An integrated approach of machine learning and Bayesian spatial Poisson model for large-scale real-time traffic conflict prediction.

Li, Dongya; Fu, Chuanyun; Sayed, Tarek; Wang, Wei

Li, Dongya; Fu, Chuanyun; Sayed, Tarek; Wang, Wei.

Affiliation

Li D; School of Transportation, Southeast University, China; Department of Civil Engineering, The University of British Columbia, Canada.
Fu C; School of Transportation Science and Engineering, Harbin Institute of Technology, China; Department of Civil Engineering, The University of British Columbia, Canada. Electronic address: fcy1128@mail.ubc.ca.
Sayed T; Department of Civil Engineering, The University of British Columbia, Canada.
Wang W; School of Transportation, Southeast University, China.

Accid Anal Prev ; 192: 107286, 2023 Nov.

Article in En | MEDLINE | ID: mdl-37690284

Subject(s)

Accidents, Traffic; Algorithms; Humans; Bayes Theorem; Accidents, Traffic/prevention & control; Machine Learning; Random Forest

Key words

Bayesian spatial Poisson model; Integrated approach; Large-scale; Machine learning; Real-time conflict prediction

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Full text: 1 Collection: 01-internacional Health context: 2_ODS3 Database: MEDLINE Main subject: Algorithms / Accidents, Traffic Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Accid Anal Prev Year: 2023 Document type: Article

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