Initial impact of COVID-19’s stay-at-home order on motor vehicle traffic and crash patterns in Connecticut: an interrupted time series analysis
Mitchell L. Doucette, Andrew Tucker, Marisa E. Auguste, A. M. Watkins, Christa Green, Flavia Pereira, Kevin Borrup, David S. Shapiro, Garry Lapidus
Abstract
INTRODUCTION: Understanding how the COVID-19 pandemic has impacted our health and safety is imperative. This study sought to examine the impact of COVID-19's stay-at-home order on daily vehicle miles travelled (VMT) and MVCs in Connecticut. METHODS: Using an interrupted time series design, we analysed daily VMT and MVCs stratified by crash severity and number of vehicles involved from 1 January to 30 April 2017, 2018, 2019 and 2020. MVC data were collected from the Connecticut Crash Data Repository; daily VMT estimates were obtained from StreetLight Insight's database. We used segmented Poisson regression models, controlling for daily temperature and daily precipitation. RESULTS: The mean daily VMT significantly decreased 43% in the post stay-at-home period in 2020. While the mean daily counts of crashes decreased in 2020 after the stay-at-home order was enacted, several types of crash rates increased after accounting for the VMT reductions. Single vehicle crash rates significantly increased 2.29 times, and specifically single vehicle fatal crash rates significantly increased 4.10 times when comparing the pre-stay-at-home and post-stay-at-home periods. DISCUSSION: Despite a decrease in the number of MVCs and VMT, the crash rate of single vehicles increased post stay-at-home order enactment in Connecticut after accounting for reductions in VMT.