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Quantifying the impact of COVID-19 on e-bike safety in China via multi-output and clustering-based regression models

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  • Xingpei Yan
  • Zheng Zhu

Abstract

The impacts of COVID-19 on travel demand, traffic congestion, and traffic safety are attracting heated attention. However, the influence of the pandemic on electric bike (e-bike) safety has not been investigated. This paper fills the research gap by analyzing how COVID-19 affects China’s e-bike safety based on a province-level dataset containing e-bike safety metrics, socioeconomic information, and COVID-19 cases from 2017 to 2020. Multi-output regression models are adopted to investigate the overall impact of COVID-19 on e-bike safety in China. Clustering-based regression models are used to examine the heterogeneous effects of COVID-19 and the other explanatory variables in different provinces/municipalities. This paper confirms the high relevance between COVID-19 and the e-bike safety condition in China. The number of COVID-19 cases has a significant negative effect on the number of e-bike fatalities/injuries at the country level. Moreover, two clusters of provinces/municipalities are identified: one (cluster 1) with lower and the other (cluster 2 that includes Hubei province) higher number of e-bike fatalities/injuries. In the clustering-based regressions, the absolute coefficients of the COVID-19 feature for cluster 2 are much larger than those for cluster 1, indicating that the pandemic could significantly reduce e-bike safety issues in provinces with more e-bike fatalities/injuries.

Suggested Citation

  • Xingpei Yan & Zheng Zhu, 2021. "Quantifying the impact of COVID-19 on e-bike safety in China via multi-output and clustering-based regression models," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-15, August.
    • Handle: RePEc:plo:pone00:0256610
    DOI: 10.1371/journal.pone.0256610
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    References listed on IDEAS

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    1. Zhang, Yunchang & Fricker, Jon D., 2021. "Quantifying the impact of COVID-19 on non-motorized transportation: A Bayesian structural time series model," Transport Policy, Elsevier, vol. 103(C), pages 11-20.
    2. Jie Zhang & Baoheng Feng & Yina Wu & Pengpeng Xu & Ruimin Ke & Ni Dong, 2021. "The effect of human mobility and control measures on traffic safety during COVID-19 pandemic," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-9, March.
    3. Vickerman, Roger, 2021. "Will Covid-19 put the public back in public transport? A UK perspective," Transport Policy, Elsevier, vol. 103(C), pages 95-102.
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