Discovering injury severity risk factors in automobile crashes: A hybrid explainable AI framework for decision support
Yükleniyor...
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
ELSEVIER SCI
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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 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.
Açıklama
Anahtar Kelimeler
Explainable AI, Variable Neighborhood Search, Machine Learning, Information Fusion, Injury Severity Risk Factors
Kaynak
RELIABILITY ENGINEERING & SYSTEM SAFETY
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
226
Sayı
Künye
Amini, 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.
