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

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dc.authoridDursun Delen / 0000-0001-8857-5148en_US
dc.authorscopusidDursun Delen / 55887961100
dc.authorwosidDursun Delen / AGA-9892-2022en_US
dc.contributor.authorAmini, Mostafa
dc.contributor.authorBagheri, Ali
dc.contributor.authorDelen, Dursun
dc.date.accessioned2022-08-08T11:05:23Z
dc.date.available2022-08-08T11:05:23Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractMillions 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 TreeEx-plainer) 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.en_US
dc.identifier.citationAmini, M., Bagheri, A., Delen, D. (2022). Discovering injury severity risk factors in automobile crashes: A hybrid explainable AI framework for decision support. Reliability Engineering & System Safety, 226.en_US
dc.identifier.doi10.1016/j.ress.2022.108720en_US
dc.identifier.issn0951-8320en_US
dc.identifier.scopus2-s2.0-85134753162en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.ress.2022.108720
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3071
dc.identifier.volume226en_US
dc.identifier.wosWOS:000832135600003en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorDelen, Dursun
dc.language.isoenen_US
dc.publisherELSEVIER SCIen_US
dc.relation.ispartofRELIABILITY ENGINEERING & SYSTEM SAFETYen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectExplainable AIen_US
dc.subjectVariable Neighborhood Searchen_US
dc.subjectMachine Learningen_US
dc.subjectInformation Fusionen_US
dc.subjectInjury Severity Risk Factorsen_US
dc.titleDiscovering injury severity risk factors in automobile crashes: A hybrid explainable AI framework for decision supporten_US
dc.typeArticleen_US

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