Injury severity analysis of pedestrian and bicyclist trespassing crashes at non-crossings: A hybrid predictive text analytics and heterogeneity-based statistical modeling approach.

Wali, Behram; Khattak, Asad J; Ahmad, Numan

Wali, Behram; Khattak, Asad J; Ahmad, Numan.

Afiliação

Wali B; Urban Design 4 Health, Inc., United States; Department of Civil & Environmental Engineering, The University of Tennessee, United States. Electronic address: bwali@ud4h.com.
Khattak AJ; Department of Civil & Environmental Engineering, The University of Tennessee, United States. Electronic address: akhattak@utk.edu.
Ahmad N; Department of Civil & Environmental Engineering, The University of Tennessee, United States. Electronic address: nahmad1@vols.utk.edu.

Accid Anal Prev ; 150: 105835, 2021 Feb.

Article em En | MEDLINE | ID: mdl-33310430

Assuntos

Pedestres; Ferrovias; Ferimentos e Lesões; Acidentes de Trânsito; Mineração de Dados; Humanos; Modelos Logísticos; Modelos Estatísticos; Ferimentos e Lesões/epidemiologia

Palavras-chave

Concept/Entity extraction; Dynamic factor analysis; Heterogeneity-based discrete outcome modeling; Injury severity; Machine learning; Non-crossings; Non-motorist trespassing crashes; Text analysis

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Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 2_ODS3 Base de dados: MEDLINE Assunto principal: Ferrovias / Ferimentos e Lesões / Pedestres Tipo de estudo: Prognostic_studies / Qualitative_research / Risk_factors_studies Limite: Humans Idioma: En Revista: Accid Anal Prev Ano de publicação: 2021 Tipo de documento: Article

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