Investigating the effect of road condition and vacation on crash severity using machine learning algorithms

Abstract

Investigating the contributing factors to traffic crash severity is a demanding topic in research focusing on traffic safety and policies. This research investigates the impact of 16 roadway condition features and vacations (along with the spatial and temporal factors and road geometry) on crash severity for major intra-city roads in Saudi Arabia. We used a crash dataset that covers four years (Oct. 2016 – Feb. 2021) with more than 59,000 crashes. Machine learning algorithms were utilized to predict the crash severity outcome (non-fatal/fatal) for three types of roads: single, multilane, and freeway. Furthermore, features that have a strong impact on crash severity were examined. Results show that only 4 out of 16 road condition variables were found to be contributing to crash severity, namely: paints, cat eyes, fence side, and metal cable. Additionally, vacation was found to be a contributing factor to crash severity, meaning crashes that occur on vacation are more severe than non-vacation days.

Keywords:

• Road crash severity
• machine learning classifiers
• roadway conditions
• feature importance
• SHAP

Acknowledgments

We would like to thank the National Road Safety Center (NRSC) at the Ministry of Transport and Logistic Services (MoTLS) for its technical and logistical support while conducting this study. Researchers Supporting Project number (RSP2023R291), King Saud University, Riyadh, Saudi Arabia.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.