CRASH SEVERITY MODELLING INVOLVING PUBLIC BUS CRASHES IN DHAKA CITY

Abstract

This study attempts to identify the influencing factors for triggering public bus crash injury severity in Dhaka city where public buses alone were involved in 23 % of all the crashes. Though there are some descriptive-based works in Bangladesh pertinent to public bus safety, very few in-depth studies on the crash severities of public bus have been conducted; those are however, mostly based on an old crash data. Hence, utilizing the recent crash data (2017-2020) collected from the Accident Research Institute (ARI) of Bangladesh University of Engineering and Technology (BUET), the primary goal of this study is to discover the roadway and environment-related factors impacting the public bus crash severity in the context of Dhaka city. A prominent way to deal with crash injury severity is by using statistical modelling techniques; the selection of these suitable methods often depends on the nature of data, especially the response variables. R software environment has been adopted to facilitate the analysis. In relation to the genre of police-reported public bus crash data, four different established models namely, Multinomial Logit (MNL), Ordered Logit (OL), Ordered Probit (OP) and Partial Proportional Odds (PPO) have been selected for the study. All of these severity models were then applied on this crash data to investigate public bus safety mechanism prevalent in Dhaka city. The analysis showed that pedestrians, bicyclists and motorcyclists are the most vulnerable road user group (around 80%), as indicated by the all selected models. Lack of efficient police controlled traffic in all the places (in some cases, 0% fatal incidents in policecontrolled areas), absence of dividers in two way roads (38.23% fatal vs 57.78% fatal where there are no dividers), over speeding, lack of necessary safety parameters as per the condition/geometry of roads etc. seemed to accelerate road traffic crashes. In addition, the severity models (i.e., MNL, OL, OP, and PPO) were evaluated in terms of relevant comparative parameters where MNL model is found to be more effective in terms of loglikelihood (-237) and PPO model fared better in terms of Akaike Information Criterion ii (AIC_529) and Bayesian Information Criterion (BIC_616). The models were further evaluated on the significance of their predictors where collision type, junction type, movement, road class, road geometry, surface quality, surface type and time are found to be significant for triggering public bus related accidents in Dhaka city. Some viewpoints related to pedestrian facilities and roadway improvement (safety features) have been recommended for the decision makers for reducing both accident frequency and severity.