Study of the effects of built environment on pedestrian crash frequency and severity in Dhaka

Abstract

Pedestrian crashes have become a major safety concern in urban areas throughout the world, including Bangladesh. This scenario is no better in Dhaka, the capital and megacity of Bangladesh. From 1998 to 2014, more than 10 thousand crashes occurred here, and 4,514 pedestrians died in those crashes. To improve this critical situation, researchers have been trying to identify the contributory factors behind pedestrian crashes through studies at both macroscopic and microscopic levels. Macroscopic level pedestrian crash occurrences analysis and microscopic level pedestrian crash severity analysis are the most common techniques used for identifying contributory factors behind pedestrian crashes. Recent literature suggests to improvepedestrian safety by altering the built environment of urban areas considering its effect on pedestrian crashes. Therefore, identifying contributory built environment factors behind pedestrian crashes is important to reduce the number of crashes and their severity level. In the case of macroscopic level pedestrian crash occurrences analysis,built environment-related factors have primarily been examined in the developed countries, resulting in a limited understanding of the phenomenon in the context of developing countries. Methodologically, these studies mostly used global regression models, which failed to incorporate spatial autocorrelation and spatial heterogeneity. Althougha fewof these studies usedspatial regression models, they applied them randomly without following a comprehensive logical framework behind their selections. This study aimed to develop a comprehensive spatial regression modeling framework to examine the relationships between pedestrian crash occurrences and the built environment at the macroscopic level in Dhaka.Using secondary data, the study applied one global non-spatial model, two global spatial regression models, and two local spatial regression models following a comprehensive spatial regression modeling framework.The analysis results identified the factors thatwere found to significantly contribute to pedestrian crash occurrences in Dhaka. Those factors were employed person density, mixed and recreational land use density, primary road density, major intersection density, and share of non-motorized modes. Except for the last factor, all the other ones were positively related to pedestrian crash density. Among the five models used in this study, the multiscale geographically weighted regression (MGWR) performed the best as it calibrated each local relationship with distant spatial scale parameter. The findings and recommendations presented in this study would be useful for reducing pedestrian crashesand choosing the appropriate model for crash analysis. In the case of microscopic level pedestrian crash severity analysis, a large number of studies tried to explore the relationships between the built environment and pedestrian crash severity in developed countries.Unfortunately, there is a lack of similar studies in developing countries, especially Bangladesh. Methodologically, the contributory factors influencing pedestrian crash severity are commonly identified through global logistic regression (GLR) models.However, these models are unable to capture the spatial heterogeneity in the relationships between the dependent and independent variables. The local logistic regression model, such as geographicallyweighted logistic regression (GWLR), can potentially overcome this issue. Still, the application of local logistic regression to model pedestrian crash severity is absent in the literature. Therefore, this study applied the GWLR technique to explore spatially heterogeneous relationships between the natural and built environment-related factors with pedestrian crash severity in Dhaka. First, using secondary data, a binary logistic regression model was developed to identify significant factors influencing pedestrian crash severity. Results of the model showed that the probability of fatal pedestrian crash occurrence increased at night, in unlit locations, and during adverse weather conditions. Also, the likelihood of fatal crashes increased on straight and flat roads and at locations with more bus stops. On the other hand, the chance of fatal crashesreduced around institutional land uses and when medians exist on roads. Finally, this study explored spatial variation in the effect intensity of these significant variables across the study area using the GWLR technique. High-intensity variation across the study area was found for road geometry and institutional landuse factors. On the other hand, low-intensity variation was found for light conditions and the presence of median factors. This technique can be applied in any area, and the results would be helpful to provide insights into the spatial dimension of traffic safety.