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

Accidents 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 elec...

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Autores principales:	Tong Zhu, Zishuo Zhu, Jie Zhang, Chenxuan Yang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	mixed logit model heterogeneity in means and variances injury severity electric bicycle crashes visibility Medicine R
Acceso en línea:	https://doaj.org/article/89455cce4840484ebcb7b1f416affaf2
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spelling	oai:doaj.org-article:89455cce4840484ebcb7b1f416affaf22021-11-11T16:17:27ZElectric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances10.3390/ijerph1821111311660-46011661-7827https://doaj.org/article/89455cce4840484ebcb7b1f416affaf22021-10-01T00:00:00Zhttps://www.mdpi.com/1660-4601/18/21/11131https://doaj.org/toc/1661-7827https://doaj.org/toc/1660-4601Accidents 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.Tong ZhuZishuo ZhuJie ZhangChenxuan YangMDPI AGarticlemixed logit modelheterogeneity in means and variancesinjury severityelectric bicycle crashesvisibilityMedicineRENInternational Journal of Environmental Research and Public Health, Vol 18, Iss 11131, p 11131 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	mixed logit model heterogeneity in means and variances injury severity electric bicycle crashes visibility Medicine R
spellingShingle	mixed logit model heterogeneity in means and variances injury severity electric bicycle crashes visibility Medicine R Tong Zhu Zishuo Zhu Jie Zhang Chenxuan Yang Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances
description	Accidents 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.
format	article
author	Tong Zhu Zishuo Zhu Jie Zhang Chenxuan Yang
author_facet	Tong Zhu Zishuo Zhu Jie Zhang Chenxuan Yang
author_sort	Tong Zhu
title	Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances
title_short	Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances
title_full	Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances
title_fullStr	Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances
title_full_unstemmed	Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances
title_sort	electric bicyclist injury severity during peak traffic periods: a random-parameters approach with heterogeneity in means and variances
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/89455cce4840484ebcb7b1f416affaf2
work_keys_str_mv	AT tongzhu electricbicyclistinjuryseverityduringpeaktrafficperiodsarandomparametersapproachwithheterogeneityinmeansandvariances AT zishuozhu electricbicyclistinjuryseverityduringpeaktrafficperiodsarandomparametersapproachwithheterogeneityinmeansandvariances AT jiezhang electricbicyclistinjuryseverityduringpeaktrafficperiodsarandomparametersapproachwithheterogeneityinmeansandvariances AT chenxuanyang electricbicyclistinjuryseverityduringpeaktrafficperiodsarandomparametersapproachwithheterogeneityinmeansandvariances
_version_	1718432368595828736

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

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