Mitigating the impact of outliers in traffic crash analysis: A robust Bayesian regression approach with application to tunnel crash data.

Li, Zhenning; Liao, Haicheng; Tang, Ruru; Li, Guofa; Li, Yunjian; Xu, Chengzhong

Li, Zhenning; Liao, Haicheng; Tang, Ruru; Li, Guofa; Li, Yunjian; Xu, Chengzhong.

Afiliação

Li Z; State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macau SAR 999078, China. Electronic address: li2016@hawaii.edu.
Liao H; State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, University of Macau, Macau SAR 999078, China.
Tang R; State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macau SAR 999078, China.
Li G; College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China.
Li Y; Institute of Applied Physics and Materials Engineering, University of Macau, Macao SAR 999078, China.
Xu C; State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, University of Macau, Macau SAR 999078, China.

Accid Anal Prev ; 185: 107019, 2023 Jun.

Article em En | MEDLINE | ID: mdl-36907031

Assuntos

Acidentes de Trânsito; Ferimentos e Lesões; Humanos; Acidentes de Trânsito/prevenção & controle; Teorema de Bayes; Modelos Logísticos

Palavras-chave

Bayesian inference; Robit model; Robust regression; Traffic safety modeling; Tunnel crash

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Ferimentos e Lesões / Acidentes de Trânsito Tipo de estudo: Guideline / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Accid Anal Prev Ano de publicação: 2023 Tipo de documento: Article

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