scholarly journals Increased motor vehicle crashes following the 2016 Kumamoto earthquake, Japan: an interrupted time series analysis of property damage crashes

2021 ◽  
Author(s):  
Takuya Maruyama ◽  
Kazutake Taguchi
Keyword(s):  
Time Series ◽  
Motor Vehicle ◽  
Interrupted Time Series ◽  
Motor Vehicle Crashes ◽  
Vehicle Crashes ◽  
2016 Kumamoto Earthquake ◽  
Interrupted Time Series Analysis ◽  
Property Damage ◽  
Affected Area ◽  
The 2016 Kumamoto Earthquake

AbstractDriving after natural disasters entails a substantial amount of stress; therefore, the number of motor vehicle crashes may increase. However, few studies have examined this issue. This study investigated motor vehicle crashes after the 2016 Kumamoto earthquake in Japan. Monthly data about crashes resulting in property damage from 49 municipalities in Kumamoto from 2015 to 2018 were used. An interrupted time series analysis using Poisson or negative binomial regression models was conducted for 49 municipalities; the models were estimated for four classified areas to obtain the robust results. We found that property damage crashes increased significantly in the heavily affected area (Relative Risk (RR) = 1.48, 95% Confidence interval (CI): 1.29, 1.71) and the affected area (RR = 1.25, 95% CI: 1.15, 1.36) after the earthquake. A mountainous area showed a reduction in property damage crashes despite its heavy damage (RR = 0.74, 95% CI: 0.67, 0.82), which can be attributed to the closure of its main gate routes. The unaffected area showed no difference before and after the earthquake. Geographical presentation of the result demonstrates a clear positive association of earthquake damage and increased crashes. The findings of this study highlight the importance of motor-vehicle-crash alerts after an earthquake.

scholarly journals Ridesharing and Motor Vehicle Crashes in 4 US Cities: An Interrupted Time-Series Analysis

2017 ◽  
Vol 187 (2) ◽  
pp. 224-232 ◽  
Author(s):  
Christopher N Morrison ◽  
Sara F Jacoby ◽  
Beidi Dong ◽  
M Kit Delgado ◽  
Douglas J Wiebe
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Motor Vehicle ◽  
Interrupted Time Series ◽  
Motor Vehicle Crashes ◽  
Vehicle Crashes ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
Us Cities

Motor Vehicle Crashes Involving a Bicycle Before and After Introduction of a Bike Share Program in Philadelphia, Pennsylvania, 2010–2018

2020 ◽  
Vol 110 (6) ◽  
pp. 863-867
Author(s):  
Ghassan B. Hamra ◽  
Leah H. Schinasi ◽  
D. Alex Quistberg
Keyword(s):  
Motor Vehicle ◽  
Interrupted Time Series ◽  
Motor Vehicle Crashes ◽  
Vehicle Crashes ◽  
Interrupted Time Series Analysis ◽  
The Road ◽  
Before And After ◽  
On The Road ◽  
The Impact ◽  
Bike Share

Objectives. To quantify the impact of a citywide bicycle share program on rates of motor vehicle collisions involving a bicycle. Methods. We conducted an interrupted time series analysis, using crash records from the Pennsylvania Department of Transportation for Philadelphia County from 2010 through 2018. We also calculated summary statistics to illustrate annual and monthly trends in rates of motor vehicle crashes involving a bicycle. Results. The baseline rate of bike events was 106% greater (95% confidence interval [CI] = 1.25, 3.38) at the time bicycle share was implemented compared with January 2010. Before bicycle share implementation, the rate of bicycle events decreased 1% (95% CI = 0.95, 1.03) annually. After the bicycle share program started, the rate of bicycle events decreased 13% (95% CI = 0.82, 0.94) annually. Conclusions. In the long term, programs that increase the number of bicycles on the road, such as bike share, may reduce rates of motor vehicle crashes involving a bicycle.

scholarly journals Florida’s Opioid Crackdown and Mortality From Drug Overdose, Motor Vehicle Crashes, and Suicide: A Bayesian Interrupted Time-Series Analysis

2020 ◽  
Vol 189 (9) ◽  
pp. 885-893
Author(s):  
Kenneth A Feder ◽  
Ramin Mojtabai ◽  
Elizabeth A Stuart ◽  
Rashelle Musci ◽  
Elizabeth J Letourneau
Keyword(s):  
Time Series ◽  
Drug Overdose ◽  
Motor Vehicle ◽  
Interrupted Time Series ◽  
Credible Interval ◽  
Motor Vehicle Crash ◽  
Motor Vehicle Crashes ◽  
Structural Time Series ◽  
Vehicle Crash ◽  
Overdose Deaths

Abstract In 2011, Florida established a prescription drug monitoring program and adopted new regulations for independent pain-management clinics. We examined the association of those reforms with drug overdose deaths and other injury fatalities. Florida’s postreform monthly mortality rates—for drug-involved deaths, motor vehicle crashes, and suicide by means other than poisoning—were compared with a counterfactual estimate of what those rates would have been absent reform. The counterfactual was estimated using a Bayesian structural time-series model based on mortality trends in similar states. By December 2013, drug overdose deaths were down 17% (95% credible interval: −21, −12), motor vehicle crash deaths were down 9% (95% credible interval: −14, −4), and suicide deaths were unchanged compared with what would be expected in the absence of reform. Florida’s opioid prescribing reform substantially reduced drug overdose deaths. Reforms may also have reduced motor vehicle crash deaths but were not associated with a change in suicides. More research is needed to understand these patterns. Bayesian structural time-series modeling is a promising new approach to interrupted time-series studies.

scholarly journals Initial impact of COVID-19’s stay-at-home order on motor vehicle traffic and crash patterns in Connecticut: an interrupted time series analysis

2020 ◽  
pp. injuryprev-2020-043945
Author(s):  
Mitchell L Doucette ◽  
Andrew Tucker ◽  
Marisa E Auguste ◽  
Amy Watkins ◽  
Christa Green ◽  
...  
Keyword(s):  
Time Series ◽  
Motor Vehicle ◽  
Interrupted Time Series ◽  
Data Repository ◽  
Interrupted Time Series Analysis ◽  
Crash Rates ◽  
Single Vehicle ◽  
The Mean ◽  
The Impact ◽  
At Home

IntroductionUnderstanding 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.MethodsUsing 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.ResultsThe 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.DiscussionDespite 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.

Time-Series Analysis of Workload for Support in Rebuilding Disaster Victims’ Lives – Comparison of the 2016 Kumamoto Earthquake with the 2007 Niigataken Chuetsu-oki Earthquake –

2017 ◽  
Vol 12 (6) ◽  
pp. 1161-1173
Author(s):  
Munenari Inoguchi ◽  
Keiko Tamura ◽  
Haruo Hayashi ◽  
Keisuke Shimizu ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Time Series Analysis ◽  
2016 Kumamoto Earthquake ◽  
Property Damage ◽  
Disaster Victims ◽  
Kumamoto Earthquake ◽  
Series Analysis ◽  
The 2016 Kumamoto Earthquake

Japan has experienced many disasters. However, the question of when and how much work is generated in support of rebuilding disaster victims’ lives remains unsolved. Considering this situation, this study solves the question through a time-series analysis of daily workload in support of rebuilding the lives of victims of the 2016 Kumamoto Earthquake. In addition, a correlation analysis is conducted through comparison with the case of the 2007 Chuetsu-oki Earthquake that a prior study focused on, and another correlation analysis is conducted between municipalities affected by the Kumamoto Earthquake. These analyses do not indicate the presence of a high correlation in tasks for which the requirements for payment vary depending on the disaster but do indicate the presence of a high correlation between disasters in tasks for which the requirements for payment are uniform, thereby indicating the presence of a possible generalization. In addition, the correlation analysis results of comparisons between municipalities affected by the Kumamoto Earthquake indicate a high correlation between local public entities that have suffered great human and property damage. These results indicate that when a certain condition is met, it is highly likely that daily workload can be estimated.

scholarly journals High Ambient Temperatures and Risk of Motor Vehicle Crashes in Catalonia, Spain (2000–2011): A Time-Series Analysis

2015 ◽  
Vol 123 (12) ◽  
pp. 1309-1316 ◽  
Author(s):  
Xavier Basagaña ◽  
Juan Pablo Escalera-Antezana ◽  
Payam Dadvand ◽  
Òscar Llatje ◽  
Jose Barrera-Gómez ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Motor Vehicle ◽  
Motor Vehicle Crashes ◽  
Vehicle Crashes ◽  
Ambient Temperatures ◽  
Series Analysis
Sign in / Sign up

Export Citation Format

Share Document