Pedestrian-vehicle Crashes Research Articles

A multinomial logit model of pedestrian-vehicle crash severity in North Carolina

Abstract This article develops a multinomial logit (MNL) model to investigate and identify significant contributing factors that determine the pedestrian-vehicle crash severity in North Carolina, United States. Pedestrian-vehicle crash data from Highway Safety Information System (HSIS) database from 2005 to 2012 are collected and used in this study. Crash injury severities are classified into five categories: no injury (property damage only), injury class 3 (possible injury), injury class 2 (evident injury), injury class 1 (disabling injury), and fatality. A preferred multinomial logit model is developed using SAS PROC MDC procedure and marginal effects are also calculated. The results show that the factors that significantly increase the probability of fatalities and disabling injuries include: driver’s physical condition (bad condition), vehicle type (motorcycle and heavy truck), pedestrian age (26–65 and over 65), weekend, light condition (dawn, dusk and dark), roadway characteristics (curve), roadway surface (water), roadway class (NC route) and speed limit (35–50 mph and above 50 mph). The developed model and analysis results provide insights on developing effective countermeasures to reduce vehicle-pedestrian crash severities and improve traffic system safety performance.

Modelling the severity of pedestrian injury in pedestrian—vehicle crashes in North Carolina: A partial proportional odds logit model approach

More than 2,000 pedestrians are involved in reported traffic crashes with vehicles in North Carolina every year. Of these, 10% to 20% of these pedestrians were killed or severely injured. Studies are needed to explore the reasons under such situation for improvement. Traditional methods for modeling crash injury severities use multinomial logit (MNL) models, mixed logit (ML) models, or ordered logit/probit models. However, traditional MNL and ML models treat injury severity levels as nonordered, ignoring the inherent hierarchical nature of crash injury severities, whereas the data used in ordered logit models should be strictly subjected to the proportional odds (PO) assumption. A partial proportional odds (PPO) logit model approach is applied here to address these concerns. The predictors in the PPO model can have different effects on different levels of the dependent variable who violates the PO assumption. This study uses police-reported pedestrian crash data collected from 2007 to 2014 in North Carolina. A variety of motorist, pedestrian, environmental, roadway characteristics are examined. Comparisons of different models are also made and results show that PPO outperforms others. Parameter estimates and associated marginal effects are calculated and used to interpret the model and evaluate the significance of each dependent variables, followed by recommendations in the Conclusion.

The Impact of Sidewalks on Vehicle-Pedestrian Crash Severity

Walking is a sustainable mode of transportation that has several benefits related to improved health and reducing traffic congestion. The drawback to walking as a mode of transportation is the increased potential to be involved in a severe crash, which is greater than when two automobiles are involved in a crash. This paper provides a statistical analysis of pedestrian crashes that occurred in two Alabama cities where the crashes are divided into those where a sidewalk was present and those where a sidewalk was not present. The goal of the paper is to determine the difference in crash experiences and variables that contribute to vehicle-pedestrian crashes associated with the presence of the sidewalk. The paper uses binary logistic regression to develop models of pedestrian crashes and evaluates the models to determine factors that contribute the pedestrian crashes. The paper concludes that pedestrian crashes often happen in the evenings, with low lighting and visibility levels, independent of the presence of sidewalks.

Spatial Analysis to Identify Pedestrian Crash Zones: A Case Study of School Zones in Thailand

Background:In Thailand, it has the second highest road traffic fatality rate in the world at 36.2 deaths per 100,000 populations. The pedestrian crash zones are based on the road and physical environment, vehicle and driver behavior and pedestrian behavior, especially the area around the school. Therefore, this paper would like to improve safety by identifying crash area through Geographic Information Systems (GIS).Objective:The objective of this paper is to identify pedestrian crash zones of primary schools and secondary schools in Bangkok, Thailand through the spatial analysis and GIS tool.Method:The research methodology was the data collection from pedestrian-vehicle crashes in 2016 at 12 schools of 1,218 locations in Bangkok. The data analysis used GIS for geocoding the crash locations. The spatial patterns and pedestrian crash zone map were applied by Moran’s I statistic and the Kernel Density Estimation (KDE).Results:The Moran’s index showed that the accident locations within school zone were a clustered pattern considering on Moran’s Index which approached +1 and the z-scores greater than 2.58. The KDE showed that the pedestrian crash zones were different depending on the physical environment; however, the most significant areas were at urban areas, crowded areas, and intersections of arterial roads and local roads about 508 meters from the school center.Conclusion:This research could be concluded that the spatial patterns and pedestrian crash zone map will assist the transportation planners and traffic police for identifying crash locations and specific vulnerable road users, especially pedestrians and bicycle users.

Pedestrian Behavior at Signalized Intersection Crosswalks: Observational Study of Factors Associated with Distracted Walking, Pedestrian Violations, and Walking Speed

In the past two decades, cell phone and smartphone use in the United States has increased substantially. Although mobile phones provide a convenient way for people to communicate, the distraction caused by the use of these devices has led to unintended traffic safety and operational consequences. Although it is well recognized that distracted driving is extremely dangerous for all road users (including pedestrians), the potential impacts of distracted walking have not been as comprehensively studied. Although practitioners should design facilities with the safety, efficiency, and comfort of pedestrians in mind, it is still important to investigate certain pedestrian behaviors at existing facilities to minimize the risk of pedestrian–vehicle crashes, and to reduce behaviors that may unnecessarily increase delay at signalized intersections. To gain new insights into factors associated with distracted walking, pedestrian violations, and walking speed, 3,038 pedestrians were observed across four signalized intersections in New York and Arizona using high-definition video cameras. The video data were reduced and summarized, and an ordinary least squares (OLS) regression model was estimated to analyze factors affecting walking speeds. In addition, binary logit models were estimated to analyze both pedestrian distraction and pedestrian violations. Ultimately, several site- and pedestrian-specific variables were found to be significantly associated with pedestrian distraction, violation behavior, and walking speeds. The results provide important information for researchers, practitioners, and legislators, and may be useful in planning strategies to reduce or mitigate the impacts of pedestrian behavior that may be considered unsafe or potentially inefficient.

A study of fatal pedestrian crashes at rural low-volume road intersections in southwest China

ABSTRACTObjective: Although intersections correspond to a small proportion of the entire roadway system, they account for a disproportionally high number of fatal pedestrian crashes, especially on rural roads situated in low- and middle-income countries. This article examines pedestrian safety at rural intersections and suggests applicable accident prevention treatments by providing an in-depth analysis of 28 fatal pedestrian crashes from 8 low-volume roads in southwest China.Methods: The driving reliability and error analysis method (DREAM) is a method to support a systematic classification of accident causation information and to facilitate aggregation of that information into patterns of contributing factors. This is the first time that DREAM was used to analyze pedestrian–vehicle crashes and provide suggestions for road improvements in China.Results: The key issues adversely affecting pedestrian safety can be organized in 4 distinctive thematic categories, namely, deficient intersection safety infrastructure, lack of pedestrian safety education, inadequate driver training, and insufficient traffic law enforcement. Given that resources for traffic safety investments in rural areas are limited, it is determined that the potential countermeasures should focus on low-cost, easily implementable, and long-lasting measures increasing the visibility and predictability of pedestrian movement and reducing speeding and irresponsible driving among drivers and risk-taking behaviors among pedestrians.Conclusions: Accident prevention treatments are suggested based on their suitability for rural areas in southwest China. These countermeasures include introducing better access management and traffic calming treatments, providing more opportunities for pedestrian education, and enhancing the quality of driver training and traffic law enforcement.

Supervised association rules mining on pedestrian crashes in urban areas: identifying patterns for appropriate countermeasures

ABSTRACTIn 2011, 4,432 pedestrians were killed (14% of total traffic crash fatalities), and 69,000 pedestrians were injured in vehicle-pedestrian crashes in the United States. Particularly in Louisiana, vehicle-pedestrian crashes have become a key concern because of the high percentage of fatalities in recent years. In 2012, pedestrians were accounted for 17% of all fatalities due to traffic crashes in Louisiana. Alcohol was involved in nearly 44% of these fatalities. This research utilized ‘a priori’ algorithm of supervised association mining technique to discover patterns from the vehicle-pedestrian crash database. By using association rules mining, this study aims to discover vehicle-pedestrian crash patterns using eight years of Louisiana crash data (2004–2011). The results indicated that roadway lighting at night helped in alleviating pedestrian crash severity. In addition, a few groups of interest were identified from this study: male pedestrians’ greater propensity towards severe and fatal crashes, younger female drivers (15–24) being more crash-prone than other age groups, vulnerable impaired pedestrians even on roadways with lighting at night, middle-aged male pedestrians (35–54) being inclined towards crash occurrence, and dominance of single vehicle crashes. Based on the recognized patterns, this study recommends several countermeasures to alleviate the safety concerns. The findings of this study will help traffic safety professionals in understanding significant patterns and relevant countermeasures to raise awareness and improvements for the potential decrease of pedestrian crashes.

Macro-spatial approach for evaluating the impact of socio-economics, land use, built environment, and road facility on pedestrian safety

With the increasing demand for sustainability, walking is being encouraged as a main active mode of transportation. However, pedestrians are vulnerable to severe injuries when involved in crashes, which can discourage road users from walking. Therefore, studying the factors that affect the safety of pedestrians is important. This paper investigates the relationship between pedestrian-vehicle crashes and various zone characteristics in the city of Vancouver. The goal is to assess the impact of socio-economics, land use, built environment, and road facility on pedestrian safety using macro-level collision prediction models. The models were developed using generalized linear regression and full Bayesian techniques. Both walking trips and vehicle kilometres travelled were used as the main traffic exposure variables in the models. The safety models showed that pedestrian-motorist crashes were non-linearly positively associated with the increase in traffic exposure. The crashes were also found positively associated with the socio-economic variables (i.e., employment and household densities), some built environment variables (transit stop, traffic signal, and light pole densities), commercial area density, and arterial-collector roads proportion. On the other hand, the models revealed a decline in the pedestrian-motorist crashes associated with the increase in the proportions of pedestrian-actuated signals and local roads, as well as the increase in the recreational and residential areas’ densities. The spatial effects were accounted for in the full Bayes models and were found significant, which imply the importance of considering spatial correlation when developing macro-level pedestrian safety models.

Dynamic model for pedestrian crossing in congested traffic based on probabilistic navigation function

More than 1.2 million people die in road crashes each year, and more than 20 million are severely injured, making it the 9th leading cause of death in the world (2.2% of all deaths globally). Pedestrian deaths comprise more than 35% of road accident deaths, mostly as a result of pedestrian-vehicle crashes. This paper proposes a new model for formulating the dynamics of the interaction between drivers and pedestrians at congested conflict spots where drivers and/or pedestrians do not closely follow the traffic laws and regulations. In this type of spots, characterized by heavy traffic, pedestrians and vehicles interact in close proximity, often requiring sharp and aggressive maneuvers to avoid crashes. The model is based on the Probabilistic Navigation Function (PNF), originally developed for robotics motion planning, that constructs a trajectory according to the probabilistic collision risks. According to this model, pedestrians construct a virtual risk map that assigns the entire crossing area with probabilities for a collision with vehicles, and then select their actions based on their perceived probability for collision. Many accidents can be interpreted in terms of the proposed model, either as a result of incorrect perception of risks, or, despite proper estimation of risks, by a wrong choice of collision maneuvers. The development of the model follows a theoretical and experimental investigation of pedestrian/vehicle interactions at crosswalks. The model is implemented in an agent-based simulation system for pedestrian/driver interaction, and is validated using video clips taken at several congested road spots. It can be used for analyzing the effect of changes in location architecture and traffic regulations for each spot. The model can also serve as a standard tool in simulations for assessing accident risks in urban environments. Finally, it can be utilized in control systems of autonomous vehicles and in drivers’ on-board alert systems.

The effects of neighborhood characteristics and the built environment on pedestrian injury severity: A random parameters generalized ordered probability model with heterogeneity in means and variances

Transportation infrastructure facilities and pedestrian/driver behaviors are associated with neighborhood characteristics and the built environment. However, the effects of neighborhood characteristics and the built environment on pedestrian injury severity are not well documented. To investigate and quantify the effects of neighborhood characteristics and built environment on pedestrian injury severity, a random parameters generalized ordered probit model with heterogeneity in means and variances was proposed to consider the ordinal nature of injury data and the issue of threshold and unobserved heterogeneity. A total of 3867 pedestrian-vehicle crashes that occurred in Florida Department of Transportation District 7 from 2011 to 2014 were analyzed. Based on the estimation results, three factors (African American community, school zone, and bus stop area) related to neighborhood characteristics and the built environment were identified to have significant influence on pedestrian injury severity. Pedestrian-vehicle crashes that occurred in African American community or bus stop area are less likely to involve incapacitating and fatal injuries. The presence of a school within a 0.5-km buffer from a crash leads to a decreased probability of fatal injury and an increased probability of incapacitating injury. In addition, the estimated parameter for elderly pedestrian indicator (50<age≤65) was found to be random with significant heterogeneity in both mean and variance (where the mean and variance are associated with intersection-related crash indicator). Compared to younger pedestrians (age≤30), 74.3% of elderly pedestrians who are involved in intersection-related crashes are more likely to suffer severe injury (fatal or incapacitating injury), while 52.4% of elderly pedestrians who are not involved in intersection-related crashes are more likely to suffer severe injury.

Neighborhood Influences on Vehicle-Pedestrian Crash Severity.

Socioeconomic factors are known to be contributing factors for vehicle-pedestrian crashes. Although several studies have examined the socioeconomic factors related to the location of the crashes, limited studies have considered the socioeconomic factors of the neighborhood where the road users live in vehicle-pedestrian crash modelling. This research aims to identify the socioeconomic factors related to both the neighborhoods where the road users live and where crashes occur that have an influence on vehicle-pedestrian crash severity. Data on vehicle-pedestrian crashes that occurred at mid-blocks in Melbourne, Australia, was analyzed. Neighborhood factors associated with road users' residents and location of crash were investigated using boosted regression tree (BRT). Furthermore, partial dependence plots were applied to illustrate the interactions between these factors. We found that socioeconomic factors accounted for 60% of the 20 top contributing factors to vehicle-pedestrian crashes. This research reveals that socioeconomic factors of the neighborhoods where the road users live and where the crashes occur are important in determining the severity of the crashes, with the former having a greater influence. Hence, road safety countermeasures, especially those focussing on the road users, should be targeted at these high-risk neighborhoods.

Quantitative analysis of pedestrian safety at uncontrolled multi-lane mid-block crosswalks in China

A lot of pedestrian-vehicle crashes at mid-block crosswalks severely threaten pedestrian’s safety around the world. The situations are even worse in China due to low yielding rate of vehicles at crosswalks. In order to quantitatively analyze pedestrian’s safety at multi-lane mid-block crosswalks, the number of pedestrian-vehicle conflicts was utilized to evaluate pedestrian’s accident risk. Five mid-block crosswalks (Wuhan, China) were videoed to collect data of traffic situation and pedestrian-vehicle conflicts, and the quantity and spatial distribution of pedestrian-vehicle conflicts at multi-lane mid-block crosswalk were analyzed according to lane-based post-encroachment time(LPET). Statistical results indicate that conflicts are mainly concentrated in lane3 and lane6. Percentage of conflict of each lane numbered from 1 to 6 respectively are 4.1%, 13.1%, 19.8%, 8.4%, 19.0%, 28.1%. Conflict rate under different crossing strategies are also counted. Moreover, an order probit (OP) model of pedestrian-vehicle conflict analysis (PVCA) was built to find out the contributions corresponding to those factors (such as traffic volume, vehicle speed, pedestrian crossing behavior, pedestrian refuge, etc.) to pedestrian-vehicle conflicts. The results show that: pedestrian refuge have positive effects on pedestrian safety; on the other hand, high vehicle speed, high traffic volume, rolling gap crossing pattern, and larger pedestrian platoon have negative effects on pedestrian safety. Based on our field observation and PVCA model, the number of conflicts will rise by 2% while the traffic volume increases 200 pcu/h; similarly, if the vehicle speed increases 5km/h, the number of conflicts will rise by 12% accordingly. The research results could be used to evaluate pedestrian safety at multi-lane mid-block crosswalks, and useful to improve pedestrian safety by means of pedestrian safety education, pedestrian refuge setting, vehicle speed limiting, and so on.

Computation modeling of laminated crack glass windshields subjected to headform impact

Polyvinyl butyral-laminated glass has been extensively applied to automotive windshields to reduce the severity of head injuries resulting from pedestrian–vehicle crashes. Thus, designing an optimized pedestrian protection design for laminated windshields (LWs) has become a priority. In this study, finite element models that describe LWs, headform, and sub-frontend vehicles are established to numerically investigate the dynamic mechanical behavior of LWs subjected to headform impact. First, headform impact tests are conducted on five different locations on an LW. Second, a single-layered model for LW is proposed using a formatted material constitutive description. A reasonable consistency is observed between the numerical simulation and test results for the acceleration–time behavior of LWs. Third, a triple-layered LW model is devised to provide a highly realistic cracking morphology with an enhanced impact response for LWs. Fourth, the characterized material constitutive properties are applied to LWs with different thicknesses on another vehicle. The simulation and test results show a satisfactory correlation, thereby providing a solid foundation for formulating pedestrian protection design guidelines for LWs to reduce the severity of head injuries resulting from pedestrian–vehicle crashes.

Influence of pedestrian age and gender on spatial and temporal distribution of pedestrian crashes

ABSTRACTObjectives: Every year, about 1.24 million people are killed in traffic crashes worldwide and more than 22% of these deaths are pedestrians. Therefore, pedestrian safety has become a significant traffic safety issue worldwide. In order to develop effective and targeted safety programs, the location- and time-specific influences on vehicle–pedestrian crashes must be assessed. The main purpose of this research is to explore the influence of pedestrian age and gender on the temporal and spatial distribution of vehicle–pedestrian crashes to identify the hotspots and hot times.Methods: Data for all vehicle–pedestrian crashes on public roadways in the Melbourne metropolitan area from 2004 to 2013 are used in this research. Spatial autocorrelation is applied in examining the vehicle–pedestrian crashes in geographic information systems (GIS) to identify any dependency between time and location of these crashes. Spider plots and kernel density estimation (KDE) are then used to determine the temporal and spatial patterns of vehicle–pedestrian crashes for different age groups and genders.Results: Temporal analysis shows that pedestrian age has a significant influence on the temporal distribution of vehicle–pedestrian crashes. Furthermore, men and women have different crash patterns. In addition, results of the spatial analysis shows that areas with high risk of vehicle–pedestrian crashes can vary during different times of the day for different age groups and genders. For example, for those between ages 18 and 65, most vehicle–pedestrian crashes occur in the central business district (CBD) during the day, but between 7:00 p.m. and 6:00 a.m., crashes among this age group occur mostly around hotels, clubs, and bars.Conclusions: This research reveals that temporal and spatial distributions of vehicle–pedestrian crashes vary for different pedestrian age groups and genders. Therefore, specific safety measures should be in place during high crash times at different locations for different age groups and genders to increase the effectiveness of the countermeasures in preventing and reducing vehicle–pedestrian crashes.

Application of partial proportional odds model for analyzing pedestrian crash injury severities in Switzerland

ABSTRACTAccording to the Swiss Microcensus, Switzerland is a place where people choose to walk more than 40% of their daily trip time, resulting in a higher pedestrian crash to total crash ratio when compared to many other developed countries. Furthermore, the high number of old and young pedestrians as well as the large number of pedestrian crashes with higher levels of severity makes the pedestrian crash analysis for Switzerland very important. This study aims to identify the important factors that influence the injury severity levels of pedestrian–vehicle crashes in Switzerland. To address this we employ a partial proportional odds model to analyze pedestrian crash injury severities in Switzerland, based on the 5-year (2008–2012) pedestrian safety data. The study also focuses on a separate analysis for two vulnerable groups of pedestrians (young and old pedestrians) to examine the change in the influence of factors over age. The analysis involves an extensive evaluation based on pedestrian and driver characteristics; main reason for the crash; traffic, temporal, vehicle, roadway geometric and environmental characteristics. The study shows that irrespective of age, dark unlighted areas, sight obstructions, midblock crossings and heavy vehicles adversely affect the injury severity levels of pedestrians.

Exploring the mechanisms of vehicle front-end shape on pedestrian head injuries caused by ground impact

In pedestrian–vehicle accidents, pedestrians typically suffer from secondary impact with the ground after the primary contact with vehicles. However, information about the fundamental mechanism of pedestrian head injury from ground impact remains minimal, thereby hindering further improvement in pedestrian safety. This study addresses this issue by using multi-body modeling and computation to investigate the influence of vehicle front-end shape on pedestrian safety. Accordingly, a simulation matrix is constructed to vary bonnet leading-edge height, bonnet length, bonnet angle, and windshield angle. Subsequently, a set of 315 pedestrian–vehicle crash simulations are conducted using the multi-body simulation software MADYMO. Three vehicle velocities, i.e., 20, 30, and 40km/h, are set as the scenarios. Results show that the top governing factor is bonnet leading-edge height. The posture and head injury at the instant of head ground impact vary dramatically with increasing height because of the significant rise of the body bending point and the movement of the collision point. The bonnet angle is the second dominant factor that affects head–ground injury, followed by bonnet length and windshield angle. The results may elucidate one of the critical barriers to understanding head injury caused by ground impact and provide a solid theoretical guideline for considering pedestrian safety in vehicle design.

A new spatial and flexible multivariate random-coefficients model for the analysis of pedestrian injury counts by severity level

We propose in this paper a spatial random coefficients flexible multivariate count model to examine, at the spatial level of a census tract, the number of pedestrian injuries by injury severity level. Our model, unlike many other macro-level pedestrian injury studies in the literature, explicitly acknowledges that risk factors for different types of pedestrian injuries can be very different, as well as accounts for unobserved heterogeneity in the risk factor effects. We also recognize the multivariate nature of the injury counts by injury severity level within each census tract (as opposed to independently modeling the count of pedestrian injuries by severity level). In concrete methodological terms, our model: (a) allows a full covariance matrix for the random coefficients (constant heterogeneity, or CH, and slope heterogeneity, or SH, effects) characterizing spatial heterogeneity for each count category, (b) addresses excess zeros (or any other excess count value for that matter) within a multivariate count setting in a simple and elegant fashion, while recognizing multivariateness engendered through covariances in both the CH and SH effects, (c) accommodates spatial dependency through a spatial autoregressive lag structure, allowing for varying spatial autoregressive parameters across count categories, and (d) captures spatial drift effects through the spatial structure on the constants and the slope heterogeneity effects. To our knowledge, this is the first time that such a general spatial multivariate model has been formulated. For estimation, we use a composite marginal likelihood (CML) inference approach that is simple to implement and is based on evaluating lower-dimensional marginal probability expressions.The data for our analysis is drawn from a 2009 pedestrian crash database from the Manhattan region of New York City. Several groups of census tract-based risk factors are considered in the empirical analysis based on earlier research, including (1) socio-demographic characteristics, (2) land-use and road network characteristics, (3) activity intensity characteristics, and (4) commute mode shares and transit supply characteristics. The empirical analysis sheds light on both engineering as well as behavioral countermeasures to reduce the number of pedestrian-vehicle crashes by severity of these crashes.

Early Detection of Sudden Pedestrian Crossing for Safe Driving During Summer Nights

Sudden pedestrian crossing (SPC) is the major reason for pedestrian-vehicle crashes. In this paper, we focus on detecting SPCs at night for supporting an advanced driver assistance system using a far-infrared (FIR) camera mounted on the front roof of a vehicle. Although the thermal temperature of the road is similar to or higher than that of the pedestrians during summer nights, many previous researches have focused on pedestrian detection during the winter, spring, or autumn seasons. However, our research concentrates on SPC during the hot summer season because the number of collisions between pedestrians and vehicles in Korea is higher at that time than during the other seasons. For real-time processing, we first decide the optimal levels of image scaling and search area. We then use our proposed method for detecting virtual reference lines that are associated with road segmentation without using color information and change these lines according to the turning direction of the vehicle. Pedestrian detection is conducted using a cascade random forest with low-dimensional Haar-like features and oriented center-symmetric local binary patterns. The SPC is predicted based on the likelihood and the spatiotemporal features of the pedestrians, such as their overlapping ratio with virtual reference lines, as well as the direction and magnitude of each pedestrian’s movement. The proposed algorithm was successfully applied to various pedestrian data sets captured by an FIR camera, and the results show that its SPC detection performance is better than those of other methods.

Beyond affordable shelters: Subsidized housing and surrounding environments for pedestrian safety

Beyond the provision of affordable housing itself, planners and policymakers have raised concerns as to whether subsidized housing developments provide “suitable living environments” for the nation's poor. Despite numerous concerns regarding unfavorable living environments and the neighborhood context of subsidized housing, we have limited understanding as to whether built environments around subsidized housing ensure pedestrian safety. This study addressed this gap by examining how built environments around Low Income Housing Tax Credit (LIHTC) sites affect pedestrian-vehicle crashes in Austin, Texas. We employed the two-level negative binomial regression to clarify the impacts of street segment-level and neighborhood-level built environments on pedestrian crashes around LIHTC complexes. We found that higher speed roads, traffic-generating land uses, higher transit stop densities, and higher four-or-more-leg intersection densities may hinder pedestrian safety. Conversely, local roads as well as single-family residential parcels and connected sidewalks along street segments may enhance pedestrian safety around LIHTC complexes. Our results may inform planners and policymakers on how to enhance pedestrian safety for subsidized housing by modifying surrounding environments and how to provide better site selection considerations for subsidized housing to ensure pedestrian safety.

Vehicle-Pedestrian crashes at Intersections in Dhaka city

Background: Pedestrians are some of the most vulnerable road users, especially in large congested cities in developing countries. In order to develop appropriate countermeasures to improve safety, research has to be conducted to understand the factors contributing to vehicle-pedestrian collisions. Objective: This study aims to identify the factors contributing to intersection crashes in a developing country context. Method: A Poisson regression model was applied to police reported crash data from the capital of Bangladesh, Dhaka. Results: This study finds that an increase in vehicle traffic and the presence of police officer, footbridge, bus stop, solar panel and waste deposit facility were associated with an increase in the number of vehicle-pedestrian crashes, whereas an increase in pedestrian volume, roads with the same number of inbound and outbound lanes, roads with greater number of lanes, and the presence of traffic signal, commercial area or offices, speed breaker and rail crossing were associated with a reduction in the number of vehicle-pedestrian crashes. Conclusion: While the results of most traffic and engineering factors are consistent with those obtained in previous studies in developed countries, some of the results on human related factors and unusual road furniture are atypical and require more locally targeted countermeasures.