Using computer vision and machine learning to identify bus safety risk factors.

Loo, Becky P Y; Fan, Zhuangyuan; Lian, Ting; Zhang, Feiyang

Loo, Becky P Y; Fan, Zhuangyuan; Lian, Ting; Zhang, Feiyang.

Afiliación

Loo BPY; Department of Geography, The University of Hong Kong, Hong Kong, China; School of Geography and Environment, Jiangxi Normal University, Nanchang, China. Electronic address: bpyloo@hku.hk.
Fan Z; Department of Geography, The University of Hong Kong, Hong Kong, China. Electronic address: yuanzf@connect.hku.hk.
Lian T; Department of Geography, The University of Hong Kong, Hong Kong, China. Electronic address: tlian@connect.hku.hk.
Zhang F; Department of Geography, The University of Hong Kong, Hong Kong, China.

Accid Anal Prev ; 185: 107017, 2023 Jun.

Article en En | MEDLINE | ID: mdl-36889236

Asunto(s)

Accidentes de Tránsito; Peatones; Humanos; Accidentes de Tránsito/prevención & control; Vehículos a Motor; Transportes; Factores de Riesgo; Aprendizaje Automático; Seguridad

Palabras clave

Bus safety; Crash modeling; Pedestrian behaviour; Video analytics

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Accidentes de Tránsito / Peatones Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Accid Anal Prev Año: 2023 Tipo del documento: Article Pais de publicación: Reino Unido

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