A Statistical and machine learning framework for analyzing road traffic crash severity in Sri Lanka

Abstract

This study analyses 397,850 police-reported crashes in Sri Lanka from 2010 to 2020 using a multinomial logit model, validated through Random Forest and XGBoost, to identify key factors influencing crash severity across four outcome levels. Results reveal four findings not apparent from conventional crash statistics. First, motorcycle–vulnerable road user crashes (OR = 202, 10.24% of crashes) produced higher fatal odds than heavy vehicle–motorcycle interactions (OR = 143, 6.86%), despite the latter receiving greater policy attention. This suggests that motorcycle-related risk to vulnerable road users is considerably underappreciated in current safety programs. Second, single-vehicle motorcycle crashes (OR = 46.2, 9.05%) were three times more fatal per incident than motorcycle–passenger vehicle collisions (OR = 14.6), implicating loss of control and emergency response delays as under-recognized contributors. Third, passengers falling from buses yielded a fatal odds ratio of 2,337, the highest in the model, despite representing only 1.50% of crashes, indicating a concentrated fatality risk understated in national reporting. Fourth, dusk and dawn conditions (OR = 2.24, 17.27% of crashes) were found to be equally as dangerous as completely unlit nighttime roads (OR = 2.26), while fog and mist produced the highest lighting-related fatal odds ratio of 4.71. The findings indicate that targeted interventions addressing motorcycle–vulnerable road user interactions, bus passenger safety, and transitional lighting conditions offer the greatest potential for reducing fatal crash outcomes in Sri Lanka.