Discovering injury severity risk factors in automobile crashes: A hybrid explainable AI framework for decision support. (October 2022)

• Record Type: Journal Article
• Authors: Amini, Mostafa; Bagheri, Ali; Delen, Dursun
• Is Part Of: Reliability engineering & system safety. Volume 226(2022)
• Publication Date: 2022-10
• Subjects: Explainable AI -- Variable neighborhood search -- Machine learning -- Information fusion -- Injury severity risk factors -- Traffic safety; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108720
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: An explanation model based on a variable neighborhood search approach was proposed. Three model-agnostic explainable AI methods were used to interpret the black-box models. The proposed VNS-based method provided a more predictive short-list of features. Among the tree-based ML models, random forest was the best predictor with 0.903 AUC. The resultant system can help policy makers to understand and mitigate injury severity risk factors. 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.