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This paper deals with the modelling and examination of the influence of predictor variables (contributory factors) on road crashes (RCs) among functionally classified vehicles (i) motorcycles, (ii) non-commercial (private) vehicles, and (iii) commercial (work-related vehicles) in Pakistan. A retrospective study was performed on the RC data (2013-2017), collected from the National Highway and Motorway Police (NHMP) in Pakistan. A multinomial logit model was developed to examine the risk of RCs in three distinct functionally classified vehicles based on contributory factors. The contributory factors in this study belong to four major categories: crash characteristics (crash severity, weekday indicator, weekend indicator, tourist season), driver characteristics (careless driving, fatigue driving, speeding), vehicle characteristics (mechanical fault, tire bursts), and road characteristics (poor road conditions). The findings of this study provide evidence of RCs caused by careless driving, fatigue driving, speeding, tire burst, poor road conditions, mechanical failure, and crash severity associated with different functionally classified vehicles. Among all the predictor variables, RCs caused by careless driving, speeding, and poor road conditions were significantly associated with motorcycles than commercial drivers. Whereas, RCs caused by fatigue driving and mechanical fault were more prevalent in commercial vehicles as compared to non-commercial vehicles and motorcycles. Tire bursts and crash severity were found to be significant predictors of RCs in non-commercial vehicles. The results could apply to designing and implementing a crash prevention system that could minimise the risk of accidents between vehicles of different types in Pakistan and countries with similar road safety challenges.

Modelling and examining the influence of predictor variables on the road crashes in functionally classified vehicles in Pakistan