Which factors contribute to road crashes in non-commercial and commercial vehicles? An examination of administrative data from motorways in Pakistan

Abstract

This study aimed to exhibit the crash distribution and compare the contributory factors (crash characteristics, driver characteristics, vehicle characteristics and road characteristics) responsible for road crashes between non-commercial and commercial vehicles. To achieve the objective, a step-wise binary logistic regression (LR) model was employed with the forward LR method to explore the contributing factors to road crashes between the non-commercial and commercial vehicles. The road crash data (2013–2017) on motorways (M1 and M2) was collected from the National Highway and Motorway Police (NHMP) in Pakistan. During the study period, a total of 1110 road crashes were recorded. The proportion of fatal and non-fatal crashes were 29% and 71% for non-commercial vehicles and 31% and 69% for commercial vehicles, respectively. The results from LR model revealed that drowsy driving, poor road conditions, overspeeding and tire bursting were found to be significant predictors of road crashes. Road crashes caused by drowsy driving and poor vehicle condition were prevalent in commercial vehicles. On the contrary, overspeeding and tire bursting were more prevalent in non-commercial vehicles. The remaining factors could not achieve significant values in the model. On the basis of these empirical findings, suggestions to improve safety were pointed out.

Keywords:

• Road safety
• road crashes
• contributory factors
• commercial vehicles
• non-commercial vehicles
• motorway
• Pakistan

Acknowledgements

We acknowledge the financial support from the National Natural Science Foundation of China. Furthermore, we are obliged to NHMP, Pakistan who provided the data set which enabled this research to be conducted.

Disclosure statement

The authors declare no conflict of interest.

Author’s contributions

Muhammad Hussain: Investigation, Data curation, Software, Formal analysis, Writing – original draft, Jing Shi: Conceptualization, Funding acquisition, Supervision, Investigation, Data curation, Writing – review and editing, Muladilijiang Baikejuli: Investigation, Software, Formal analysis.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (No. 51578319 and No.51778340).