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Traffic Injury Prevention Volume 22, 2021 - Issue 2
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Validating the representativeness assumption of the quasi-induced exposure method using a national representative field observation survey

Sijun Shena The Center for Injury Research and Policy, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, Ohio;b Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, OhioView further author information
,
Shan Baoc Industrial and Manufacturing Systems Engineering Department, University of Michigan-Dearborn, Dearborn, Michigan;d University of Michigan Transportation Research Institute, Ann Arbor, MichiganView further author information
&
Motao Zhua The Center for Injury Research and Policy, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, Ohio;b Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, Ohio;e Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OhioCorrespondence[email protected]
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Pages 133-138 | Received 18 Mar 2020, Accepted 22 Dec 2020, Published online: 10 Feb 2021

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Guopeng Zhang, Ying Cai, Xinguo Jiang, Yingfei Fan, Yue Zhou & Jun Qian. (2023) An ensemble machine learning method for crash responsibility assignment in quasi-induced exposure theory. Journal of Transportation Safety & Security 15:1, pages 24-42.
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Articles from other publishers (1)

Guopeng Zhang, Ying Cai & Lei Li. (2023) The difference in quasi-induced exposure to crashes involving various hazardous driving actions. PLOS ONE 18:2, pages e0279387.
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