As road traffic accidents (RTAs) cause enormous economic and human losses, especially in developing countries, numerous research efforts are needed to identify the key risk factors that significantly influence accident and crash severity. Despite that Dhaka city is registering alarming rises in related deaths and severe injuries, Bangladesh has yet to collect significant RTAs data. Thus, this study adopts probit and Heckman selection probit models to investigate RTAs and injury severity levels using original data from an on-field survey collecting 786 participants' responses regarding their socio-economic and demographic characteristics, their knowledge of road traffic systems and rules, the roads and vehicles types, and the road infrastructure conditions. Probit model showed that the major risk factors that increase road accidents causing severe injuries were wrong-way driving, and lack of speed control signs and adequate street lights. RTAs resulting in severe injuries were significantly associated with being married, not having an educational degree, driving on highways and in city areas. Furthermore, the Heckman probit model's selection equation showed that respondents who were unaware of road accident risks, resided in rural areas, and with high household income had higher risks of being directly involved in RTAs.
Keywords: Bangladesh; Heckman selection model; Injury severity; Road accidents; probit model.