Changes in the number of traffic collisions during the various waves of COVID-19 infection in Japan

An analysis of the national traffic collision trends in Japan for the January 2018 to June 2022 period using existing statistical data indicates that the number of traffic incidents, injuries, and fatalities decreased over time. After the outbreak of COVID-19 in December 2019, traffic volume decreas...

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Bibliographic Details
Main Authors: Ryo Shimada, Kazuhiko Kibayashi
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2022-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9754189/?tool=EBI
Description
Summary:An analysis of the national traffic collision trends in Japan for the January 2018 to June 2022 period using existing statistical data indicates that the number of traffic incidents, injuries, and fatalities decreased over time. After the outbreak of COVID-19 in December 2019, traffic volume decreased. In this study, to explore how the COVID-19 pandemic correlates with traffic collisions, we used the Spearman rank correlation of non-parametric statistical test to compare the number of COVID-19 infections with the number of traffic collisions. The number of COVID-19 infections showed a significant inverse correlation with the number of traffic collisions nationwide, in some regions, and in some prefectures. When the number of COVID-19 infections increased, a State of Emergency or Semi-Emergency Spread Prevention Measures were repeatedly declared. We submit that these measures along with the restrictions on the population’s autonomy and movement to prevent the spread of infection, reduces the number of traffic incidents, injuries, and fatalities owing to a decrease in traffic volume. Therefore, these lessons learned from the COVID-19 pandemic advocate that regulation of vehicle traffic volume is an effective means of reducing the occurrence of traffic collisions. These results can be applied to future policy development to support road safety improvements during unique events.
ISSN:1932-6203