Quantifying the impact of COVID-19 on e-bike safety in China via multi-output and clustering-based regression models.
PLoS One
; 16(8): e0256610, 2021.
Article
in English
| MEDLINE | ID: covidwho-1367710
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Wounds and Injuries
/
Bicycling
/
Accidents, Traffic
/
COVID-19
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
Limits:
Humans
Country/Region as subject:
Asia
Language:
English
Journal:
PLoS One
Journal subject:
Science
/
Medicine
Year:
2021
Document Type:
Article
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