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Article

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

1
College of Transportation Engineering, Chang’an University, Xi’an 710064, China
2
Research Institute of Highway, Ministry of Transport, Beijing 100088, China
3
Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Paul B. Tchounwou
Int. J. Environ. Res. Public Health 2021, 18(21), 11131; https://doi.org/10.3390/ijerph182111131
Received: 2 September 2021 / Revised: 19 October 2021 / Accepted: 21 October 2021 / Published: 22 October 2021
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. View Full-Text
Keywords: mixed logit model; heterogeneity in means and variances; injury severity; electric bicycle crashes; visibility mixed logit model; heterogeneity in means and variances; injury severity; electric bicycle crashes; visibility
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MDPI and ACS Style

Zhu, T.; Zhu, Z.; Zhang, J.; Yang, C. Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances. Int. J. Environ. Res. Public Health 2021, 18, 11131. https://doi.org/10.3390/ijerph182111131

AMA Style

Zhu T, Zhu Z, Zhang J, Yang C. Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances. International Journal of Environmental Research and Public Health. 2021; 18(21):11131. https://doi.org/10.3390/ijerph182111131

Chicago/Turabian Style

Zhu, Tong, Zishuo Zhu, Jie Zhang, and Chenxuan Yang. 2021. "Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances" International Journal of Environmental Research and Public Health 18, no. 21: 11131. https://doi.org/10.3390/ijerph182111131

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