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Impact of drivers'attributes on children injury severities in traffic crashes

Journal of Traffic and Transportation Engineering(English Edition) 2023年第4期10卷 647-658页

作者： Sahima Nazneen Ahmed Farid Khaled Ksaibati HNTB Overland ParkKS 66223USA Caltrans District 3 MarysilCA 95901USA Wyoming Technology Transfer Center Department of Civil and Architectural Engineering and Construction ManagementUniversity of WyomingLaramieWY 82071USA

Every year,a substantial number of children sustain injuries and fatalities in motor vehicle crashes in *** the factors contributing to child injury is crucial for the development of appropriate mitigation measures that aid in alleviating the severity of such *** this study,a hierarchical Bayesian binary logit regression model was developed to investigate the factors that contribute to children’s injuries resulting from crashes while accounting for possible intra-class correlation effects(those of unobserved factors common to children involved in the same crash).A strong correlation among crashes justified the use of the hierarchical Bayesian logit *** per the modeling results,the children’s ages,safety restraint types,vehicle types,drivers’ages,alcohol/drug involvement,drivers’seat belt use habits,drivers’actions,manners of collision and environmental conditions contributed to child injury *** child’s age was found to be inversely related to the risk of ***,among safety restraint types,rear-facing car seats and forward-facing car seats were found to reduce injury likelihoods in *** it comes to the drivers’characteristics,the probability of incurring injuries among the child population increased in the presence of young,unbuckled and impaired ***,improper driving actions,such as running off the road,raised the risk of incurring injuries to *** findings of this study may be beneficial to authorities regarding developing and implementing road safety programs aimed at ameliorating child injury concerns.

关键词： Child injury Bayesian inference Hierarchical logitmodel intra-class correlations Unobserved heterogeneity effects

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