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A comparative study of machine learning classifiers for injury severity prediction of crashes involving three-wheeled motorized rickshaw.
Ijaz, Muhammad; Lan, Liu; Zahid, Muhammad; Jamal, Arshad.
Affiliation
  • Ijaz M; School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China. Electronic address: m.ijaz58@yahoo.com.
  • Lan L; School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China. Electronic address: jianan_l@home.swjtu.edu.cn.
  • Zahid M; College of Metropolitan Transportation, Beijing University of Technology, Beijing, 100124, China. Electronic address: zahid@emails.bjut.edu.cn.
  • Jamal A; Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, KFUPM, Box 5055, Dhahran, 31261, Saudi Arabia. Electronic address: arshad.jamal@kfupm.edu.sa.
Accid Anal Prev ; 154: 106094, 2021 May.
Article in En | MEDLINE | ID: mdl-33756425
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Wounds and Injuries / Accidents, Traffic Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Country/Region as subject: Asia Language: En Journal: Accid Anal Prev Year: 2021 Type: Article
Fulltext
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PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Wounds and Injuries / Accidents, Traffic Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Country/Region as subject: Asia Language: En Journal: Accid Anal Prev Year: 2021 Type: Article