety by affecting pedestrian activity or pedestrian crash frequency. When the studies are conducted in the area-level, attributes like population density, land use mix and commute travel patterns are often included in multivariate models. But in general, they are treated as any other contributing factor of pedestrian collisions instead of attributes related to the pedestrian activity (L.F. Miranda-

Moreno et al.,2011). To understand the main three built environment factors of crashes, roadway characteristics, environmental characteristics and crash characteristics, this study also explored several kinds of literature. Stone and Broughton,1999 & Ballesteros et al.,2003 found a relationship between the speed limit of the road and fatality rate. They also found lighting condition to be factor, especially the time between 9 pm- 6 am, for severe crashes. During crashes, the rear end impact is found deadlier than the frontal impact in the body. Kim et al.,2008 in their study found the opposite. That is, head-on collisions have a more severe effect than back and found curved roads and poor lighting condition as 10

a factor for increasing injury severity. Lefler and Gabler,2003 saw a relationship between injury severity and high speed. Pitt et al.,1990 concluded with the remark that roadway classifications, travel lane or presence of traffic control have no significant relationship with crash reduction. They found the evening period the most crash-prone. They also found that pedestrians who are moving instead of standing still on the road are more injury-prone.

Miles-Doan,1996 in his study also found the same results. Klop and Khattak,1999 in their logistic regression study explained that presence of a higher average of traffic reduce the severity level and crash location. They also found the presence of shoulders has no significant impact. On the other hand, Zajac & Ivan,2003, through their ordered probit modeling concluded that roadway width has a positive relationship with the injury severity.

Downtown and residential areas have high crash severity whereas commercial areas found to be less crash-prone. Lee and Abdel aty,2005 in their study found dark lighting condition and absence of traffic control device have a positive influence on injury severity. Sze and

Wong,2007 in their research concluded that crashes occurring at intersections with traffic signals tend to have a more severe impact than the other intersection type. They also found the time period 7 pm - 7 am as more crash-prone and identified inattentive and tired drivers a reason. Wet surfaces are found to be less crash intensive because of the cautiousness of the drivers (Zhu & Srinivasan,2011). In another research of Kaplan and Prato,2012 they also found the same results. They found dry surfaces are more crash-prone. In the case of intersections, horizontal ones result in more crashes than the other junctions. But intersections with a signal for pedestrian crossing found to have a positive impact on reducing pedestrian crashes (Lu je et al.,2004). Two-way streets without a divider are also found to be more crash-prone (Quddus et al.,2002). Few studies have investigated the 11

effects of geometric design attributes, including road width, a number of lanes, presence of marked pedestrian crosswalks, the presence of the median, types of turn restrictions, etc. on collision frequency at intersections. For instance, Harwood et al. (2008) completed comprehensive studies of pedestrian crash occurrences and the effect of some geometric attributes. In their study, they found that after controlling for pedestrian and traffic volumes, the number of lanes and the presence of raised medians had significant negative effects on collision frequency. The impact of curb parking was also documented by Box

(2004). It is important to indicate that geometric design may also influence speeds and in turn, severity. For instance, road width or calming traffic measures should be associated with operating speeds (King et al., 2003; Ewing and Dumbaugh, 2009; Tester et al., 2004).

The clustering of traffic crashes which are also known as hotspots is developed based on geographical space (Xie & Yan, 2008). The identification of a hotspot is the first essential step for safety improvements of the transportation system (Anderson, 2009). There are many relevant studies on traffic crash hotspot analysis and detection (Black and Thomas,

1998; Li et al., 2007), among which the kernel density estimation approach is a widely- adopted method. Density estimation estimates the density function from the observed data

(Silverman, 1986). For spatial point data, it assesses the bivariate probability density

(Bailey and Gatrell, 1995). Kernel Density Estimation (KDE) analyzes the first order properties of a point event distribution (Silverman, 1986; Bailey and Gatrell, 1995), and it shows traffic crash hotspots. The planar KDE can analyze the urban cyclists’ traffic hazard intensity (Delmelle and Thill, 2008), detect pedestrian crash zones (Pulugurtha et al., 2007) or for analyze wildlife-vehicle crashes (Krisp and Durot, 2007). KDE has also been used for estimating highway crash hotspot analysis (Erdogan et al., 2008), road crash hotspot 12

classification (Anderson, 2009), and fatal crash analysis (Oris, 2011). Recently, Xie &

Yan,2008 used a network-based kernel density estimation (NetKDE) to estimate the frequency of traffic crashes over a network space. From a broad perspective, NetKDE represents an emerging effort to extend the applications of standard spatial statistical methods. It analyzes spatial point events in a network space (Okabe et al., 1995, 2009;

Okunuki and Okabe, 1999; Okabe and Yamada, 2001; Yamada and Thill, 2004, 2007; Lu and Chen, 2007; Xie and Yan, 2008; Okabe and Sugihara, 2012). The quantitative hotspot analysis relies exclusively on local spatial statistics, another recent significant advance in geographic information science and spatial analysis (O’Sullivan and Unwin, 2010). Local statistics are any descriptive statistic associated with a spatial data set whose value varies spatially. Many local and global statistics are available, and a general contextualization of local statistics is in the research works of Anselin (1995). Two of the most popular spatial statistics are Getis–Ord Gi (Ord and Getis, 1995) and Global Moran’s I (Anselin, 1995).

Both are used for detecting crash hotspots (Moons et al., 2009; Truong and Somenahalli,

2011; Kuo et al., 2012). The reason behind the increasing interest in investigating spatial and temporal factors affecting traffic crashes is that many of the factors that influence crashes work on a spatial scale. In recent studies, Wang and Abdel-Aty, 2006, Aguero-

Valverde and Jovanis, 2008 and Mitra et al.,2007 have shown that spatial factors have a strong influence on crash occurrences. They found a significant relationship between several built environmental factors and behavioral patterns of the drivers with crash intensity.

To understand the spatial distribution pattern of the crash locations, Haining (1993) defined four spatial processes: diffusion, dispersion, interaction, and exchange. However, he failed to explain how interaction and exchange can describe a spatial distribution pattern of crash analysis. Later on, for crash studies, these four processes are condensed into three: disperse, random and clustered. If crashes are not spatially random, some areas might produce more crashes than others (Nunn et al., 2014). The statistical significance of the spatial structure can be understood through the organization of these patterns (Haining, 1993). At the micro level of individual traffic collisions, the forces of spatial interaction indeed operate. The interactions of drivers and vehicles within the surrounding traffic environment comprise the behavioral and situational dynamic in which fatal collisions occur. Global and local

Moran’s I can be calculated to assess the nature and strength of these spatial interactions

(Nunn et al., 2014) and can be considered as a first step toward understanding the risk landscape of the crashes. Briggs (2000) noted that crash exposure does not occur at single, fixed point. It develops across a complex web of locations as people move through the field. Whitelegg, 1987 also added a time factor and described the crash as a noxious space- time event. Various risk factors are related to the crash outcomes and have a relationship with the density of populations as well (Bhalla et al.,2007). For example, people living in the rural area are more exposed to severe crash than the people living in the metro city area because of the high-speed county or state roads (Ossiander & Cummins, 2002). More specifically, traffic collisions have environmental, situational, geophysical, and circumstantial dimensions (Dumbaugh & Rae 2009). Correlational analysis can also develop a relationship with crash number and some additional factors like population,

zoning, etc. Identifying clusters of mortality is one way of trying to extract preventative and public safety policy issues to shape public responses to traffic deaths.

Chapter 3

Research Design & Data Management

3.1 Introduction

Every study is a series of research strategies. The term method refers to “means and ways” of achieving the research goals. The methods, tools, and techniques which are used for conducting a study or research for attaining specific sets of objects are essential to complete the work. It is the pre-instruction or guideline of the research or study problems of how the activities are conducted step by step. This chapter will describe the technique, tool of the study. It will also explain the data collection procedure and its management.

3.2 Background of the study

The statistical forecasting has indicated that over the next decade, the overall crash trend of the world will increase and developing countries like Bangladesh will be the worst sufferer. The main factor that stands in the way of addressing this issue is inadequate transport safety (Hoque, 2004). The developing countries of the world have about 40 percent of world's total motor vehicles and have a fatalities rate of 86 % (Hoque et al.,

2001). The rapid economic growth, increasing income, and cost for comfort and sprawl

development are raising the demands for transportation rapidly in developing countries. As a result, the number of vehicles on roads is also increasing rapidly. The presence of motorized and non-motorized vehicles without any prior road segregation contribute to severe road crash casualties. A more detailed statement of the problem is incorporated in chapter one.

3.3 Study Area

Dhaka City, the capital of Bangladesh, is in Dhaka District that is surrounded by rivers.

Dhaka is in the center of Bangladesh at 23°43’’N,90°24’’ E, on the eastern banks of the

Buriganga River (CIA,2015). The study area for this research is Dhaka Metropolitan Area.

The study area covers the whole Dhaka City Corporation area. This area is prominent for urban agglomeration and is the economic hub of Bangladesh (Ahmed & Ahmed,2012).

Dhaka Municipality was established on August 1, 1864. It became the capital of

Bangladesh with the independence in the year 1971. Then Dhaka Municipality was awarded the status of Dhaka City Corporation (DCC) in 1978. DCC is a statutory organization constituted under the Dhaka Municipal Corporation Ordinance of 1993 and is headed by elected mayor(s). The City area is divided into 92 wards. The area of the City

Corporation at present is about 360 sq. km. The area of Dhaka Metropolitan City is 1530 sq. km. and the estimated population is currently approximately 14 million (Dhaka South

City Corporations,2012). The Local Government (City Corporation) Amendment Act

(2011) has divided DCC into two parts known as Dhaka South City Corporation (DSCC) and Dhaka North City Corporation (DNCC) on 4th December 2011 (Dhaka South City

Corporations,2012). Because of this amendment, the city has been divided into two parts: 17

DSCC and DNCC. But the project intervene area covers both parts of DCC. Therefore, in this report, the common term DCC is often used. The study area is indicated in figure 3.1 with a red solid line.

Figure- 3. 1 Study Area

3.4 Data Collection & Processing

3.4.1 Crash Data Collection (2007-2011)

The data collection phase started with the collection of the necessary documents from

2007-2011 of the Dhaka Metropolitan Area from various organizations such as Dhaka

Metropolitan Police Station (DMP), Bangladesh Road Transport Authority (BRTA),

Accident Research Institute Dhaka (ARI), Center for Injury Prevention and Research

(CIPRB), Urban Planning Division, Dhaka City Corporation etc. Afterwards, the data from the Accident Reporting Form (ARF) of each of the crashes was stored. Each of these ARFs have a total of 69 points from which the researcher collected the necessary data for this research. The data were received from an analysis package named MAAP5

(Microcomputer Accident Analysis Package) which is set up in the office of the Deputy

Inspector General of Police. Next, a database has been created in Microsoft Access which was later transformed into CSV file format for the analysis in the SPSS 23 analysis package.

The dataset that has been used in this analysis is unique in character because this is the first comprehensively developed crash database for Dhaka Metropolitan Area, so this is also one of the first studies that is discussing the crash situation of this metropolitan area.

Moreover this study re-created the whole spatial database and the spatial analyses that are conducted here are also the very first spatial observations of the crash situation of this locality.

3.4.2 Data Classification

For the analysis, the researcher initially had to go through the data in detail to find out the missing values and to identify the outlier values. The total number of observation was 2730.

But there were some values which seemed to be recorded wrong or missing. Box Cox normality plot was used for outlier identifications. These had been cleaned for better distribution and analysis. After that, a total of 2716 observations are considered. When the data were collected, the intensity of the crash level was divided into four categories: death, extensive injury, minor injury, and vehicle collision. But as the data were collected without any AIS (Abbreviated Injury Scale) measurement, it was challenging to differentiate the extensive injuries from the minor injuries. For that reason, this research considered three severity categories combining the two injury levels into one: extensive or minor injury.

There were also six types of traffic intersections which were redistributed into four, combining railway crossing, roundabout and staggered intersection, since the observation number within these categories is the smallest. The traffic control system was also classified into six groups which were condensed into five categories. Median separation and pedestrian crossing were attached together. A total of nine categories of collisions were collected in this database which were changed into five. Here the rollovers/single vehicle collision, right angle collision, and side collisions are considered into one category and striking something besides, on, or a standing car in the road is viewed in a different category altogether. In the weather condition, foggy and rainy weather conditions are considered as one and the other one is good weather condition. The lighting condition was also collected into four categories. But for the analysis, they were recategorized into two. The day and

dawn/dusk observations are combined into one group and the crashes that occurred at night with or without the presence of traffic light is considered in the other group. To understand the geometric condition of the road, the data were collected into five categories. But for the analysis, this study reclassified them into three where peak and sloppy roads are categorized together into one group and curved, curvilinear roads are in another. The conditions of the top layer of the road were also classified into two instead of its primary three categories combining wet and muddy conditions together. When the data were collected, to understand the characteristics of the road, they were divided into three categories. But here for the analysis purpose, this study split them into two where rough and under construction roads are joined together in a single category. The roads were classified into four categories in the database. But for the analysis, they were recategorized into two considering city and feeder road into one and highway and regional highway on the other. The road types are primarily divided into three categories where bridge and speed breaker are joined together in a single group in this analysis. The descriptive statistics of the variables are given below in Table 3.1.

Variables Factors Frequency Percentage

Crash Death/Fatal Crashes 1868 68.80% Intensity Extensive or Minor Injury 515 19.00% Vehicle Collisiona 333 12.30% Type of Railway, Roundabout or Staggereda 47 1.70% Intersection No Intersection 1924 70.80% 3-Way/ T junction 426 15.70% 4-way 319 11.70% Traffic Police & Traffic Signal Controlleda 38 1.40% Control System Uncontrolled 1710 63.00% Police Controlled 883 32.50% Traffic Signal Controlled 44 1.60% Median & Pedestrian Crossing 41 1.50% Type of Right Angle, Rollovers, or Side Collision 212 7.81% Collision Pedestrian Collision 1664 61.27% Rear-End Collision 684 25.18% Head on Collision 96 3.53% Striking something on or beside or a standing Cara 60 2.21%

Direction of Two Waya 740 27.20% the Traffic One Way 1976 72.80% Flow Presence of Does Not Exista 541 19.90% Road Divider Exists 2175 80.10% Foggy & Rainya 23 0.80%

Variables Factors Frequency Percentage

Condition of Good 2693 99.20% the Weather Lighting Night Time (With or Without Traffic Light) a 902 33.20% Condition Daytime 1814 66.80% Geometric Slope or Peaka 13 0.50% Description Straight & Plain 2632 96.90% of the Road Curvy & Sloppy 71 2.60% Condition of Muddy & Weta 21 0.80% the Top Dry 2695 99.20% Layer of the Road General Rough or Under Constructiona 30 1.10% Observation of the Good 2686 98.90% condition of the Road Road National or Regional Highwaya 898 33.10% Classification City & Feeder Road 1818 66.90% Road Type Bridge or Speed Breakera 6 0.20% Normal 2710 99.80%

Note- N=2716, aReference or Base Category.

3.4.3 Spatial Data Collection

The ARF had the location information of the crash points and based on that, the latitudinal and longitudinal points of those places were collected. This research then used those data for the network-based kernel density mapping. All the information was in an Access file. From there it was collected and then put in an Excel sheet. Then the Dhaka Metropolitan Area Boundary shapefile is obtained from the Dhaka City Corporation Authority. Then it was re projected in a

BTM (Bangladesh Transverse Mercator) projection system for the overlaying. The road shapefile was collected from the Local Government and Engineering Department (LGED). In many cases, the crash data could not be plotted accurately due to inaccurate recording. There were also errors in filling out the forms, which was found during the review process. During the analysis, this research found that 20% of the ARF did not have any information about the coordinate (X, Y) values. This loss of information is because of the outdated maps that are still used by DMP.

Because of this problem, some of the crashes that have occurred on new roads could not be located.

This study was conducted based on the available data and incorporating correction of locations to the extent possible. The accident points are digitized based on the available address information of the database. For that, Google Map is used as a base layer for the DMA. Over that the road shapefile was plotted. After that, the points were digitized over the road layer as a point feature based on the reference of the Google map.

In the Access file, the crash points were all merged, but before that, this research wanted to see each of the year's crash condition. The preferred attribute choice was used and a separate layer created. Then they were merged to get a single shapefile. This was used for the hotspot analysis.

The Network Kernel Density Estimation (NKDE) influences each of the points to extend to a chosen distance along the network. But in the overall kernel density, it only considered a planar surface for the zone of influence. For example, a 1-mile bandwidth on a network means one mile from a point to another through their shortest path distance. In the general KDE, each location had a circle (or curve, depending on the type of kernel function) with radius (or bandwidth) of one mile of influence. However, in the NKDE, bandwidth defines shortest path distance. In the traditional

KDE, the events often appear more clustered than they are (Okabe, 2009). In the NKDE, it interprets the value of the surface as most significant at the location of every point location and it starts to decrease as the distance from these points increases. Network kernel density estimation is a nonparametric approach. It distributes over one-dimensional space which helps to estimate the density at any location in the study region (Mohaymany et al. 2012).

ESRI did not have that tool. This study used SANET (Spatial Analysis Along Networks), a spatial analyst tool for web-based kernel density estimation. While performing the NKDE, the polygon feature of the road first needed to change into polyline feature. Then a select by location analysis was carried out to choose the corridor and its related lanes and collect them separately. They merged into the crash shapefiles from 2007-2011 to create a single shapefile for this analysis. The

Global Moran's I spatial autocorrelation performed through Geographic Information System (GIS) also. The population data are collected from the Bangladesh Bureau of Statistics (BBS).

3.5 Techniques of Research

3.5.1 Multinomial Logistic Regression Analysis

For the analysis, the multinomial logistic regression method is used. The reason behind this is the findings of a study by Ye & Lord, 2013. The three most commonly used crash severity analysis models are a multinomial logistic regression (MNL), ordered probit (OP) and mixed logit model

(MLM). Then in their study, Eluru et. Al used a completely new model named mixed generalized ordered response logit (MGORL) model which they suggested needs more testing in different data sets. Though for ordinal data, OP is a clear choice, it has some issues. In the OP model the threshold limit is fixed, and since the data is ordinal, it tends to influence the variables to the highest or lowest values. For that reason, the models in the simulation were designed on the observation- focused rather than outcome-based, which might be affected in the OP model. For the confidence interval, they estimated the injury, non-incapacitant injuries, incapacitant injuries and fatal constants. The goodness of fit of this model is slightly better than multinomial logistic regression, but it is true for small sample of <1000. For a bigger sample, the mean absolute bias probabilities

(ABP), max (ABP_ and RMSE (root mean square error) ) of OR are very high, and only for lower sample size we can get a stable mean point from the intervals. As the number gets higher, the confidence interval for this process gets more wide and difficult to develop a relationship with the reference point. Mixed logit model has the best interpretative capacity among the three models.

But it will be only true if the sample size is bigger (>5000). Fewer than 5000 observations limit

the effects of the ABP and RMSE value of MLM. In a comparison of these two, the only problem

MNL has is in the IIA (Independence from Irrelevant attributes) or the prediction that the factors which are not calculated in MNL have no spatial effect. It allows MNL to go with the randomness of the dataset which decreases the goodness of fit of the model but nevertheless, the likelihood ratio is good enough from OP and MLM. It is also in between OP and MLM in the sample estimation. So, the conclusion is that in a case of interpretation, MLR is the best, but the sample size needs to be larger. For the smaller sample, it is better to go with OR. But the sample size that this study has 2716 observations, for which it is better to go with multinomial logistic regression for the analysis. Moreover, the multinomial models do not impose any unrealistic parameter restrictions which is common in the traditional ordered probit models because of its irrelevance of independent assumption character. It can present the under-reporting variables in a corrected estimate rather than the ordered or mixed logit models (Washington et. Al, 2011). In similar studies

Savolainen et. Al,2011, Lord & Bonneson,2005; Lord & Mannering,2010 & Train,2003 supported this claim. Multinomial logistic regression also allows for understanding the non-monotonic effect of the independent variables with relation to the dependent one. (Ulfasson & Mannering,2004;

Kim et.al,2007). Moreover, it allows the estimation of the effect of the independent variables in each severity category on the reference category. (Tay, Et, Al, 2011)

When we are dealing with more than two independent variables and want to predict the future outcome of the dependent variable without explicit ordering, multinomial logistic regression is the traditional choice. In this model, the framework that is used to model the injury severity with its factors behind is a linear function S that fixes the injury outcome for observation n as

Sin = βiXin + εin (Shankar & Mannering,1996) ------(1)

Where βi is a vector of the estimated parameters and Xin is the observable independent variables

(type of intersection, collision, lighting system, weather etc.) that affect the severity level sustained by the number of observation n. εin is the disturbance term for the unobserved effects. But we want to independently distribute the unobserved terms as generalized extreme values and the model would be following Daniel McFadden’s original derivations (McFadden 1978,

1981),

Pn(i) = P (βiXin+ έin ≥ max (βiXin+ έin) (Mannering et. al,2011)------(2)

For the extreme value Type 1 distribution, if all ε in are independently and identically (same variances) distributed random variates and a common scale parameter η (which implies equal variances), then the maximum of βiXin+ έin extreme value Type 1 distributed with mode

1/η LN ∑EXP (η βiXin) (Mannering et. al,2011) ------(3) because the difference between two independently distributed extreme value Type 1 variates with common scale, parameter η is logistic distributed

Pn (i) = EXP [η (βiXin)]/ EXP [η (βiXin)] + EXP [η (βiʹXʹn] (Mannering et. al,2011)------(4)

Substituting with Equation (2) and setting η= 1 (there is no loss of generality, Johnson and Kotz

1970) the equation becomes

Pn (i) = EXP[βiXin]/ ∑ EXP [βiXin] (Shankar & Mannering,1996)------(5)

3.5.2 Predicting & Outcome Variables

In this analysis, the data were re-categorized into three intensity categories namely, fatal, extensive injury or minor injury, and vehicle collision. Among them, 68% of the crashes were fatal. For the analysis, crash intensity was considered as an outcome or dependent category where vehicle collision is considered as a dummy variable. A total of twelve independent variables are used to 28

analyze this dependent variable where some of the categories are joined together for the better understanding and lack of observation. The reference categories are selected based on the nature of the variable and the available frequency of them. In some cases, the frequency of some of these categories is so small that it was not possible to retrieve any prominent standard deviation or significant result and so this study adds them together and use them as a reference category. And in the other cases, the reference categories are selected based on the relationship of the dependent and the independent variable. They are selected to ensure the best interpretation out of the model.

3.5.3 Network-Based Kernel Density Estimation

Identifications of high-crash-risk ward segments of a city offer safety specialists an insight to the better understanding of crash patterns which in turn help them to enhance the road security and management system of the localities. The standard hotspot identification methods fail to visualize the underlying shape of crash patterns because they neglect the spatial properties of crash data. As the crash data are discrete and have limited accessibility to identify exact locations where crashes occur, it expects a continuous surface drawn from distinct points which will better reflect crash density. It will also be able to draw a more realistic picture of crash distribution. The network- based kernel density works as an extension of the standard two-dimensional kernel density, and the estimate which is used to point the event in a network space is:

1 푑 휆(푠) = ∑ 푘 ( 푖푠) (Xie & Yan,2013) r r

Where λ(s) is the thickness at location s, r is the search radius (bandwidth) of the KDE (only points within r are used to estimate λ(s)), k(dis/r) is the weight of a point I at a distance dis to locations s.

The so-called kernel function, k, is formed as a function of the ratio between dis and r so that the

‘‘distance decay effect’’ can take into account density estimation.

In implementing NetKDE, Xie and Yan (2008) suggested using a linear segment (called lixel) of roads as the core unit for aggregating crashes, calculating density, and for visualization. They found that segments of shorter length are more capable of showing the local variations of the layers. The linear segments with geocoded crashes are known as source layers. For each source segment, the NetKDE density values are computed for the segment and its neighbors. For a portion falling within the search bandwidths of multiple source layers (including functioning as a source layer itself), its density is the cumulative value of the frequencies computed from all relevant source segments.

In this study, equal-split continuous kernel function was employed. The advantage of equal-split continuous kernel function as explained by Okabe is shortening of the computation time, particularly when applied to a complex network (Okabe, 2009). Xie, 2008 also found this same justification. The band width distance is selected 1 KM for faster movement of the analysis tool.

After finishing the study, the SANET created two shapefiles where one contains the line density and the other contains the point density. The line density shapefile is developed to show the intensity through its Z axis. From there, the vulnerable wards and the intersection points are identified through the identify tool. From the NKDE Map, the present and future significant high- intensity crash wards and intersections are determined.

3.5.4 Optimized Hotspot Analysis

The Getis–Ord Gi*statistic also known as hot-spot analysis (Getis and Ord, 1992, 1996; Mitchell,

2005; Ord and Getis, 1995) is a method for analyzing the location-related tendency (clustering) in the attributes of spatial data (points or areas). The method is an adaptation of the General G statistic

(Getis and Ord, 1992), a global method for quantifying the degree of spatial autocorrelation over an area. The General G statistic computes a single statistic for the entire study area, while the Gi statistic serves as an indicator for local autocorrelation, i.e. it measures how spatial autocorrelation varies locally over the study area and computes a statistic for each data point. To improve statistical testing, Ord and Getis (1995) developed a z-transformed form of Gi* by taking the statistic Gi*(d) minus its expectation, divided by the square root of its variance.

Where Gi*(d) is computed for feature (i) at a distance (d) standardized as a z-score. Xj is the attribute value of each neighbor, wij is the spatial weight for the target-neighbor i and j pair and n is the total number of samples in the dataset. A Euclidean method is used to calculate the geographical distances from each feature to its neighboring features.

The output of the Gi* statistic is a map showing the location of spatial clusters in the study area.

Positive values of Gi* denote spatial dependence among high values. Negative values of Gi* indicate spatial dependence for low values. The degree of clustering and its statistical significance is evaluated based on a confidence level and on output z-scores. These define whether a data point belongs to a hot-spot (spatial cluster of high data values), cold-spot (spatial cluster of low data values) or an outlier (a high data value surrounded by low data values or vice versa) (Peeters,2015).

3.5.5 Spatial Autocorrelation Analysis

Spatial autocorrelation and spatial heterogeneity are the two most common type of exploratory spatial data analysis tool (ESDA) used for accidental spatial analysis. This study followed Moran’s

I spatial correlation method of Moran (Moran, P,1948; Griffith, D, 2008 & Anselin, L,1995). The idea of this analysis is that all the crashes are independent and have no relationship with each other.

If the accident points are dependent, there should be a positive autocorrelation between them. The equation that is here is:

푛 푛 푛 ∑푖=1 ∑푖=1 푤푖푗(푦푖 − 푦̅) 퐼 = 푛 푛 2 (∑푖≠푗 ∑푖=1 푤푖푗)( ∑푖=1(푦푖 − 푦̅)

Here n indicates the number of samples (crashes), 푦̅ is global mean value for the number of crashes takes place. yi and yj are the numbers of accidents at ith and jth locations. Wij is a spatial weight matrix, which determines the degree of local effects of crashes and been defined based on network distances. The larger the absolute value of Moran’s I, the more significant the spatial autocorrelation while a value of zero means perfect spatial randomness (Anselin, L,1995).

The Z score of the analysis can be obtained by: 32

퐼 − 퐼 푧 = 푂 퐸 푆퐷퐼퐸

Where Io and IE represent the observed and expected number of crashes and SDIE is the standard deviation for crash frequencies. In this analysis, along with Moran's I coefficient, it is employed to figure out whether the crashes are interrelated with each other or independent.

Chapter 4

Results

4.1 Descriptive Statistics

Road transportation is an extremely important part of the economy of Bangladesh. About 12% of

Gross Domestic Product (GDP) and 20% of the annual development budget is spent on transport, and 9.4% of the national employment is related with the transport sector. The national loss due to road crash is estimated around $600 million (at 2% of GDP) every year (BRTA,2010).

4.1.1 Road Crashes in the Major Cities of Bangladesh

Crash rate per 10,000 population in the major cities of Bangladesh 0.8 0.6 0.4 0.2 0

Dhaka Rajshahi Khulna Chittagong Proportion of rates Crash Major Cities

Fatal Injury Major and Minor Injury

Figure 4. 1 Crash rates in the major cities of Bangladesh

Source -BRTA Report 2010 34

From the annual evaluation report of the road transport authority of Bangladesh, BRTA (Figure

4.1) it was found that the crash trend of Dhaka is higher than most of the other big cities in

Bangladesh. In percentage, the rate is 0.7 for the fatal crashes and 0.5 for extensive or minor injuries per 10,000 population. In compare to Dhaka, the second largest urban area, Chittagong, has a fatal crash rate of .2 and.15 for fatal crashes and extensive or minor injuries respectively.

The difference in the total metropolitan population is a crucial factor behind that. The total metropolitan population of Chittagong is 4 million while this number is 14.4 million for Dhaka

(BBS ,2011). The second highest crash rate is found in Rajshahi which has a population of .7 million (BBS,2011).

4.1.2 Crash Trend of Dhaka Metropolitan Area

The overall crash trend of Bangladesh is in a downward shift whereas the crash tendencies for

Dhaka is almost static. By analyzing the yearly crash data from 2007-2011 of Dhaka Metropolitan

Area (Figure 4.2), this research found that the number of deaths is in a decreasing trend. However, the extensive and minor injuries are increasing. The crash data collection system of this country is poor. Many crashes are not even reported or collected properly. This might be a reason for the downward trend of the fatal crash curve for Bangladesh.

Accident Trend comparison between Bangladesh & Dhaka Metropolitan Area 8000 7022 7049 6000 5644 4449 4000 3858 2000 731 655 518 0 434 382

2007 2008 2009 2010 2011 Totalnumber casualities of Trend Years

Overall Casualities in Bangladesh Casualities in Dhaka

Figure- 4. 2 Crash trend comparison between Bangladesh and Dhaka metropolitan area

Source- Ahmed, 2013

4.1.3 Crash Statistics of Dhaka Metropolitan Area 2007-2011

The total number of crashes that recorded from 2007-2011 showed a decreasing trend. Most of them are the pedestrian collisions (Table 4.1). The number of fatal injuries was found to be higher than the extensive and minor injury related crashes. The number of in between vehicular crashes are also in a decreasing trend, and the number is smaller in comparison to the fatal crashes.

Table 4. 1 Summary table of the overall Crash situation of Dhaka Metropolitan Area 2007-2011

Number of Fatal Crashes Number of Extensive or Minor

Injuries

Year Total Pedestrian Non- Motorized Pedestrian Non- Motorized Number

Number Motorized Vehicle Motorized Vehicle of Motor

of Crash vehicle vehicle Collision

Recorded

2007 731 361 46 47 78 32 59 109

2008 655 367 40 56 20 28 56 88

2009 518 307 26 45 14 23 40 65

2010 434 249 31 32 30 17 48 44

2011 382 197 29 42 41 17 50 28

From the linear exponential crash trend of DMA (Figure 4.3), it is found that the death or fatal

crash trend of the metropolitan area is in an increasing trend and there is a positive correlation

among the years. The injuries also follow an increasing trend with positive correlation. In

contrast to them, the only negative or decreasing trend that is found is the trend of the vehicular

collisions.

Crash Severity of Dhaka Metropolitan Area (Per 10,000 crashes)

8000 7069 7269 6918 6634 7000 6202 R² = 0.0736 6000 5000 4000 2673 3000 2309 2106 2000 1489 1588 1344 1481 1250 R² = 0.1565 976 693 1000 R² = 0.9564 0 2007 2008 2009 2010 2011 Death Injuries Vehicle Collision Linear (Death) Linear (Injuries) Linear (Vehicle Collision)

Figure- 4. 3 Crash Severity of Dhaka Metropolitan Area (Per 10,000 crashes)

4.1.4 Crash Severity

Almost 69% of the crashes that are reported in the timeline from 2007-2011 are fatal crashes.

Among them 83% them are found to be pedestrian collision. The percentage of extensive and minor crashes are 15 and 12 percent. In comparison, vehicular collision rate is only 4%.

Percentage of the crash intensities (2007-2011)

4% 12%

15%

69%

Fatal Accident Major Accident Minor Accident Motor/ Vehicle Collision

Figure- 4. 4 Percentage of different Crash intensities of Dhaka Metropolitan Area

4.1.5 Intersection Type of the Crashes

Most of the crashes (70%) did not occur in an “no intersection” or along the road points. Among the intersections, most of them occurred in the 3 way or T junction and 4-way intersections.

Intersection Type of the Crashes (Per 10,000)

Staggered 15

Roundabout 103

Railway Crossing 55

Others 4

No Intersection 7073

4-way 1178

3-Way/ T junction 1572

0 1000 2000 3000 4000 5000 6000 7000 8000

Figure- 4. 5 Showing Intersection types of the crashes in DMA

4.1.6 Traffic control system of the Crash Locations

About 60% of the crashes took place in an uncontrolled traffic situation. Police-controlled traffic points are responsible for 30% of the crashes. Pedestrian crossings and signalized intersections tend to have a small number of crashes. These two initiatives have a positive impact in reducing crashes.

Traffic control system (Per 10,000 Crashes)

Uncontrolled

Traffic Signal Controlled

Police Controlled

Police & Traffic Signal Controlled

Pedestrian Crossing

Median Separation

0 1000 2000 3000 4000 5000 6000 7000

Figure- 4. 6 Traffic control system of the crash locations

4.1.7 Collision Type

As described earlier, the crash rate among the pedestrian is the highest among all the other travel modes. Among the reported crashes, 60% of them are found to be pedestrian collisions and 25% of them are rear end collision. The rest of the rates are negligible. Since walking is the principal mode of transportation for a sizable population of this metropolitan area and the road designs are not pedestrian friendly, this situation is expected.

Collision type

Head on 4% Rear end

25% Right Angle Side Swipe

2% Overturned Vehicle 60% Hit object in the road 3% 4% Hit Object off the road 1% Hit Parked Vehicle 1% 1%

Figure- 4. 7 Collision types in Dhaka Metropolitan Area

4.1.8 Traffic Flow Direction in the Location of the Crashes

The majority of the crashes that are recorded are on a one-way street. Comparatively speaking, the numbers are not significant for the two-way traffic street. A big reason behind this situation is the variation of vehicular speed on the road. All motorized, non-motorized and semi-motorized vehicles share the same road with no lane division and regulations here. This created many of the rear end collisions and the lack of sidewalks force people to walk on the road, which results in pedestrian collisions.

Traffic Flow Direction of the Crash Points

27%

73%

One Way Street Two Way Street

Figure- 4. 8 Traffic flow direction of the locations of the crashes

4.1.9 Impact of Road Divider

More than 80% of the crashes of the study period occurred in the roadways which have a road divider. A principal reason is the two major collision types: pedestrian hit and rear end collision, which can occur irrespective of the presence of a road divider. So, in another sense, this study fails to capture the real impact of road divider because of the collision and crash trend.

Presence of Road Divider (Per 10,000 crashes) 9000 8012 8000 7000 6000 5000 4000 3000 1988 2000 1000 0 Exist Does not Exist

Figure- 4. 9 Presence of road dividers

4.1.10 Weather Condition of the Crash Time

The weather condition seemed to have no significant impact on the frequency of the crashes.

Though the popular believe is that bad, foggy, or rainy weather condition tends to responsible for more crashes than clear weather condition, the situation is different in the Dhaka Metro area.

Almost 99% of the crashes that are recorded in this study period were in a good weather condition.

Weather Condition (Per 10,000 crashes) 12000 9915 10000 8000 6000 4000 2000 66 18 0 Good Rainy Foggy

Figure- 4. 10 Weather condition

4.1.11 Lighting Condition

Lighting condition also has no significant impact on the crash intensity or number. Among the crashes, 54% of them occurred in broad daylight, while 29 % of them were at night with the presence of street and traffic light. Only a handful of them occurred at night without any traffic light. Another reason behind the lower percentage of the night traffic crashes are the lower number of vehicles on the road in the night time.

Figure- 4. 11 Lighting condition

4.1.12 Road Geometry

Road geometry also has no significant impact on the crash intensity. Most of the crashes occurred in the straight and plain roads. For rest of the road geometries, the numbers are negligible.

Road Geomatry (Per 10,000 crashes) 12000

10000

8000

6000

4000

2000

0 Straight & Flat Curve Slope Curve & Slope Crest

Figure- 4. 12 Road geometry

4.1.13 Temporal Trend of the Crashes

From the beginning of the year to the end, the number of crashes displayed a decreasing trend.

The number is highest in the month of April and lowest in December. March – June have the most crash-prone periods. According to this study, the reason is unknown. Month-wise, the pedestrian crash table also supports this claim.

Accident trend by month to month ( Per 10,000 Accidents)

987 869 946 891 902 880 869 758 773 762 703 659

Figure- 4. 13 Temporal trend of the crashes

Fatal crashes are prominent all through the years. The numbers are highest in April and lowest in

December. Most of the crashes are fatal in nature. In comparison to that, the crash-related injuries are less significant.

Table- 4. 2 Temporal Trend of the pedestrian crashes

Pedestrian Crash Fatal

Situation (2007-2011) Crashes

Month

January 111

February 104

March 140

April 146

May 121

June 123

July 108

August 118

September 129

October 106

November 119

December 102

Total 1427

4.2 Multinomial Logistic Regression

Preliminary to the statistical modeling, the data set is first tested for multicollinearity through

Variance Inflation Factor and then through correlation matrix. Then based on these two analyses, the independent variables are selected. Then Box and Whiskers chart, otherwise known as “Box

Plot,” is developed for each of the independent variables with the dependent one to identify the outliers. The detected outliers are deleted from the database. Then the modelling was performed.

In this study, vehicle collision is considered as a reference category.

Table 4. 3 Estimation of Parameters for Crash Death/Fatal Crash Extensive or Minor Injury Severity Through Multinomial Logistic Sig. Odds 95% Sig. Odds 95% Confidence Interval Regression Ratio Confidence Ratio Interval

Independent Category Lower Upper Lower Bound Upper Bound Variables Bound Bound

Type of No Railway 0.000 2.057 0.708 5.973 0.000 1.174 0.44 3.135 Intersection Intersection Crossing, 3-Way/ T Roundabout or 0.003 2.341 0.799 6.864 0.001 1.022 0.378 2.763 junction Staggered 4-way 0.281 1.821 0.612 5.414 0.628 1.28 0.472 3.471 Traffic Uncontrolled Police and 0.001 1.577 0.517 4.806 0.001 1.096 0.382 3.146 Control Traffic Signal System Police Controlled 0.605 0.75 0.253 2.227 0.000 0.795 0.286 2.209 Controlled Traffic 0.003 1.427 0.345 5.9 0.687 1.32 0.342 5.099 Signal Controlled

Median & 0.34 2.564 0.37 17.739 0.524 1.866 0.274 12.722 Pedestrian Crossing Type of Pedestrian Striking 0.000 11.125 7.065 18.879 0.77 9.788 6.078 15.97 Collision Collision Something Rear-End besides, on or 0.000 4.183 2.196 7.971 0.000 2.92 1.609 5.3 Collision with a standing Head on car 0.000 5.153 2.27 11.699 0.001 3.63 1.644 8.015 Collision Right Angle, 0.001 3.385 1.672 6.853 0.003 2.413 1.246 4.672 Rollovers, or 50

Independent Category Lower Upper Lower Bound Upper Bound Variables Bound Bound

Side Collision Direction of One Way Two Way 0.003 1.831 1.122 2.987 0.003 2.141 1.293 3.544 the Traffic Flow Presence of Exists Does Not Exist 0.000 0.147 0.077 0.282 0.000 0.138 0.071 0.268 Road Divider

Condition of Good Foggy or 0.969 0.341 0.238 0.529 0.971 0.744 0.732 0.839 the Weather Rainy

Lighting Day time Night Time 0.002 0.699 0.509 0.961 0.002 1.041 0.753 1.439 Condition (With or Without Traffic Light) Geometric Straight & Peak or Slope 0.646 0.567 0.051 6.355 0.428 0.387 0.037 4.046 Description Plain of the Road Curvy & 0.587 0.489 0.037 6.451 0.098 0.112 0.008 1.499 Sloppy Condition of Dry Wet & Muddy 0.619 1.835 0.168 20.057 0.612 0.421 0.421 0.543 the Top Layer of the Road

Independent Category Lower Upper Lower Bound Upper Bound Variables Bound Bound

General Good Rough and 0.301 0.311 0.034 2.843 0.342 0.522 0.05 5.461 Observation Under of the Construction condition of the Road Road City & National & 0.000 0.439 0.313 0.615 0.002 0.851 0.599 1.208 Classification Feeder Road Regional Highway Road Type Normal Bridge & 0.641 2.894 0.033 250.672 0.000 2.83 0.041 196.283 Speed Breaker

Note- Total Observations=2716, chi-square value =1362.062 (df 42), Likelihood Ratio=1044.669 (df 42) & Pseudo R=.37.

4.2.1 Traffic Factors

It is observed that in a no intersection location or in the normal road segment, the odds of a fatal crash occurring are 2.057 times higher than the probability of the same crash occurring in a railway crossing or roundabout or staggered location. This probability is 2.34 times higher for a three-way or T junction. In comparison to that, the odds of extensive or minor injury occurring are found to be 1.17 times and 1.02 times higher than the reference category. So, from the decreasing odds in the intensity, it can be said that the no intersection and 3-way intersection points are more susceptible to fatal crashes than extensive injuries. The reasons are the high speed of vehicles in the no intersection points and lack of directional instructions and control system in the 3-way junctions. The majority of the drivers in this metropolitan area have very little educational background and have a lack of knowledge about the signs and driving manual about these different intersections as opposed to the typical 4-way intersections.

Uncontrolled traffic locations are 1.57 times more vulnerable to fatal crashes than the locations having police and traffic control. Intersections only supported by traffic signal are also found vulnerable to fatal crashes in 1.4 times higher than the reference category. Uncontrolled and police- controlled intersections are exposed to extensive and minor injuries with an odds ratio of 1.09 and

0.79 in comparison to police and signal-controlled intersections. Lack of respect for the laws and knowledge about the signals among the drivers is a reason behind this intensity difference. There is no institutionalized signal and driving training system for the drivers, especially for the drivers of the public transports in Bangladesh. Moreover, many of these electronic signal systems are turned off by DMP recently. Lack of respect to these automated systems among the drivers is the

reason indicated in their briefing. This made the situation more vulnerable and completely reliable upon the traffic officers.

All the collision types are found to be significant in comparison to the reference category of striking something besides, on or with a standing car. The odds of a fatal crash occurring in a pedestrian collision are found to be 11.25 times higher than the reference category and vehicle collision. The odds are five times higher for the head-on collision. For the rear end and right-angle collision, the odds are four times and 1.8 times respectively. On the other hand, rear end, head on and right-angle crashes have a significant impact on the extensive and minor injury in comparison to the vehicle collision. The probability of occurring head-on collision is found 3.63 times higher than the reference category. For the rear end and right-angle crashes, the odds are 2.9 and 2.4 times respectively. Since the pedestrian movement has the least safety, most of the crashes are found to be fatal. However, the vehicles that fall into rear end and right-angle collisions have an advanced safety system. That is the reason behind the significant difference between death and extensive or minor injuries and their respective collision types.

In comparison to two-way roads, the odds of occurring fatal crashes on the one-way road is found to be 1.83 times higher. But for extensive and minor injuries, the intensity is found to be more severe, at 2.41 times higher. Most of the roads in the Dhaka Metropolitan Area have no dedicated lane system. Both motorized and non-motorized vehicles use the same lane, and during the peak hours when everyone is in a rush, it creates uneven traffic flow on the road and crashes occur as a result. On the other hand, since the roads are busy, the speed of the motorized vehicles become moderate which reduce the chance of fatal crashes and increase chances of extensive and minor

injuries. Most of the injuries are found to be rear-ended collisions and the casualties are school children and women.

Like the widely held belief, road dividers are found to have impact in reducing traffic crashes in

Dhaka Metropolitan Area. In comparison to the absence of divider, the odds of fatal crashes occurring is found to be 0.147 times, which indicated a significant negative impact on the crash intensity. For extensive and minor injuries, the odds are also found to have a negative impact of

.138 times.

4.2.2. Lighting & Environmental Factors

Lighting condition or the time of the crashes has been demonstrated to have a more intense impact on the extensive or minor injury crashes than the fatal ones. In comparison to night time, the odds of extensive or minor injuries occurring is found to be 1.04 times higher in the daytime while the odds are 0.69 for fatal crashes, which indicated a negative impact on the crash severities. This shows that fatal crashes are more common in the night times, rather than the day time. Unlike the common belief that the lack of visibility of night time causes crashes, the condition of the Dhaka

Metropolitan Area is different. Here most of the crashes occurred due to the negligence of the drivers and pedestrians rather than a visibility issue. Peak hours in the morning and evening period tend to have more of an impact on the number of crashes than lighting in the Dhaka Metropolitan

Area. Weather tends to have no significant effect on the various crash intensities.

4.2.3. Roadway Factors

In comparison to national and regional highways, the city and feeder roads were found to have an odd of 0.43 for fatal crashes and 0.85 for extensive and minor injuries. So, the national and regional highways are more vulnerable to crashes than the city or feeder roads. This is due to the speeding, lack of safety signs and features in the highways and reckless nature of driving.

Road type has no significant impact on the fatal crashes but is found to affect the extensive and minor injuries in comparison to vehicle collisions. The odds of extensive or minor crashes occurring in the normal road are found to be 2.83 times higher than the bridge or roads with speed breakers. Speed breakers have an impact on decreasing the death rate. Geometric condition and the top layer of the roads were both found to have no significant effect.

In summary, the no intersection or general road segments and 3-way intersection points are more susceptible to fatal crashes than extensive injuries. Police-controlled intersections have a positive impact on reducing crashes. Pedestrian, rear end and head-on collisions are found to be the most significant collision type. A one-way road is more vulnerable to crashes. Road dividers have a positive impact on crash reduction. Night time is more vulnerable to fatal crashes while day time is more vulnerable to extensive or minor injuries. National and regional highways are more vulnerable to crashes than the city or feeder roads. Speed breakers have an impact on decreasing the fatal crashes.

4.3 Spatial Analysis of the Traffic Crash Severity of Dhaka Metropolitan Area (2007-2011)

This part of the study is divided into four phases. For the first part, the general fatality, injury, and collisions map are represented for the overall understanding of the traffic crash situations. Then in the second phase, this study conducted a spatial autocorrelation study along with a hotspot analysis to understand the most vulnerable locations based on the weight created by the number of victims and their level of crash intensity. Then in the third phase, a network-based Kernel Density

Estimation analysis was performed to understand the specific road segments and to identify their respective zones. Then in the final phase, a correlation study has been performed among the land use, population, and areas of those zones with the frequency of the crashes of those specific zones.

4.3.1 Fatal Crashes

A clear majority of the fatal crashes took place in the southern and northeastern part of the metropolitan area. Major arterial roads like Bir Uttam Rafiqual Islam road, Sohrawardi Uddan

Avenue, New Airport Avenue, Mirpur- Azimpur Road, Begum Rokeya Sharani Road, Lal matia are found to be the most fatal crash-prone. The fatal crash map for DMA of this period is given below:

Figure- 4. 14 Fatal Crash Locations of Dhaka Metropolitan Area,2007-2011

4.3.2 Extensive or Minor Injury Related Crashes

The extensive and minor injury related crashes also had the same spatial orientation as the fatal crashes. In general, the southern part of the metropolitan area is found to be more extensive or minor crash prone than the north or east side. Major arterial roads like Outer Circular Road,

Suhrawardi Uddan, Kazi Nazrul Islam Avenue, Mirpur are found to be locations with higher extensive and minor injury related incidents. The map of these incidents in the metropolitan area is depicted in figure 4.15:

Figure- 4. 15 Extensive or minor injury related crashes of Dhaka Metropolitan Area 2007-2011

4.3.3 Motor Vehicle Collision

In comparison to the fatal crashes and major and minor injuries, the percentage of vehicular or motor collisions is not significant. The southern part of the metropolitan area is more vehicular collision prone than the rest of the metropolitan area. Principal arterial roads like Mirpur,

Panthapath, Suhrawardi Udaan Road have more incidents of vehicular collisions than the rest of the city. The vehicular collision map of the DMA is given below:

Figure- 4. 16 Motor Collision related crashes of Dhaka Metropolitan Area, 2007-2011

4.3.4 Hotspot Analysis of the Traffic Crash Locations

The northern and southern part of the metro area have crash hotspots with a small portion of the central city area. No cold spot was found in this analysis which indicated the uniform spatial severity of this locality. A primary reason behind the hotspots of the northern and southern part is that these two points are the entry point to the Dhaka metro area from the northern and southern part of the country. As there is no dedicated exit or entrance ramp available, the vehicles directly enter the metropolitan area from the highways and most of the cases crashes occur in that moment due to the speed and negligence of the drivers and the pedestrians.

Figure- 4. 17 Hotspot analysis of the crash points

4.3.5 Spatial Auto Correlation Analysis

To understand the spatial relationship among the traffic crash points, this study conducted a

Moran’s I Index test. For that, each of the points are weighted based on their number of crashes and their respective severity level. The findings indicated that these crash points are highly clustered, and the result is also significant. It represented that, during the period 2007-2011, traffic 64

crashes occurred repeatedly in a same level of intensity, nature and number of the victims. This indicated the lack of awareness among the drivers and authorities because if proper initiatives were taken to those hotspot locations after each of the incidents, the overall scenario would not be as clustered.

Table 4. 4 Spatial autocorrelation analysis

Moran's Index: 0.074371

Expected Index: -0.000368

Variance: 0.000043

z-score: 11.440678

p-value: 0.000000

4.3.6 Network Based Kernel Density Estimation

After understanding the clustered nature of the incident points, this study took an approach to identify the locations which are most and moderately vulnerable. For that, this study conducted a

Network Based Kernel Density Estimation technique. After finding the locations which are most vulnerable to fatal crashes, this study also conducted a study on the population and crash intensity of those areas. The results indicated that Ward Groups 1, 9, 10, 14, 17, 19, 36, 37, 39, 40, 41, 53,

56, 84, 86, and 90 are found to be the most vulnerable wards. The resulting maps are given below.

Figure- 4. 18 Network based kernel density estimation (Linear representation)

The following map is based on the population of those wards and their respective populations.

Figure 4. 19 Network based kernel density estimation (Point representation)

The map indicated a relationship between the population and area of those wards with their respective crash frequencies. So, this study also developed a correlation matrix among the area of these wards, their respective population, and crash frequencies.

4.3.7 Relationship among the Land use pattern, population, area, and crash frequencies

Table 4. 5 Correlation matrix

Area of the

Population density Crash Frequencies Wards

Population density 1

Crash Frequencies 0.4422 1

Area of the Wards 0.6859 0.538 1

The frequency of the traffic incidents and the population of those wards have a correlation of .44.

The relationship between the area of the wards and crash frequencies are also significant with a

.54 value. As expected, areas of these wards and the population density have a positive strong correlation. So, these findings indicate that the wards with large areas and moderate-high population density have a positive relationship with the crash frequencies of those wards.

Then this study also wanted to focus on the land use situation of those vulnerable wards to traffic crashes. The results are given below:

Table 4. 6 Land use pattern of the vulnerable wards

WARD_NO Population Crash DAP Proposal Current Land Use

Density 2011 Frequencies

1 183298 133 Mixed Use Residential- commercial

17 196479 88 Mixed Use Commercial- Industrial

84 58741 86 Mixed Use Residential- commercial

40 90224 71 Mixed Use Residential- commercial

39 67876 67 Mixed Use Residential- commercial

19 96291 61 Restricted Use Special Use

(Cantonment

Area)

37 103274 61 Industrial Industrial

86 56766 47 Residential Residential

41 65984 42 Mixed use Residential industrial

53 55920 42 Mixed use Residential -Commercial

10 87879 40 Mixed use Commercial -Industrial

56 38201 36 Institutional Special Use (Institutional zoning)

Zoning

(Educational-

Residential)

9 71260 34 Mixed use Residential -Commercial

36 59639 31 Mixed use Educational -Commercial

90 66637 31 Mixed Use • Commercial- Industrial.

• Residential -

Commercial.

14 163797 30 Residential -Commercial

Source- DAP, Rajuk,2011

Residential Commercial and Commercial Industrial Mix land uses are prominent in the ward zones

with high frequency of traffic incidents. But the Detailed Area Plan of this metropolitan area also

suggested mixed land use for those zones. So, does this approach of mixed use zoning has a

negative impact on the traffic safety of those communities? It could be an interesting topic for

discussion in the future, but according to this study, it has some negative impact on the road safety

and security of the people of those communities.

Chapter 5

Conclusions & Policy Implications

The overall crash severity level of Dhaka is alarming. A significant part of the crashes are fatal collisions. Though the overall traffic crash number is in a decreasing trend, the frequency of fatal crashes is still in an increasing static trend. Pedestrians are the primary victims. A major reason behind this high frequency of pedestrian collisions in the absence of necessary pedestrian safety measures and also the reluctance of the pedestrians to follow the safety measures. In many of the critical intersections in Dhaka, the city corporations created elevated passage or over-bridge for the convenience of the passing of the pedestrians. But since it is a little bit more time consuming than to pass through the middle of the road, most of the pedestrians do not want to use these elevated passages. As a result, these structures lose their primary cause and are lately encroached by illegal street vendors and homeless people. None of the intersections have any pedestrian crossing signals. The drivers of the vehicles are also not interested in sharing their right-of-way with the pedestrians. These issues all result in these fatal crashes.

According to this study, the road geometry has no significant impact on the intensity and frequency of crashes. The no intersection or general road segments and 3-way intersection points are more susceptible to fatal crashes than to extensive injuries. Pedestrian, rear end and head-on collisions are found to be the most significant collision type. A substantial proportion of these crashes occur

between the motorized and non-motorized vehicle. There is no dedicated lane for the non- motorized vehicles like rickshaws and bikes on the road, and they share the same lane with the motorized vehicles like cars and buses. During the rush hours, when people are in a hurry, these speed mismatches cause a lot of the rear end collisions. Then comes the issue of the absence of dedicated bus stops. Many of these crashes occurred when the public transports unload passengers in the middle of the road, and before they can pass over the road to the sidewalk, another vehicle runs over them from their back.

Police-controlled intersections have a positive impact in reducing crashes. Still, the general traffic perception of South Asian cities is not welcoming to technology. The drivers are not comfortable to follow the automatic traffic signals and they only follow the instruction of the traffic police. But for an overpopulated like Dhaka, it is challenging to ensure traffic police at every intersection of the road when they also have limited resources. One-way roads are more vulnerable to crashes. As most of the crashes are from the rear end and right angle, signals and lack of traffic knowledge play more roles in the crash severities than the traffic direction here. Road dividers have a positive impact on crash reduction.

Night time is more vulnerable to fatal crashes while daytime is for extensive or minor injuries. As the amount of traffic is lower in the night time, the drivers become reckless. Speeding causes many of the night time fatal crashes. On the other hand, during the daytime, most of the roads are congested, and it is not possible to speed up, which reduces the severity of the crashes. That is also the reason behind the greater vulnerability of the national and regional highways in comparison to the city or feeder roads.

Speed breakers have an impact on decreasing the fatal crashes. But these types of traffic calming measures are not common in the roadways of Dhaka City, and hence a reason behind the high number of fatal crashes.

Fatal crashes are prominent all through the years. The number is highest in April and lowest in

December.

From the spatial analysis, it is found that there is no significant cold spot in the whole DMA with respect to traffic crashes. This result indicates how transportation planning and safety of the road sectors is absent in this metropolitan area. Crash points are highly clustered. A principal reason behind that is the lack of awareness after each incident. The crashes are occurring in the same places and road segments repeatedly, but neither the authority nor the commuters nor the drivers are concerned about that. As a result, this study found this highly clustered, uniform spatial distribution for this city. The north, east and south side of the metropolitan area are more vulnerable to the road traffic crashes than the western part of the city. As these two portions are the entry point to this metropolitan area, the highway sections of these two portions are responsible for most of the crashes. Unlike the highway design of North America, the highways of Bangladesh lack of any dedicated exit or ramp. So, the vehicles enter the city directly from the highways without any prior signals or cautions. As a result, speeding and late maneuvering cause many of the fatal crashes and pedestrians are the worst sufferer. Wards with moderate-high population and mixed

(residential-commercial, commercial- industrial) land use are more crash-prone than the dedicated residential or educational zones.

Some of the factors which are found responsible for this situation are rapid growth in urban population, poor infrastructure, or lack of infrastructure with a low level of maintenance. Weak and outnumbered law enforcement agencies with inadequate capacity for planning and implementing projects are making the situation more difficult to deal with. Then comes the issues of overlapping and poor coordination among different ministries, departments and municipal agencies entrusted with managing urban transport. There is no robust public transportation regulation system. The whole vehicle fitness examination is rigged and can be quickly passed with a little bribe. There is also a complete absence of a driving training program and awareness about travel safety among the drivers of both public and private vehicles. Despite the suggestions in the

Transportation Improvement Plans, this city lacks infrastructural support for pedestrian safety.

In general, human errors play more of a role in these crashes than the built environmental or non- human factors for this metropolitan area. Alam & Spainhour,2008; Alam & Spainhour,2009 in their analysis on Florida also made this same conclusion. Alcohol use, inattention, young drivers, misjudging speeds of other vehicles, failure to observe other vehicles, disregarding traffic signals, and high speed are the reasons for these human errors. For the Dhaka Metropolitan Area, though alcohol use is not a major factor due to the religious restraint, the rest of the factors are prominent for most of the crashes. Irrespective of the difference in economic, social and infrastructure, the human errors regarding the traffic crashes have similarities all over the world. But the difference is that the traffic context of this metropolitan area magnifies these errors more in comparison to other less populated cities. The overpopulation, and absence of necessary traffic safety measures with lack of knowledge of traffic rules and regulations intensify any minor mistakes on the roads.

5.1 Recommendations and Policy Implications

For the improvement of the traffic safety, this study provides the following recommendations.

5.1.1 Design and Infrastructural Level

Road geometry and intersection treatment can be performed by minor changes/improvements in geometric road layout like channelization of traffic and traffic islands and the use of roundabouts where necessary. Other approaches like widening of the road and construction of bridges are also in need.

For pedestrian safety, adequate pedestrian facilities like safe crossing, treatments or construction of sidewalks/footpaths and foot-over bridges, safer zones, grade separation, time separation, raised meridians, etc. should be introduced. It is also necessary to force the pedestrians to use the foot- over bridges or over-passes or underpasses through proper channelization.

To reduce head-on and rear end collisions, provision of special facilities (e.g., separate lanes) for non-motorized vehicles and designated bus lanes are necessary. With that, improved access controls, road surface, roadway shoulder, cross-sections, sight distances, alignments, traffic signs,

traffic signals, road markings, traffic calming devices, and lighting should be installed. Haphazard parking on the roadside and illegal use of footpath should be eradicated.

A pedestrian count and travel speed survey of the most vulnerable roads should be performed on a regular basis. Based on the findings of these analyses, infrastructural measures like introducing and utilizing different traffic calming measures (speed hump, raised intersection, closure, central island narrowing, etc.) on the roads can be considered.

5.1.2 Policy Level

It is necessary to ensure safer vehicle standard for roadworthiness as well as for crashworthiness for the road safety of Dhaka Metropolitan Area. Effective enforcement of laws and provision for adequate penalties for violating the rules can help to make that happen. With that, strict driving licensing and improvement of the existing “Motor Vehicle Ordinance” is critically important. The technical inspection system for checking and testing of vehicles and periodic safety audits should be strengthened. Detailed land use plan must be developed, and proper implementation should be ensured.

Prompt emergency assistance and efficient trauma care management are essential in minimizing the road crash deaths and therefore should be introduced. But this entire system is completely absent for Dhaka and for the country as a whole. Many of the fatal crashes could be avoided if this prompt emergency system was there. Then there are issues of traffic congestion which block the ambulances in the middle of the road. As a result, they failed to take the survivor of the crashes to the hospital on time, and the patient died on the way.

To ensure the natural flow of traffic to the road, it is an urgent need to rehabilitate the street hawkers, mobile vendors and slum dwellers from carriageways and footpaths. When this illegal encroachment takes all the sidewalks, people have no other choice than to walk down the road which results in many of the pedestrian crashes.

Electronic and print media should publish articles and news on traffic safety rules and crash incidents regularly to create strong public awareness. They can also raise awareness on developing an Emergency Response System for the road traffic crashes.

5.1.3 Educational Level

A significant limitation of this study was the lack of abbreviated accident scale of the crashes in the database, and the reason behind this absence is the lack of adequate knowledge of these types of crash severity terms among the local police, who are the primary source of this data. For that reason, it is necessary to ensure adequate training for the police officers and concerned staffs about traffic management, crash data collection and filling the ARF’s. With that, the incorporation of modern technologies (e.g., using GIS techniques instead of MAAP5 software) in analyzing and upgrading traffic crashes database management system would make the total system more efficient and research-friendly.

The idea of developing and implementing community-based road safety programs is new for

Bangladesh but gaining popularity day by day. This approach of community involvement in a way helps to create more public awareness and in return it creates an impact on the policy level of the country. This approach includes but is not limited to developing proper road safety resource materials and the promotion of road safety education in schools. With that is also necessary to strengthen the institutional and professional ability of all the concerned agencies, stakeholders, 77

NGOs, private companies, and organizations for the successful implementation of road safety measures. It can also play a key role in securing legitimate and adequate funding to support road safety initiatives including research, training, and road safety promotional activities.

A major reason behind the traffic crashes of this metropolitan area is the driver’s fatigue. Here most of the drivers of public transportation are paid based on their number of trips rather than an hour or any monthly basis. It encourages them to make as many trips as possible and results in speeding. Over that, there are no specific regulations about the average daily working hours of these drivers. Most of them work on average 12-14 hours daily which reduce their reflex, make them tired and results in traffic crashes. It is necessary to promote strategies to counteract the effect of drivers fatigue in driving for sustained periods.

This study is one of very few that investigate causes of traffic fatalities in Dhaka, a city of about

15 million. Severe crashes cause not only the loss of lives of people, but also significant economic loss of the country. Reducing traffic crashes is one of the major factors that the traffic engineers and transportation planners need to focus on in a crowded city like Dhaka. For that, appropriate and evidence-based policies need to be in place by the transportation planners and policy makers.

The findings of this study could play significant roles towards that end and help transportation planners and policy makers in preparing such policies in order to reduce traffic crashes. Traffic collisions are a significant accountability for law enforcement and emergency response agencies.

The broader administrative periphery of the traffic safety infrastructure is composed of state and local law enforcement, fire departments, and emergency medical services. So, coordination among all these agencies, understanding the nature of these incidents and identifications of the locations with higher risks can help to develop strategies, programs, and preventative tactics to respond more

efficiently and quickly to crashes. It can also prevent future collisions and reduce mortality rates in targeted areas.

References

• Adesunkanmi, A. R. K., L. M. Gini, O. A. Oyelami, and O. S. Badru, 2000. Road Traffic

Crashes to African Children: Assessment of Severity Using the Injury Severity Score

(ISS). Injury, Vol. 31, No. 4, pp. 225–228.

• Afshin Shariat Mohaymany, Matin Shahri & Babak Mirbagheri ,2013. GIS-based method

for detecting high-crash-risk road segments using network kernel density estimation.

Geo-spatial Information Science 16:2, 113-119.

• Agran, P.F., Winn, D.G., Anderson, C.L., Tran, C., Del Valle, C.P., 1996. The role of the

physical and traffic environment in child pedestrian injuries. Pediatrics 9, 1096–1103.

• Aguero-Valverde, J., and P. P. Jovanis, 2008. Analysis of Road Crash Frequency with

Spatial Models. Transportation Research Record: Journal of the Transportation Research

Board, No. 2061, Transportation Research Board of the National Academies,

Washington, D.C., pp. 55–63.

• Ahmed, B., & Ahmed, R. ,2012. Modeling Urban Land Cover Growth Dynamics Using

Multitemporal Satellite Images: A Case Study of Dhaka, Bangladesh, ISPRS

International Journal of Geo-Information, 1(1), 3-31.

• Ahmed, Bayes, 2013. Traffic Crash Study in Dhaka City (2007-2011). Clean Air and

Sustainable Environment (CASE) Project. The World Bank.

• Ahsan, H. M.,2012. Road Safety in Bangladesh: Key Issues and Countermeasures.

Forum, monthly publication of Daily Star, Vol. 6, No. 7, July.

• Alam, Bhuiyan, & Spainhour, Lisa,2008 “Contribution of Behavioral Aspects of Older

Drivers to Fatal Crashes in Florida”. Transportation Research Record, 2078: 49–56.

• Alam, Bhuiyan., & Spainhour, Lisa,2009. “Contributing Factors for Young At-Fault

Drivers in Fatal Traffic Crashes in Florida”. Journal of Transportation Safety & Security,

1(2): 152–168.

• AlEassa, E., H. Eid, and F. Abu-Zidan, 2013. Effects of Vehicle Size on Pedestrian Injury

Pattern and Severity: Prospective Study. World Journal of Surgery. 37, No. 1, pp. 136–

140.

• Al-Ghamdi, A.S., 2002. Pedestrian–vehicle crashes and analytical techniques for

stratified contingency tables. Accident Analysis and Prevention 34, 205–214.

• Al-Mahmood, S. Z, 2007. Death on the Asphalt. Star Weekend, Daily Star.

• Anderson, T.K., 2009. Kernel density estimation and K-means clustering to profile road

crash hot spots. Accident Analysis & injury Prevention 41, 359–364.

• Anowar, S., Alam, M. D., Raihan, M. A. 2008. Analysis of Crash Patterns at

Selected Intersections of an Urban Arterial. Proceedings of the 21st ICTCT Workshop.

Melbourne, Australia.

• Anselin, L, 1995. Local Indicators of Spatial Association – LISA. Geographic Analysis

27,93–115.

• Atkins, R.M., Turner, W.H., Duthie, R.B., Wilde, B.R., 1988. Injuries to pedestrians in

road traffic crashes. Br. Med. J. 297 (6661), 1431–1434.

• Peeters et al,2015. Getis–Ord’s hot- and cold-spot statistics as a basis for multivariate

spatial clustering of orchard tree data. Computers and Electronics in Agriculture 111, PP

140–150.

• Bailey, T.C., Gatrell, A.C., 1995. Interactive Spatial Data Analysis. Longman Scientific,

Harlow, UK

• Ballesteros, M.F., Dischinger, P.C., Langenberg, P., 2003. Pedestrian injuries and vehicle

type in Maryland, 1995–1999. Accident Analysis & injury Prevention 36 (1), 73–81

• Barua, U., and R. Tay, 2010. Severity of Urban Transit Bus Crashes in Bangladesh.

Journal of Advanced Transportation. 44, No. 1,pp. 34–41

• Bhalla, K., M. Ezzati, A. Mahal, J. Salomon, and M. Reich. 2007. A risk-based method

for modeling traffic fatalities. Risk Analysis27 (1): 125–36.

• Black, W.R., Thomas, I., 1998. Crashes on Belgium’s motorways: a network

autocorrelation analysis. Journal of Transportation Geography. 6 (1), 23–31

• Box, P., 2004. Curb-parking problems: overview. Journal of Transport. Engineering 130

(1), 1–5.

• Briggs, D. J. 2000. Exposure assessment. In Spatial epidemiology: Methods and

applications, ed. P. Elliott, J. Wakefield, N.Best, and D. Briggs, 336–59. New York:

Oxford University Press.

• Brugger, K., Boehnke, D., Petney, T., Dobler, G., Pfeffer, M., Silaghi, C., & Pfister, K.

2016. Modeling GIS, Risk Assessment, Economic Impact a Density Map of the Tick-

Borne Encephalitis and Lyme Borreliosis Vector Ixodes ricinus (Acari: Ixodidae) for

Germany.

• BRTA Annual Traffic report, 2010.

• Chen F-W, Liu C-W , 2012. Estimation of the spatial rainfall distribution using inverse

distance weighting (IDW) in the middle of Taiwan. Paddy Water Environ

• Delmelle, E.C., Thill, J.-C., 2008. Urban bicyclists – a spatial analysis of adult and youth

traffic hazard intensity. Transportation Research Records 2074, 31–39.

• Detailed Area Plan, Dhaka,2013. RAJUK (Rajdhani Unnoyon Kotripokkho-Development

Authority)

• Dhaka South City Corporation. ,2012. History of Dhaka City Corporation, Dhaka as the

Capital of Bangladesh, Dhaka 1000, Bangladesh. Retrieved from: www.dhakacity.org,

accessed on 19th October 2017.

• Dumbaugh, E., and R. Rae. 2009. Safe urban form: Revisiting the relationship between

community design and traffic safety. Journal of the American Planning Association75

(3): 309- 29.

• Eluru, N., C. R. Bhat, and D. A. Hensher,2008. A Mixed Generalized Ordered Response

Model for Examining Pedestrian and Bicyclist Injury Severity Level in Traffic Crashes.

Accident Analysis & Prevention. 40,

• Erdogan, S., Yilmaz, I., Baybura, T., Gullu, M., 2008. Geographical information systems

aided traffic crash analysis system case study: city of Afyonkarahisar. Accident Analysis

& Prevention 40 (1), 174–181.

• Ewing, R., Dumbaugh, E., 2009. The built environment and traffic safety: a review of

empirical evidence. Journal of Planning. Literature 23, 347–367.

• Fan Ye and Dominique Lord, 2013.Comparing three commonly used crash severity

models on sample size requirements: Multinomial logit, ordered probit and mixed logit

models. Analytic Methods in Crash Research.

• Geedipally, S.R., Turner, P.A., Patel, S., 2011. Analysis of motorcycle crashes in

Texas. Accident Analysis & Prevention.

• GES, 2005. National Automotive Sampling System General Estimates System, National

Center for Statistics, and Analysis of the National Highway Traffic Safety

Administration.

• Global status report on road safety, 2015, WHO.

• Griffith, D, 2008. Spatial-Filtering-Based Contributions to a critique of Geographically

Weighted Regression (GWR). Environmental Planning A, 40(11), 2751–2769.

• Haining, R. 1993.Spatial data analysis in the social and environmental sciences. New

York: Cambridge University Press.

• Harwood, D.W., Torbic, D.J., Dum, J., Zegeer, C., Srinivasan, R., Carter, D., Raborn, C.,

Lyon, C., Persaud, B., 2008. Pedestrian safety prediction methodology. Final report,

NCHRP (National Cooperative Highway Research Program), project 17–26, document

129, Transportation Research Board, Washington D.C.

• Holubowycz, O.T., 1995. Age, sex, and blood alcohol concentration of killed and injured

pedestrians. Accident Analysis & Prevention 27 (3), 417–422.

• Hoque, M. M. ,2004. The Road to Road Safety: Issues and Initiatives in Bangladesh,

Regional Health Forum, 8(1), 39-51.

• Hoque, M. M., McDonald, M.,& Hall, R. D., 2001.Road Safety Improvements in

Developing Countries: Priority Issues and Options. Proceedings of the Twentieth

Australian Road Research Board (ARRB) Conference, Australia.

• http://www.rajukdhaka.gov.bd/rajuk/developmentHome.

• Huda, F., D. Sah, and S. B. Sharma,2010. Impact of Driver Characteristics on the

Severity of Injuries and Final Outcome of Four-Wheeler Auto Mobile Crash Victims.

Internet Journal of Surgery. 22, No. 2.

• Huda, F., D. Sah, and S. B. Sharma,2010. Impact of Driver Characteristics on the

Severity of Injuries and Final Outcome of Four-Wheeler Auto Mobile Crash Victims.

Internet Journal of Surgery. 22, No. 2.

• Institute for Road Safety Research (IRSR). Fact Sheet of Roundabouts. 2010. Available

at: http://www.swov.nl/rapport/Factsheets/ UK/FSRoundabouts.pdf. Accessed November

12, 2016.

• Islam, S., and M. Rahman,2010. Highways Unsafe for Poor Policing: Chaotic Vehicular

Movement, Reckless Driving Causing Frequent Crashes. Daily Star, Nov. 21, 2010.

http://archive.thedailystar.net/newDesign/news-details.php?nid=163049. Accessed March

13, 2016.

• Jehle, D., Cottington, E., 1988. Effect of alcohol consumption on outcome of pedestrian

victims. Annual Emerging. Medicine 17, 953–956.

• Kaplan, S., and C. G,2012. Prato. Risk Factors Associated with Bus Crash Severity in the

United States: A Generalized Ordered Logit Model. Journal of Safety Research. 43, No.

3, pp. 171–180.

• Kim, J.-K., G. F. Ulfarsson, V. N. Shankar, and S. Kim,2008. Age and Pedestrian Injury

Severity in Motor-Vehicle Crashes: A Heteroskedastic Logit Analysis. Crash Analysis

and Prevention. 40, No. 5, pp. 1695–1702.

• Kim, J.K., Kim, S., Ulfarsson, G.F., Porrello, L.A., 2007. Bicyclist injury severities in

bicycle–motor vehicle crashes. Accident Analysis & Prevention 39 (2), 238–251.

• King, M.R., Carnegie, J.A., Ewing, R., 2003. Pedestrian safety through a raised median

and redesigned intersections. Transportation Research Record 1828, 56–66.

• Klop, J.R., Khattak, A.J., 1999. Factors influencing bicycle crash severity on two-lane,

undivided roadways in North Carolina. Transportation Research Record 1674, 78–85.

• Knox, P., McCarthy, L., 2005. Urbanization: An Introduction to Urban Geography,

second ed. Pearson/Prentice Hall, Upper Saddle River, NJ.

• Krisp, J.M., Durot, S., 2007. Segmentation of lines based on point densities – an

optimization of wildlife warning sign placement in southern Finland. Accident Analysis

& Prevention 39 (1), 38–46.

• Kuo, P., Zeng, X., Lord, D., 2012. Guidelines for choosing hot-spot analysis tools based

on data characteristics, network restrictions, and time distributions. In: Proceedings of the

91 Annual Meeting of the Transportation Research Board, January 22–26, Washington,

DC.

• Land Transport Authority Academy (LTA). 2012. Cluster 7/Module 3 (C7/M3): Safety

Issues, Capacity Building Program: Building Leaders in Urban Transport Planning

(LUTP). Singapore.

• Lee, C., Abdel-Aty, M., 2005. Comprehensive analysis of vehicle–pedestrian crashes at

intersections in Florida. Accident Analysis & Prevention 37 (4), 775–786. 86

• Lefler, D., Gabler, H., 2003. The fatality and injury risk of light truck impacts with

pedestrians in the United States. Accident Analysis & Prevention 36 (2), 295–304.

• Li, L., Zhu, L., Sui, D.S., 2007. A GIS-based Bayesian approach for analyzing spatial–

temporal patterns of intra-city motor vehicle crashes. Journal of Transportation

Geography 15 (4), 274–285.

• Lord, D., Bonneson, J., 2005. Calibration of predictive models for estimating the safety

of ramp design configurations. Transportation Research Record 1908, 88–95.

• Lord, D., Mannering, F., 2010. The statistical analysis of crash-frequency data: a review

and assessment of methodological alternatives. Transportation Research Part A 44 (5),

291–305.

• Lu, J., F. Pirinccioglu, and J. Pernia,2004. Safety Evaluation of Right-Turns followed by

U-turns at Signalized Intersection (Six or More Lanes) as an Alternative to Direct Left

Turns: Conflict Data Analysis. Florida Department of Transportation, Tallahassee.

• Lu, Y., Chen, X., 2007. False alarm of planar K-function when analyzing urban crime

distributed along streets. Social Science Research 36 (2), 611–632

• M. Kamruzzaman, M. Mazharul Haque and Simon Washington,2014. Analysis of Traffic

Injury Severity in Dhaka, Bangladesh,” Transportation Research Record: Journal of the

Transportation Research Board, no. 2451:121-130.

• McGrew Chapman & Monroe, Charles, (2000). An Introduction to Statistical Problem

Solving in Geography (2nd Edition). Waveland Press Inc.

• Miles-Doan, R., 1996. Alcohol use among pedestrians and the odds of surviving an

injury: evidence from Florida Law Enforcement Data. Accident Analysis & Prevention

28 (1), 23–31. 87

• Miles-Doan, R.1996. Alcohol Use Among Pedestrians and the Odds of Surviving an

Injury: Evidence from Florida Law Enforcement Data. Accident Analysis & Prevention,

Vol. 28, No. 1, pp. 23–31.

• Ministry of Communications, Planning Commission ,2008. Integrated Multi Modal

Transport Policy Final Draft, Prepared for the Council of Advisors, Government of the

People’s Republic of Bangladesh.

• Ministry of Environment and Forests and Ministry of Communication,2010.

Developments in EST in Bangladesh. Presented at 5th Regional Environmentally

Sustainable Transport (EST) Forum in Asia, Bangkok, Thailand.

• Ministry of Environment and Forests and Ministry of Communication. Developments in

EST in Bangladesh, 2010. Presented at 5th Regional Environmentally Sustainable

Transport (EST) Forum in Asia, Bangkok, Thailand.

• Mitra, S., S. P. Washington, and I. Van Schalkwyk.2007. Important Omitted Spatial

Variables in Safety Models: Understanding Contributing Crash Causes at Intersections.

Presented at 86th Annual Meeting of the Transportation Research Board, Washington,

D.C.

• Moran, P,1948. The Interpretation of Statistical Maps. Institute of Statistics, Oxford

10(2), 243–251.

• Mitchell, A., 2005. The ESRI guide to GIS analysis. In: Spatial Measurements &

Statistics. ESRI Press, Redlands, CA, vol. 2.

• Najmul, H. A., 2003. Enforcement in Mixed Traffic Conditions in Bangladesh. In

Benchmarking Road Traffic Enforcement for RS. Institute of Road Traffic Education,

New Delhi, India. 88

• National Road Safety Council, Bangladesh Road Transport Authority, Ministry of

Communications. 2011. National Road Safety Strategic Action Plan (NRSSAP) 2011-

2013. Government of the People’s Republic of Bangladesh. Dhaka.

• No Driving License Without Test. Daily Star, Aug. 26, 2011.

http://archive.thedailystar.net/newDesign/news-details.php?nid=200275. Accessed March

13, 2016.

• Gini, F. O., S. O. Ajike, O. N. Obuekwe, and O. Fasola, 2009. A Prospective Multicenter

Study of Injury Profile, Severity, and Risk Factors in 221 Motorcycle-Injured Nigerian

Maxillofacial Patients. Traffic Injury Prevention, Vol. 10, No. 1, pp. 70–75.

• Okabe, A., Satoh, T., Sugihara, K., 2009. A kernel density estimation method for

networks, its computational method and a GIS-based tool. Int. Journal of Geographic

Information Science 23 (1), 7–32.

• Okabe, A., Sugihara, K., 2012. Spatial Statistics along Networks: Statistical and

Computational Methods. John Wiley, Chichester, UK.

• Okabe, A., Yamada, I., 2001. The K-function method on a network and its computational

implementation. Geographic Analysis 33, 271–290.

• Okabe, A., Yomono, H., Kitamura, M., 1995. Statistical analysis of the distribution of

points on a network. Geographic Analysis 27 (2), 152–175.

• Okabe, A.; Satoh, T., Sugihara, K, 2009. A Kernel Density Estimation Method for

Network, its Computational Method and a GIS-Based Tool. International Journal of

Geographic Information Science ,23(1), 7–32.

• Okunuki, K., Okabe, A., 1999. A Computational Method for Optimizing the Location of

a Store on a Continuum of a Network when Users’ Choice Behavior Follows the Huff 89

Model. Discussion Paper #19, Center for Spatial Information Science at the University of

Tokyo.

• Oris, W.N., 2011. Spatial Analysis of Fatal Automobile Crashes in Kentucky. M.S.

Thesis, Department of Geography and Geology, Western Kentucky University, Bowling

Green, Kentucky.

• Ord, J.K., Getis, A., 1995. Local spatial autocorrelation statistics: distributional issues

and an application. Geogr. Anal. 27 (4), 286–306.

• Ossiander, E. M., and P. Cummings. 2002. Freeway speed limits and traffic fatalities in

Washington State. Crash Analysis and Prevention 34 (1): 13–18.

• Pitt, R., Guyer, B., Hsieh, C., Malek, M., 1990. The severity of pedestrian injuries in

children: an analysis of the Pedestrian Injury Causation Study. Accident Analysis &

Prevention 22 (6), 549–559.

• Prevention. http://whqlibdoc.who.int/publications/2004/9241562609.pdf

• Pulugurtha, S.S., Krishnakumar, V.K., Nambisan, S.S., 2007. New methods to identify

and rank high pedestrian crash zones: an illustration. Accident Analysis & Prevention 39

(4), 800–811.

• Quddus, M. A., R. B. Noland, and H. C. Chin,2002. An Analysis of Motorcycle Injury

and Vehicle Damage Severity Using Ordered Probit Models. Journal of Safety Research,

Vol. 33, pp. 445–462.

• Rahman, M.,2010. Guilty yet Get off Scot-Free. Daily Star, Dec. 5, 2010. http:// archive.

thedailystar.net/newDesign/news-details.php? nid=164837. Accessed March 13, 2016.

• Rodrigue, J.-P., Comtois, C., Slack, B., 2009. The Geography of Transport Systems,

second ed. Routledge, New York, NY. 90

• Roudsari, B.S., Mock, C.N., Kaufman, R., Grossman, D., Henary, B.Y., Crandall, J.,

2004. Pedestrian crashes: higher injury severity and mortality rate for light truck vehicles

compared with passenger vehicles. Injury Prevention 10, 154–158.

• Savolainen, P., Mannering, F., Lord, D., Quddus, M., 2011. The statistical analysis of

highway crash-injury severities: a review and assessment of methodological alternatives.

Crash Analysis and Prevention 43 (5), 1666–1676.

• Schmucker, U., R. Dandona, G. A. Kumar, and L. Dandona,2011. Crashes Involving

Motorized Rickshaws in Urban India: Characteristics and Injury Patterns. Injury, Vol. 42,

No. 1, pp. 104–111.

• Silverman, B.W., 1986. Density Estimation for Statistics and Data Analysis. Chapman

Hall, London, UK.

• Status Paper on Road Safety Problems in Bangladesh: The Bangladesh Country Paper

(2007). United Nations Economic and Social Commission for Asia and the Pacific,

Bangkok, Thailand.

• Stephan, K., M. Kelly, R. McClure, S.-A. Seubsman, V. Yiengprugsawan, C. Bain, and

A. Sleigh, 2011. Distribution of Transport Injury and Related Risk Behaviours in a Large

National Cohort of Thai Adults. Accident Analysis & Prevention 43(3), pp. 1062–1067.

• Stone, M., Broughton, J., 2003. Getting off your bike: cycling crashes in Great Britain in

1990–1999. Accident Analysis & Prevention 35 (4), 549–556

• Sze NN, Wong SC. 2007. Diagnostic analysis of the logistic model for pedestrian injury

severity in traffic crashes. Crash Analysis & Prevention 39:1267–1278.

• Tay R, Rifaat SM.,2007. Factors contributing to the severity of intersection crashes. J

Adv. Transp.41:245–265. 91

• Tay, R., Choi, J., Kattan, L., Khan, A., 2011. A multinomial logit model of pedestrian–

vehicle crash severity. International Journal of Sustainable Transportation 5, 233–249.

• Tester, J.M., Rutherford, G.W., Wald, Z., Rutherford, M.W., 2004. A matched case

control study evaluating the effectiveness of speed humps in reducing child pedestrian

injuries. Am. Journal of Public Health 94, 646–650.

• The Central Intelligence Agency(CIA) ,2015. The World Factbook,2010, Country

Profile: Bangladesh, Office of Public Affairs, Washington, D.C. 20505, USA. Retrieved

from: https://www.cia.gov/library/publications/the-worldfactbook/geos/bg.html ,

accessed on 16th October,2017 .

• The World Bank. Motor Vehicles (per 1,000 People): The World Bank. 2013. Archived

from the Original http://data.worldbank.org/indicator/IS.VEH.NVEH.P3(2014). Accessed

28 June. 2016.

• Train, K.E., 2003. Discrete Choice Methods with Simulation. Cambridge University

Press, New York, NY.

• Truong, L.T., Somenahalli, S.V.C., 2011. Using GIS to Identify Pedestrian-Vehicle Crash

Hot Spots and Unsafe Bus Stops. Journal of Public Transport. 14 (1), 99–114.

• Ulfarsson G, Mannering F. 2004. Difference in male and female injury severities in sport-

utility vehicle, minivan, pickup and passenger car crashes. Accident Analysis &

Prevention 36(2):135–147.

• UNES, 2007. Status Paper on Road Safety Problems in Bangladesh: The Bangladesh

Country Paper. United Nations Economic and Social Commission for Asia and the

Pacific, Bangkok, Thailand.

• Wang, X., and M. Abdel-Aty,2006. Temporal and Spatial Analysis of Rear-End Crashes

at Signalized Intersections. Accident Analysis & Prevention 38, No. 6, pp. 1137–1150.

• Washington, S., Karlaftis, M., Mannering, F., 2011. Statistical and Econometric

Methods for Transportation Data Analysis, 2nd ed. Chapman and Hall/CRC, Boca

Raton, FL No. 3, 2008, pp. 1033–1054.

• Whitelegg, J. 1987. A geography of road traffic crashes. Transactions of the Institute of

British Geographers 12 (2): 161–76.

• WHO (World Health Organization). 2004. World Report on Road Traffic Injury with

multinomial logit model. Transportation Research Record 2265, 62–69.

• World Health Organization (WHO),2011. Global Plan for the Decade of Action for Road

Safety 2011-2020, Geneva 27, Switzerland.

• World Report on Road Traffic Injury Prevention,2004. World Health Organization,

Geneva.

• Xie, Z.; Yan, J, 2008. Kernel Density Estimation of Traffic Crashes in a Network Space.

Computers, Environment & Urban System,35(5), 396–406.

• Xie, Zhixiao; Yan, Jun, 2013. Detecting traffic crash clusters with network kernel density

estimation and local spatial statistics: an integrated approach. Journal of Transport

Geography 31 ,64–71.

• Zajac S, Ivan J. 2003. Factors influencing injury severity of motor vehicle crossing

pedestrian crashes in rural Connecticut. Accident Analysis & Prevention 35:193–211.

• Zhu, X., and S. Srinivasan,2011. Modeling Occupant-Level Injury Severity: An

Application to Large-Truck Crashes. Accident Analysis & Prevention 43, No. 4, pp.

1427–1437.

• Ziqiang Zeng, Wenbo Zhu, Ruimin Ke, John Ash, Yinhai Wang,, Jiuping Xu, Xinxin

Xu,2016. A generalized nonlinear model-based mixed multinomial logit approach for

crash data analysis. Accident Analysis & Prevention 99, pp- 51-65.