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Collisions involving public transit (PT) have increased significantly in the recent times. In developing countries, the scenario is much worse as the PT collision occurrence and injury severity are higher by manifold. This study investigates the injury severity of victims of PT involved crashes in Dhaka, Bangladesh. Victims include PT occupants and counterpart road users such as pedestrians who are more vulnerable. A latent segmentation-based ordered logit (LSOL) model is developed using police-reported crash records of Dhaka. The LSOL model addresses the ordinality of injury severity and captures unobserved heterogeneity by allocating victims into discrete latent segments. The LSOL model is estimated for two segments: segment two is high-risk segment which includes unconventional vehicle occupants and pedestrians, whereas segment one is low-risk segment. The injury severity model results suggest that person profile, environmental factors and built environment characteristics significantly influence injury severity. For example, female and elderly victims have higher likelihood to sustain severe injury. The model confirms the existence of significant heterogeneity. For instance, mid-block collisions are more likely to yield severe injury in the high-risk segment, and less likely to result in severe injury in the low-risk segment. Similar heterogeneity is confirmed for variables representing impaired driver, three-way intersection, and police-controlled intersection. The elasticity effect analysis suggests that female victims, younger and adult victims, unconventional vehicle occupants, pedestrians, and mid-block crashes have substantial positive impact on fatality. The identified influential factors and heterogeneity captured in the study need to be accommodated in road safety plans and policies.

Modelling injury severity of victims in collisions involving public transit in Dhaka, Bangladesh