COVID-19 lockdown and fatal motor vehicle collisions due to speed-related traffic violations in Japan: a time-series study

Inada, Haruhiko; Ashraf, Lamisa; Campbell, Sachalee

doi:10.1136/injuryprev-2020-043947

Cited by 70 publications

(59 citation statements)

References 1 publication

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“…Could this be a result of greater collision severity, due to speeding on empty streets? [6][7] Or could it be a result of increased alcohol consumption? 12 Figure A2 in the Appendix shows the trends in speeding fines and fines relating to driving under the influence of alcohol or drugs in 2020 and previous years (before 2017 there did not seem to be much testing for alcohol).…”

Section: The Potential Role Of Speeding On Empty Streetsmentioning

confidence: 99%

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Empty Streets, Speeding and Motor Vehicle Collisions during Covid-19 Lockdowns: Evidence from Northern Ireland

Vandoros

Papailias

2021

Preprint

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Covid-19 and lockdowns have had spillover effects on other health outcomes. Motor vehicle collisions (MVC) are likely to have been affected by the pandemic due to, among others, less traffic volume and speeding on empty streets. This paper studies the impact of the pandemic on MVCs in Northern Ireland. Using monthly data on injuries and deaths, we find a steep decline in slight and serious injuries compared to what would have been expected in the absence of the pandemic. However, we find no effect on the number of deaths. Based on data from speeding tickets, a plausible explanation for the differential effect on the number of injuries and deaths is speeding on empty streets during the pandemic.

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Section: The Potential Role Of Speeding On Empty Streetsmentioning

confidence: 99%

“…the likelihood and severity of collision. [6][7] Second, factors associated with the pandemic have had an impact on driving behaviour.…”

Section: Introductionmentioning

confidence: 99%

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Empty Streets, Speeding and Motor Vehicle Collisions during Covid-19 Lockdowns: Evidence from Northern Ireland

Vandoros

Papailias

2021

Preprint

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“…4 We developed 30 contender models, leveraging both statistical and machine learning approaches such as ARIMA, TBATS, and a feed-forward autoregressive neural network that have been utilized in other related COVID-19 research that has sought to generate forecasts for the test dataset. [5][6][7][8] The point forecasts generated from different forecasting techniques are then compared with the actual data for the test period and the result is used as a proxy measure for accuracy. Numerous error metrics are used here, including Mean Absolute Scaled Error (MASE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Symmetric Mean Absolute Percentage Error (SMAPE).…”

Section: Training Data Test Datamentioning

confidence: 99%

Miami-Dade: A Case Study of Domestic Violence Arrests During the COVID-19 Pandemic

Kurland

2021

Preprint

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The global health crisis that started early in 2020 has triggered a surge of interest in the effect (if any) of COVID-19 on patterns of domestic violence.1 The first systematic review and meta-analysis examining domestic violence during the pandemic revealed quite a lot of diversity in the approaches used to measure potential effects. Drawing on the time series forecasting literature, this brief report contributes to the growing body of evidence around the issue of domestic violence during the pandemic. Arrest data from Miami-Dade County (US) are leveraged along with a robust approach towards model identification, which is used to generate a suitably accurate forecast against which the observed pandemic period domestic violence data can be compared. The pattern uncovered for Miami-Dade County was similar what was found in other U.S. cities that during the pandemic experienced spikes (+95 CI) in the level of domestic violence arrests that were greater than expected. Interestingly these spikes appeared shortly after dips (-95 CI) in observed arrests fell below the expected level.

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“…Second, lower levels of traffic volume would create the opportunity to speed on empty streets. 6-7 Speeding is a major collision risk factor, which is also likely to lead to more severe crashes and a higher likelihood of death.…”

Section: Introductionmentioning

confidence: 99%

Covid-19, Lockdowns and Motor Vehicle Collisions: Empirical Evidence from Greece

Vandoros

2021

Preprint

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Reduced mobility during Covid-19 lockdowns means fewer vehicles at risk of collision, but also an opportunity to speed on empty streets. Other collision risk factors that have changed during the pandemic include alcohol consumption, sleeping patterns, distraction, unemployment and economic uncertainty. Evidence on the impact of the Covid-19 pandemic on motor vehicle collisions is scarce, as such statistics are often released with a delay. The objective of this paper is to examine the impact of the first wave of the pandemic and the first lockdown on motor vehicle collisions and associated injuries and deaths in Greece. Using monthly data at the regional unit level, I provide descriptive evidence and subsequently follow a difference-in-difference econometric approach, comparing trends in 2020 to those of the previous five years while controlling for unemployment and petrol prices. I found a steep decline in collisions, injuries and deaths compared to what would have been otherwise expected. In March and April 2020, there were about 1,226 fewer collisions, 72 fewer deaths, 40 fewer serious injuries and 1,426 fewer minor injuries compared to what would have been expected in the absence of the pandemic.

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COVID-19 lockdown and fatal motor vehicle collisions due to speed-related traffic violations in Japan: a time-series study

Cited by 70 publications

References 1 publication

Empty Streets, Speeding and Motor Vehicle Collisions during Covid-19 Lockdowns: Evidence from Northern Ireland

Empty Streets, Speeding and Motor Vehicle Collisions during Covid-19 Lockdowns: Evidence from Northern Ireland

Miami-Dade: A Case Study of Domestic Violence Arrests During the COVID-19 Pandemic

Covid-19, Lockdowns and Motor Vehicle Collisions: Empirical Evidence from Greece

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