Predicting and explaining severity of road accident using artificial intelligence techniques, SHAP and feature analysis

Abstract

The accurate prediction of accident severity has become an active area of research in recent years, although studies in certain regions such as South Asia and Sub-Saharan Africa are comparatively less. In this study, we aim to contribute in many ways: (i) we conduct an analytical review of the literature to gauge the interest and scope of existing studies and identify the direction for further research, and (ii) a mixture of old and relatively new artificial intelligence (AI) techniques is applied to road accident data of India (iii) we employ shapley additive explanations (SHAP) for interpretation of AI model predictions, and (iv) an AI-enabled accident management system is proposed. The findings suggest that AI models are capable of predicting the accident severity. Precisely, the gradient boosting machine attains the best test accuracy. Among features, commercial vehicles, excess speed, national highways, and pedestrians’ fault are responsible for accidental road killings.

Keywords:

• Accident severity prediction
• artificial intelligence
• machine learning
• SHAP
• feature analysis
• AI-enabled accident management system
• road safety

JEL Code:

• R41
• B23
• C53

Acknowledgement

Authors are grateful to the anonymous referee for useful comments. The views expressed in this article are personal. Usual disclaimers apply.

Disclosure statement

No potential conflict of interest was reported by the authors.

Availability of data and material

The data that support the findings of this study are openly available in the public domain: https://morth.nic.in/transport-research-wing.

Code availability

Available on special request to Authors.

Correction Statement

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

Additional information

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.