On the interpretability of machine learning methods in crash frequency modeling and crash modification factor development.

Wen, Xiao; Xie, Yuanchang; Jiang, Liming; Li, Yan; Ge, Tingjian

Wen, Xiao; Xie, Yuanchang; Jiang, Liming; Li, Yan; Ge, Tingjian.

Afiliación

Wen X; Department of Civil and Environmental Engineering, University of Massachusetts Lowell, United States.
Xie Y; Department of Civil and Environmental Engineering, University of Massachusetts Lowell, United States. Electronic address: yuanchang_xie@uml.edu.
Jiang L; Department of Civil and Environmental Engineering, University of Massachusetts Lowell, United States.
Li Y; Department of Computer Science, University of Massachusetts Lowell, United States.
Ge T; Department of Computer Science, University of Massachusetts Lowell, United States.

Accid Anal Prev ; 168: 106617, 2022 Apr.

Article en En | MEDLINE | ID: mdl-35202941

Asunto(s)

Accidentes de Tránsito; Aprendizaje Automático; Accidentes de Tránsito/prevención & control; Humanos; Factores de Riesgo

Palabras clave

Crash frequency; Crash modification factor; Machine learning; Model interpretation; SHAP; Sensitivity analysis

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Accidentes de Tránsito / Aprendizaje Automático Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Accid Anal Prev Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

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