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Modeling the Impact of COVID-19 Interventions on Interstate Crash Rates Using Comparative Interrupted Time Series

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Background Various strategies to reduce the spread of COVID-19 including lockdown and stay-at-home order are expected to reduce road traffic characteristics and consequently road traffic collisions (RTCs). We aimed to review the effects of the COVID-19 pandemic on the incidence, patterns, and severity of the injury, management, and outcomes of RTCs and give recommendations on improving road safety during this pandemic. Methods We conducted a narrative review on the effects of COVID-19 pandemic on RTCs published in English language using PubMed, Scopus, and Google Scholar with no date restriction. Google search engine and websites were also used to retrieve relevant published literature, including discussion papers, reports, and media news. Papers were critically read and data were summarized and combined. Results Traffic volume dropped sharply during the COVID-19 pandemic which was associated with significant drop in RTCs globally and a reduction of road deaths in 32 out of 36 countries in April 2020 compared with April 2019, with a decrease of 50% or more in 12 countries, 25 to 49% in 14 countries, and by less than 25% in six countries. Similarly, there was a decrease in annual road death in 33 out of 42 countries in 2020 compared with 2019, with a reduction of 25% or more in 5 countries, 15–24% in 13 countries, and by less than 15% in 15 countries. In contrast, the opposite occurred in four and nine countries during the periods, respectively. There was also a drop in the number of admitted patients in trauma centers related to RTCs during both periods. This has been attributed to an increase in speeding, emptier traffic lanes, reduced law enforcement, not wearing seat belts, and alcohol and drug abuse. Conclusions The COVID-19 pandemic has generally reduced the overall absolute numbers of RTCs, and their deaths and injuries despite the relative increase of severity of injury and death. The most important factors that affected the RTCs are decreased mobility with empty lines, reduced crowding, and increased speeding. Our findings serve as a baseline for injury prevention in the current and future pandemics.
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Between March and May 2020, Japan experienced a lockdown due to the COVID-19 crisis. Empty roads possibly triggered speed-related traffic violations that caused fatal motor vehicle collisions (MVCs). Using police data on the monthly number of fatal MVCs between January 2010 and February 2020 in which motor vehicle drivers were at fault, we forecasted the numbers of fatal MVCs due to the speed-related violations during the lockdown and compared these with those observed. We also compared the observed to forecasted using the ratio of the number of speed-related fatal MVCs to that of non-speed related fatal MVCs. The observed numbers of speed-related fatal MVCs were within the 95% CIs of the forecasted numbers. The observed ratio was higher than the forecasted ratio in April (p=0.016). In the second month of the lockdown, drivers were more likely to commit speed-related violations that caused fatal MVCs than before the lockdown.
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The spread of the COVID-19 virus has resulted in unprecedented measures restricting travel and activity participation in many countries. Social distancing, i.e., reducing interactions between individuals in order to slow down the spread of the virus, has become the new norm. In this viewpoint I will discuss the potential implications of social distancing on daily travel patterns. Avoiding social contact might completely change the number and types of out-of-home activities people perform, and how people reach these activities. It can be expected that the demand for travel will reduce and that people will travel less by public transport. Social distancing might negatively affect subjective well-being and health status, as it might result in social isolation and limited physical activity. As a result, walking and cycling, recreationally or utilitarian, can be important ways to maintain satisfactory levels of health and well-being. Policymakers and planners should consequently try to encourage active travel, while public transport operators should focus on creating ways to safely use public transport.
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This article introduces the ITSA command, which performs interrupted time series analysis for single and multiple group comparisons. In an interrupted time series analysis, an outcome variable is observed over multiple, equally spaced time periods before and after the introduction of an intervention which is expected to interrupt its level and/or trend. The ITSA command estimates the effect of an intervention on an outcome variable for either a single treatment group or when compared with one or more control groups. Additionally, its options allow the user to control for autocorrelated disturbances and to estimate treatment effects over multiple periods.
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With the rising number of cases and deaths from the COVID-19 pandemic, nations and local governments, including many across the U.S., imposed travel restrictions on their citizens. This travel restriction order led to a significant reduction in traffic volumes and a generally lower exposure to crashes. However, recent preliminary statistics in the US suggest an increase in fatal crashes over the period of lockdown in comparison to the same period in previous years. This study sought to investigate how the pandemic affected road crashes and crash outcomes in Alabama. Daily vehicle miles traveled and crashes were obtained and explored. To understand the factors associated with crash outcomes, four crash-severity models were developed: (1) Single-vehicle (SV) crashes prior to lockdown order (Normal times SV); (2) multi-vehicle (MV) crashes prior to lockdown order (Normal times MV); (3) Single-vehicle crashes after lockdown order (COVID times SV); and (4) Multi-vehicle crashes after lockdown order (COVID times MV). The models were developed using the first 28 weeks of crashes recorded in 2020. The findings of the study reveal that although traffic volumes and vehicle miles traveled had significantly dropped during the lockdown, there was an increase in the total number of crashes and major injury crashes compared to the period prior to the lockdown order, with speeding, DUI, and weekends accounting for a significant proportion of these crashes. These observations provide useful lessons for road safety improvements during extreme events that may require statewide lockdown, as has been done with the COVID-19 pandemic. Traffic management around shopping areas and other areas that may experience increased traffic volumes provide opportunities for road safety stakeholders to reduce the occurrence of crashes in the weeks leading to an announcement of any future statewide or local lockdowns. Additionally, increased law enforcement efforts can help to reduce risky driving activities as traffic volumes decrease.
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Stay-at-home policies in response to COVID-19 transformed high-volume arterials and highways into lower-volume roads, and reduced congestion during peak travel times. To learn from the effects of this transformation on traffic safety, an analysis of crash data in Ohio’s Franklin County, U.S., from February to May 2020 is presented, augmented by speed and network data. Crash characteristics such as type and time of day are analyzed during a period of stay-at-home guidelines, and two models are estimated: (i) a multinomial logistic regression that relates daily volume to crash severity; and (ii) a Bayesian hierarchical logistic regression model that relates increases in average road speeds to increased severity and the likelihood of a crash being fatal. The findings confirm that lower volumes are associated with higher severity. The opportunity of the pandemic response is taken to explore the mechanisms of this effect. It is shown that higher speeds were associated with more severe crashes, a lower proportion of crashes were observed during morning peaks, and there was a reduction in types of crashes that occur in congestion. It is also noted that there was an increase in the proportion of crashes related to intoxication and speeding. The importance of the findings lay in the risk to essential workers who were required to use the road system while others could telework from home. Possibilities of similar shocks to travel demand in the future, and that traffic volumes may not recover to previous levels, are discussed, and policies are recommended that could reduce the risk of incapacitating and fatal crashes for continuing road users.
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Introduction: Recent research suggests that COVID-19 associated stay-at-home orders, or shelter-in-place orders, have impacted intra-and-interstate travel as well as motor vehicle crashes (crashes). We sought to further this research and to understand the impact of the stay-at-home order on crashes in the post order period in Connecticut. Methods: We used a multiple-comparison group, interrupted time-series analysis design to compare crashes per 100 million vehicle miles traveled (VMT) per week in 2020 to the average of 2017-2019 from January 1-August 31. We stratified crash rate by severity and the number of vehicles involved. We modeled two interruption points reflecting the weeks Connecticut implemented (March 23rd, week 12) and rescinded (May 20th, week 20) its stay-at-home order. Results: During the initial week of the stay-at-home order in Connecticut, there was an additional 28 single vehicle crashes compared to previous years (95% confidence interval (CI): [15.8, 36.8]). However, the increase at the order onset was not seen throughout the duration. Rescinding the stay-at-home order by and large did not result in an immediate increase in crash rates. Crash rates steadily returned to previous year averages during the post-stay-at-home period. Fatal crash rates were unaffected by the stay-at-home order and remained similar to previous year rates throughout the study duration. Discussion: The initial onset of the stay-at-home order in Connecticut was associated with a sharp increase in the single vehicle crash rate but that increase was not sustained for the remainder of the stay-at-home order. Likely changes in driver characteristics during and after the order kept fatal crash rates similar to previous years.
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The current study aims to investigate the impact of the COVID-19 pandemic on road traffic collisions, fatalities, and injuries using time series analyses. To that aim, a database containing road collisions, fatalities, and slight injuries data from Greece were derived from the Hellenic Statistical Authority (HSA) and covered a ten-year timeframe (from January 2010 to August 2020. The chosen time period contained normal operations, as well as the period of the first COVID-19-induced lockdown period in Greece. Three different Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models were implemented in order to compare the observed measurements to forecasted values that were intended to depict assumed conditions; namely, without the appearance of the COVID-19 pandemic. Modelling results revealed that the total number of road collisions, fatalities, and slightly injured were decreased, mainly due to the sharp traffic volume decrease. However, the percentage reduction of the collision variables and traffic volume were found to be disproportionate, which probably indicates that more collisions occurred with regard to the prevailing traffic volume. An additional finding is that fatalities and slightly injured rates were significantly increased during the lockdown period and the subsequent month. Overall, it can be concluded that a worse performance was identified in terms of road safety. Since subsequent waves of COVID-19 cases and other pandemics may reappear in the future, the outcomes of the current study may be exploited for the improvement of road safety from local authorities and policymakers.
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This paper evaluated the effect of the COVID-19 preventive orders on arterial roadway travel time reliability (TTR). A comparative analysis was conducted to examine average travel time distributions (TTD), and their associated TTR metrics, before and during the COVID-19 pandemic. Travel time data for four urban arterial corridors in Nebraska, disaggregated by peak period and direction, were analyzed. It was found that in 2020, the average TTD mean and standard deviation values for all 16 scenarios were reduced by an average of 14.0% and 43.4%, respectively. The travel time index, the planning time index, the level of travel time reliability (LOTTR), and the buffer index metrics associated with these TTDs were reduced, on average, by 14.0%, 19.7%, 3.5%, and 35.0%, respectively. In other words, whether the test corridors were more reliable during the pandemic was a function of which TTR metric was used. The paper concludes by arguing for a fundamental change in how arterial TTR is measured and reported to different user groups.
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To minimize transmission of coronavirus disease 2019 (COVID-19), most US states in spring 2020 passed policies promoting social distancing through stay-at-home orders prohibiting nonessential travel.¹ While vehicle miles traveled in the US decreased by 41% in April 2020 compared with 2019,² the effect of this mobility decrease on motor vehicle crashes (MVCs) is poorly understood. We estimated associations between COVID-19–related social distancing policies, traffic volume, and MVC-related outcomes in Ohio.
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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.
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Background: The effect of mandated societal lockdown to reduce the transmission of coronavirus disease 2019 (COVID-19) on road traffic accidents is not known. For this reason, we performed an in-depth analysis using data from Statewide Traffic Accident Records System. Materials and methods: We reviewed data on total 2292 road traffic accident records in Missouri from January 1, 2020 through May 15, 2020. We treated March 23 as the first day of mandated societal lockdown and May 3 as the first day of re-opening. Results: We have found that there was a significant reduction in road traffic accidents resulting in minor or no injuries (mean 14.5 versus 10.8, p < 0.0001) but not in accidents resulting in serious or fatal injuries (mean 3.4 versus 3.7, p = 0.42) after mandated societal lockdown. Furthermore, there was a significant reduction in road traffic accidents resulting in minor or no injuries after the mandated social lockdown (parameter estimate -5.9, p = 0.0028) in the time series analysis. There was an increase in road traffic accidents resulting in minor or no injuries after expiration of mandatory societal lockdown (mean 10.8 versus 13.7, p = 0.04). Conclusion: The mandated societal lockdown policies led to reduction in road traffic accidents resulting in non-serious or no injuries but not those resulting in serious or fatal injuries.
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In March 2020, the World Health Organization declared COVID-19 a world-wide pandemic. Countries introduced public health measures to contain and reduce its spread. These measures included closures of educational institutions, non-essential businesses, events and activities, as well as working from and staying at home requirements. These measures have led to an economic downturn of unprecedented proportions. Generally, as economic activity declines, travel decreases and drivers are exposed to a lower risk of collisions. However, research on previous economic downturns suggests economic downturns differentially affect driver behaviours and situations. COVID-19 pandemic effects on road safety are currently unknown. However, preliminary information on factors such as the increased stress and anxiety brought about by the COVID-19 pandemic, more “free” (idle) time, increased consumption of alcohol and drugs, and greater opportunities for speeding and stunt driving, might well have the opposite effect on road safety. Using an interactionist model we identify research questions for researchers to consider on potential person and situation factors associated with COVID-19 that could affect road safety during and after the pandemic. Collaborative efforts by researchers, and public and private sectors will be needed to gather data and develop road safety strategies in relation to the new reality of COVID-19.
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Road traffic crashes threaten thousands of drivers every day and significant efforts have been put forth to reduce the number and mitigate the impacts of traffic crashes. Although the last decade has witnessed substantial methodological improvements in crash prediction modelling, several methodological challenges still remain in terms of predicting crash frequencies of different injury severity levels. These challenges include spatial correlation and/or heterogeneity, temporal correlation and/or heterogeneity, and correlations between crash frequencies of different injury severity level. A framework of Bayesian multivariate space-time model is developed to address these challenges. A series of multivariate space-time models are proposed under the Full Bayesian framework with different assumptions on the spatial and temporal random effects. In addition to the ability to consider both temporal and spatial trends, the proposed framework is also capable of addressing complex correlations between crash types. It allows the underlying unobserved heterogeneity to be better captured and enables borrowing strength across spatial units and time points, as well as over crash types. The proposed methodology is illustrated using one-year daily traffic crash data from the mountainous interstate highway I70 in Colorado, which is categorized into no injury crash and injury crash. The results show that multivariate space-time model outperforms other alternatives, including multivariate random effects model and multivariate spatial models. The model comparison results highlight the importance to properly account for spatial effects, temporal effects and correlations between crash types.
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This paper describes the relationship between crash incidence rates and hourly traffic volume and discusses the influence of traffic on crash severity, based on observations made on 2000 km of French interurban motorways over 2 years. Incidence rates involving property damage-only crashes and injury-crashes are highest when traffic is lightest (under 400 vehicles/h). These incidence rates are at their lowest when traffic flows at a rate of 1000-1500 vehicles/h. For heavier traffic flows, crash incidence rates increase steadily as traffic increases on 2- and 3-lane motorways and inflect on 2-lane motorways when traffic increases to a level of 3000 vehicles/h. For an equivalent light traffic level, the number of crashes is higher on three-lane than on 2-lane motorways and higher at weekends (when truck traffic is restricted) than on weekdays. In heavy traffic, the number of crashes is higher on weekdays. We found no significant difference between the number of daytime and night-time crashes, whatever the traffic. No difference was observed in crash severity by number of lanes or period in the week for a given level of traffic. However, severity is greater at night and when hourly traffic is light. Compared to the number of vehicles on the road, light traffic is a safety problem in terms of frequency and severity, and road safety campaigns targeting motorway users to influence their behavior in these driving conditions should be introduced.
Documenting Nebraska’s path to recovery from the coronavirus (Covid-19) pandemic 2020-2021
  • Ballotpedia
The effect of Covid-19 lockdown on mobility and traffic accidents: Evidence from Louisiana.” Global Labor Organization Discussion Paper Series 616
  • S R Barnes
  • L.-P Beland
  • J Huh
  • D Kim
Observed mobility behavior data reveal social distancing inertia.” Preprint submitted
  • S J Ghader
  • M Zhao
  • W Lee
  • G Zhou
  • L Zhao
  • Zhang
Mobility trends in New York City during COVID-19 pandemic: Analyses of transportation modes throughout
  • C Kamga
  • B Moghimi
  • P Vicuna
  • S Mudigonda
  • R Tchamna
  • Kamga C.
Nebraska Department of Health & Human Services). 2021. “Department of Health and Human Services
  • Dhhs Nebraska
Nebraska DOT-Nebraska Department of Transportation Website
  • Dot Nebraska
Injury-severity analysis of lane change crashes involving commercial motor vehicles on interstate highways
  • E K Adanu
  • A L Elsa
  • T S Jones
  • Adanu E. K.
Impact of COVID-19 mitigation on California traffic crashes
  • F Shilling
  • D Waetjen
Traffic safety impact of Covid-19: Impact to Minnesota motor vehicle crashes March 1 to May 18. 2020.” Minnesota Department of Transportation
  • E D Devoe
  • I Leuer
  • M Saari
  • Wagner