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Discovering injury severity risk factors in automobile crashes: A hybrid explainable AI framework for decision support

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  • Amini, Mostafa
  • Bagheri, Ali
  • Delen, Dursun

Abstract

Millions of car crashes occur annually in the US, leaving tens of thousands of deaths and many more severe injuries. Thus, understanding the most impactful contributors to severe injuries in automobile crashes and mitigating their effects are of great importance in traffic safety improvement. This paper develops a hybrid framework involving predictive analytics, explainable AI, and heuristic optimization techniques to investigate and explain the injury severity risk factors in automobile crashes. First, our framework examines various machine learning models to identify the one with the best prediction performance as the base model. Then, it utilizes two popular state-of-the-art explainable AI techniques from the literature (i.e., leave-one-covariate-out and TreeExplainer) and our proposed explanation method based on the variable neighborhood search procedure to construe the importance of the variables. Finally, by applying an information fusion technique, our approach identifies a unified ranking list of the most important variables contributing to severe car crash injuries. Transportation safety planners and policymakers can use our findings to reduce the severity of car accidents, improve traffic safety, and save many lives.

Suggested Citation

  • Amini, Mostafa & Bagheri, Ali & Delen, Dursun, 2022. "Discovering injury severity risk factors in automobile crashes: A hybrid explainable AI framework for decision support," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:reensy:v:226:y:2022:i:c:s0951832022003441
    DOI: 10.1016/j.ress.2022.108720
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    1. Palar, Pramudita Satria & Zuhal, Lavi Rizki & Shimoyama, Koji, 2023. "Enhancing the explainability of regression-based polynomial chaos expansion by Shapley additive explanations," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    2. Ahmed, Abdulaziz & Topuz, Kazim & Moqbel, Murad & Abdulrashid, Ismail, 2024. "What makes accidents severe! explainable analytics framework with parameter optimization," European Journal of Operational Research, Elsevier, vol. 317(2), pages 425-436.
    3. Abdulrashid, Ismail & Zanjirani Farahani, Reza & Mammadov, Shamkhal & Khalafalla, Mohamed & Chiang, Wen-Chyuan, 2024. "Explainable artificial intelligence in transport Logistics: Risk analysis for road accidents," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).

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