A systematic review of statistical models and outcomes of predicting fatal and serious injury crashes from driver crash and offense history data.

Slikboer, Reneta; Muir, Samuel D; Silva, S S M; Meyer, Denny

Slikboer, Reneta; Muir, Samuel D; Silva, S S M; Meyer, Denny.

Afiliación

Slikboer R; Centre for Mental Health, Faculty of Health Arts and Design, Swinburne University of Technology, PO Box 218, Mail H31, John St, Hawthorn, Victoria, 3122, Australia. rslikboer@swin.edu.au.
Muir SD; Centre for Mental Health, Faculty of Health Arts and Design, Swinburne University of Technology, PO Box 218, Mail H31, John St, Hawthorn, Victoria, 3122, Australia.
Silva SSM; Centre for Mental Health, Faculty of Health Arts and Design, Swinburne University of Technology, PO Box 218, Mail H31, John St, Hawthorn, Victoria, 3122, Australia.
Meyer D; Centre for Mental Health, Faculty of Health Arts and Design, Swinburne University of Technology, PO Box 218, Mail H31, John St, Hawthorn, Victoria, 3122, Australia.

Syst Rev ; 9(1): 220, 2020 09 28.

Article en En | MEDLINE | ID: mdl-32988410

Asunto(s)

Accidentes de Tránsito; Envío de Mensajes de Texto; Humanos; Modelos Estadísticos; Políticas

Palabras clave

Crash; Crash history; Driver offenses; Offense; Quality assessment tool; Statistical modeling; Statistics; Systematic review; Traffic

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Accidentes de Tránsito / Envío de Mensajes de Texto Tipo de estudio: Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: Syst Rev Año: 2020 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Reino Unido

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