Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances

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dc.contributor.authorZhu, Tong
dc.contributor.authorZhu, Zishuo
dc.contributor.authorZhang, Jie
dc.contributor.authorYang, Chenxuan
dc.contributor.otherChang'an University
dc.contributor.otherMinistry of Transport of the People's Republic of China
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2023-09-28T19:35:47Z
dc.date.available2023-09-28T19:35:47Z
dc.date.issued2021
dc.description.abstractAccidents involving electric bicycles, a popular means of transportation in China during peak traffic periods, have increased. However, studies have seldom attempted to detect the unique crash consequences during this period. This study aims to explore the factors influencing injury severity in electric bicyclists during peak traffic periods and provide recommendations to help devise specific management strategies. The random-parameters logit or mixed logit model is used to identify the relationship between different factors and injury severity. The injury severity is divided into four categories. The analysis uses automobile and electric bicycle crash data of Xi'an, China, between 2014 and 2019. During the peak traffic periods, the impact of low visibility significantly varies with factors such as areas with traffic control or without streetlights. Furthermore, compared with traveling in a straight line, three different turnings before the crash reduce the likelihood of severe injuries. Roadside protection trees are the most crucial measure guaranteeing riders' safety during peak traffic periods. This study reveals the direction, magnitude, and randomness of factors that contribute to electric bicycle crashes. The results can help safety authorities devise targeted transportation safety management and planning strategies for peak traffic periods.en_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.citationZhu, T., Zhu, Z., Zhang, J., & Yang, C. (2021). Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances. In International Journal of Environmental Research and Public Health (Vol. 18, Issue 21, p. 11131). MDPI AG. https://doi.org/10.3390/ijerph182111131
dc.identifier.doi10.3390/ijerph182111131
dc.identifier.urihttps://ir.ua.edu/handle/123456789/11526
dc.languageEnglish
dc.language.isoen_US
dc.publisherMDPI
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectmixed logit model
dc.subjectheterogeneity in means and variances
dc.subjectinjury severity
dc.subjectelectric bicycle crashes
dc.subjectvisibility
dc.subjectVEHICLE CRASHES
dc.subjectLARGE-TRUCKS
dc.subjectLOGIT MODEL
dc.subjectACCIDENT
dc.subjectVISIBILITY
dc.subjectDRIVERS
dc.subjectIMPACT
dc.subjectSEAT
dc.subjectAGE
dc.subjectEnvironmental Sciences
dc.subjectPublic, Environmental & Occupational Health
dc.titleElectric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variancesen_US
dc.typeArticle
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