How did the COVID-19 pandemic affect road crashes and crash outcomes in Alabama?

Lost years, mortality burden: the impact of COVID-19 pandemic on premature death due to road traffic accidents in a northern state in Malaysia

BACKGROUND

This study addresses the persistent global burden of road traffic fatalities, particularly in middle-income countries like Malaysia, by exploring the impact of the COVID-19 pandemic on Road Traffic Accident (RTA) fatalities in Perak state, Malaysia, with a secondary focus on applying Years of Life Lost (YLL) to understand the implications of these premature deaths.

METHODOLOGY

The cross-sectional study retrospectively reviewed certified RTA fatalities from 2018 to 2021, individually counting fatalities in accidents and excluding cases with incomplete death profiles. Data were collected from all Forensic Departments in the government hospitals in Perak. RTA fatalities were confirmed by medical officers/physicians following established procedures during routine procedures. A total of 2517 fatal accident and victim profiles were transcribed into data collection form after reviewing death registration records and post-mortem reports. Inferential analyses were used for comparison between pre- and during COVID-19 pandemic. The standard expected YLL was calculated by comparing the age of death to the external standard life expectancy curve taking into consideration of age and gender in Malaysia.

RESULTS

This study included 2207 (87.7%) of the RTA fatalities in Perak State. The analysis revealed a decreasing trend in RTA deaths from 2018 to 2021, with a remarkable Annual Percent Change (APC) of -25.1% in 2020 compared to the pre-pandemic year in 2019 and remained stable with lower APC in 2021. Comparison between pre-pandemic (2018-2019) and pandemic years (2020-2021) revealed a difference in the fatality distribution with a median age rise during the pandemic (37.7 (IQR: 22.96, 58.08) vs. 41.0 (IQR: 25.08, 61.00), p = 0.002). Vehicle profiles remained consistent, yet changes were observed in the involvement of various road users, where more motorcycle riders and pedestrian were killed during pandemic (p = 0.049). During pandemic, there was a decline in vehicle collisions, but slight increase of the non-collision accidents and incidents involving pedestrians/animals (p = 0.015). A shift in accident from noon till midnight were also notable during the pandemic (p = 0.028). YLL revealed differences by age and gender, indicating a higher YLL for females aged 30-34 during the pandemic.

CONCLUSION

The decline in RTA fatalities during COVID-19 pandemic underscores the influence of pandemic-induced restrictions and reduced traffic. However, demographic shifts, increased accident severity due to risky behaviors and gender-specific impacts on YLL, stress the necessity for improved safety interventions amidst evolving dynamics.

Post-lockdown burden of road injury involving hospitalisation in Victoria, Australia: A statewide, population-based time series analysis

OBJECTIVES

Ever since COVID-19, short-term changes in transport injury patterns have been observed. The aim is to examine both the initial and the enduring impact of government lockdown and the pandemic on road injuries requiring hospitalisation and road fatalities.

METHODS

Time series analysis of Transport Accident Commission (TAC) claims involving hospitalisation and fatalities in Victoria, Australia, from July 2016 to May 2023, including lockdown (March 2020 to October 2020) and post-lockdown (November 2020 onwards).

RESULTS

A total of 46 450 TAC claims were included. Average claims during the pre-pandemic period were 652/month. Lockdown restrictions were associated with a statistically significant fall in monthly claims (-255, 95% confidence interval [CI] = -315 to -194, P < 0.01). This was consistent across road users, days of the week, hours of the day, injury severity, sex and central versus rural locations. The post-lockdown period had a statistically significant reduction in monthly claims to 76% (95% CI = 67-84) of pre-pandemic levels (-158, 95% CI = -213 to -102, P <0.01). This was consistent across all subgroups except bicyclist injuries, which remained constant (-8, 95% CI = -16 to 0, P = 0.05). There was a significant upward trend in the fatality-to-claim ratio post-lockdown (0.001, 95% CI = 0-0.001, P <0.01).

CONCLUSION

Road injury requiring hospitalisation decreased significantly during governmental lockdown and has returned to three-quarters of pre-pandemic levels (except bicyclist injuries that have remained constant), but there is an increasingly disproportionate number of fatalities. This represents a new baseline of injury burden for EDs and hospitals that manage trauma patients.

Alcohol and drug presence in traffic crash fatalities before and after the COVID-19 pandemic: Evaluation of the fatality analysis reporting system (FARS) and linked medical examiner-vital records data in Clackamas, Multnomah, and Washington County, Oregon, 2019-2021

Traffic fatalities, with and from increased risky behaviors (reduced seat belt use, increased impairment from licit and illicit substances), have been increasing, especially during the COVID-19 pandemic. Death certificates are a major source of epidemiologic data in the United States, but have known underreporting of drug and alcohol presence. The Fatality Analysis Reporting System (FARS) is one major source of data on fatal crashes with intoxication. This study links FARS data for three counties in Oregon (2019-2021) with local medical examiner and death certificate data (FARS source data) and compares their concordance with blood alcohol concentration and toxicology for three major drug classes by year. For drivers only, our study finds good concordance between FARS and its source data in 2019 but poor concordance in 2020. This discordance may impact future analysis of impaired crash deaths, and we list some suggestions for amelioration.

Alcohol-related non-fatal motor vehicle crash injury in the US from 2019 to 2022

Background: Information on recent alcohol-related non-fatal motor vehicle crash (MVC) injuries is limited.Objectives: To analyze alcohol-related non-fatal MVC injuries, 2019-2022, considering COVID-19 and Stay-at-Home policies.Methods: State-level counts of alcohol-related non-fatal MVC injuries (involving individuals age 15+) from Emergency Medical Services data in 18 US states, chosen for comprehensive coverage, were analyzed for the annual rate. The total non-fatal MVC injury count in each state served as the denominator. We used analysis of variance to evaluate annual rate changes from 2019 to 2022 and used robust Poisson regression to compare annual mean rates to the 2019 baseline, pre-pandemic, excluding Quarter 1 due to COVID-19's onset in Quarter 2. Additional Poisson models compared rate changes by 2020 Stay-at-Home policies.Results: Data from 18 states were utilized (N = 1,487,626, 49.5% male). When evaluating rate changes of alcohol-related non-fatal MVC injuries from period 1 (Q2-4 2019) through period 4 (Q2-4 2022), the rate significantly increased from period 1 (2019) to period 2 (2020) by 0.024 (p = .003), then decreased from period 2 to period 4 (2022) by 0.016 (p = .04). Compared to the baseline (period 1), the rate in period 2 was 1.27 times higher. States with a 2020 Stay-at-Home policy, compared to those without, had a 30% lower rate (p = .05) of alcohol-related non-fatal MVC injuries. States with partial and mandatory Stay-at-Home policies had a 5.2% (p = .01) and 10.5% (p < .001) annual rate decrease, respectively.Conclusion: Alcohol-related non-fatal MVC injury rates increased initially (2019-2020) but decreased thereafter (2020-2022). Stay-at-home policies effectively reduced these rates.

Racial and ethnic disparities in motor vehicle crash-related outcomes in North Carolina surrounding the COVID-19 pandemic

Long-term impacts of the COVID-19 pandemic on racial and ethnic disparities in motor vehicle crash (MVC) injuries and death are poorly understood. This study aimed to characterize trends and investigate the heterogeneity of MVC-related disparities in North Carolina across several data sources. Crash reports, emergency department visit records, and death certificates from 2018 to 2021 were used to calculate monthly population-rates of MVC-related public health outcomes. We estimated trendlines using joinpoint regression and compared outcomes across racial and ethnic classifications. MVC and MVC-related injury rates declined in conjunction with NC's stay-at-home order, while rates of severe outcomes remained unimpacted. By December 2021 rates of MVC-related outcomes met or exceeded pre-pandemic levels, with the highest rates observed among non-Hispanic Black individuals. Racial and ethnic disparities in MVC-related outcomes remained prevalent throughout the COVID-19 pandemic. These results highlight the importance of a holistic approach to traffic injury surveillance when assessing the impact of MVCs.

Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data

The COVID-19 pandemic has had unprecedented impacts on the way we get around, which has increased the need for physical and social distancing while traveling. Shared mobility, as an emerging travel mode that allows travelers to share vehicles or rides has been confronted with social distancing measures during the pandemic. On the contrary, the interest in active travel (e.g., walking and cycling) has been renewed in the context of pandemic-driven social distancing. Although extensive efforts have been made to show the changes in travel behavior during the pandemic, people's post-pandemic attitudes toward shared mobility and active travel are under-explored. This study examined Alabamians' post-pandemic travel preferences regarding shared mobility and active travel. An online survey was conducted among residents in the State of Alabama to collect Alabamians' perspectives on post-pandemic travel behavior changes, e.g., whether they will avoid ride-hailing services and walk or cycle more after the pandemic. Machine learning algorithms were used to model the survey data (N = 481) to identify the contributing factors of post-pandemic travel preferences. To reduce the bias of any single model, this study explored multiple machine learning methods, including Random Forest, Adaptive Boosting, Support Vector Machine, K-Nearest Neighbors, and Artificial Neural Network. Marginal effects of variables from multiple models were combined to show the quantified relationships between contributing factors and future travel intentions due to the pandemic. Modeling results showed that the interest in shared mobility would decrease among people whose one-way commuting time by driving is 30-45 min. The interest in shared mobility would increase for households with an annual income of $100,000 or more and people who reduced their commuting trips by over 50% during the pandemic. In terms of active travel, people who want to work from home more seemed to be interested in increasing active travel. This study provides an understanding of future travel preferences among Alabamians due to COVID-19. The information can be incorporated into local transportation plans that consider the impacts of the pandemic on future travel intentions.

Changes in traffic crash patterns: Before and after the outbreak of COVID-19 in Florida

In the twentieth year of the twenty-first century, humanity is facing an unprecedented global crisis owing to the COVID-19 pandemic. It has brought about drastic changes in the way we live and work, as well as the way we move from one place to another, namely transportation. Previous studies have preliminarily found that mobility, travel behavior, and road traffic safety status experienced great changes after the outbreak of the COVID-19. The objective of this study is to explore how crash patterns have changed, as well as the contributing factors of such changes and the heterogeneity between counties in Florida. Thus, data of COVID-19 cases, crash, socioeconomic factors, and traffic volume of 2019 and 2020 are collected. Preliminary analyses show a considerable reduction from March to June. Substantial changes are shown in the proportions of crashes by time of occurrence and injury severity. Two types of statistical models are developed to identify factors of (1) changes in the percentages of crashes by type and (2) the numbers of crashes by type. The developed models reveal various demographic, socioeconomic, and travel factors. After controlling other factors, the total numbers of crashes are 14% lower after the outbreak. The most significant reductions are observed in peak-hour (22%), while no significant change is found in fatal crashes. The results show that the number of crashes has significantly decreased even after controlling the traffic volume, but some crash types (e.g., fatal) did not show a significant reduction. The findings are expected to provide some insights into better transportation planning and management to ensure traffic safety in a possible future epidemic.

Near-term impact of COVID-19 pandemic on seniors' crash size and severity

Recent research revealed that COVID-19 pandemic was associated with noticeable changes in travel demand, traffic volumes, and traffic safety measures. Despite the reduction of traffic volumes across the US, several recent studies indicated that crash rates increased across different states during COVID-19 pandemic. Although some recent studies have focused on examining the changes in traffic conditions and crash rates before and during the pandemic, not enough research has been conducted to identify risk factors to crash severity. Even the limited research addressing the contributing factors to crash severity were focused on the pool category of drivers and no insight is available regarding older drivers, one of the most vulnerable groups to traffic collision and coronavirus. Moreover, these studies investigated the early impact of the COVID-19 pandemic mostly using up to three months of data. However, near-term and long-term effects of the COVID-19 pandemic are still unknown on traffic collisions. Therefore, this study aims to contribute to the literature by studying the near-term impact of the COVID-19 pandemic on crash size and severity among older drivers. To this end, a relatively large sample of crash data with senior drivers at fault was obtained and analyzed. To identify the main contributing factors affecting crash outcomes, Exploratory Factor Analysis was conducted on a high-dimension data set to identify potential latent factors which were validated through Confirmatory Factor Analysis. After that, Structural Equation Modeling technique was performed to examine the associations among the identified independent latent factors and the dependent variable. Additionally, SEM model identified the impact of the COVID-19 pandemic on seniors' crash severity. The findings reveal that several latent variables were the significant predictors of crash severity of older drivers including "Driving maneuver & crash location", "Road features and traffic control devices", "Driver condition & behavior", "Road geometric characteristics", "Crash time and lighting", and "Road class" latent factors. The binary variable of "Pandemic" was found to be as highly significant as the last four latent factors mentioned above. This means not only were older drivers more likely to be involved in higher crash size with higher severity level during the pandemic period, but also "Pandemic" was a risk factor to seniors as much as "Driver condition & behavior", "Road geometric characteristics", "Crash time & lighting", and "Road class" factors. The results of this study provide useful insights that may improve road safety among senior drivers during pandemic periods like COVID-19.

Assessing the Effect of COVID-19 on the Traffic Safety of Intercity and Major Intracity Roads in Saudi Arabia

The coronavirus disease 2019 (COVID-19) pandemic led to rapid and unexpected changes across the world, particularly in road safety. Thus, this work assesses the impact of COVID-19 accompanied by government preventive policies on road safety in Saudi Arabia by investigating the crash frequency and crash rates. A 4-year crash dataset relating to 2018-2021 was collected, covering about 71,000 km in total road length. It covers all intercity roads and some of the major intercity roads in Saudi Arabia with over 40,000 data logs of involved crashes. We considered three different time phases to observe road safety. These time phases were identified by the duration of government curfew measures against COVID-19 (before, during, and after). The crash frequency analysis showed that the curfew during COVID-19 significantly impacted the crash reduction. At a national level, the crash frequency decreased during 2020 and reached a 33.2% reduction compared to 2019 (the previous year), and it surprisingly continued decreasing in 2021 (the consequent year) to another 37.7% reduction although the government measures were lifted. Moreover, considering the traffic volume and road geometry, we analyzed crash rates for 36 selected segments, and the results showed a significant reduction in the crash rate before and after the COVID-19 pandemic. Additionally, a random effect negative binomial model was developed to quantify the impact of the COVID-19 pandemic. The results showed that the reduction in crashes was significant during and after COVID-19. Also, single roads (two-lane, two-way) were found to be more dangerous than other types of roads.

Analysis of Motor Vehicle Accidents: Comparison Between Before and During the COVID-19 Lockdown in Maharashtra, India

To prevent the pandemic spread of human-to-human transmitted diseases such as COVID-19, governments commonly resort to countrywide or regional lockdown strategies. Such lockdowns, whenever and wherever implemented, curtail the movement of persons and vehicles, and drastically alter traffic conditions. This study focuses on the effect of drastic and sudden changes in the traffic conditions, during the COVID-19 lockdown in the State of Maharashtra in India, in March-June 2020, on the numbers of motor vehicle accidents (MVAs), and the resultant fatalities and injuries. Content analysis of police-reported first information reports (FIRs) of MVAs is performed, and these lockdown trends are compared with archival data from corresponding previous (normal) periods. The statistical analysis shows that, during the lockdown, while the total number of MVAs fall drastically, they are more severe and have a much higher fatality rate per MVA. Also, the pattern of vehicles involved in MVAs, and resultant pattern of fatalities, also changes during lockdowns. The paper explores the reasons for these changed patterns and provides suggestions to reduce these negative externalities of pandemic related lockdowns.

Urban Traumatic Moving Injuries Before and During SARS-CoV-2: A Multilinear Regression Analysis

Background The onset of the coronavirus pandemic (COVID-19/SARS-CoV-2) saw an overall decline in traffic. Fundamental shifts in the pattern of traffic-related traumas were observed across the United States and beyond.  Objectives This study aims to predict changes in the length of stay (LOS) for patients sustaining traumatic moving injuries before and during the coronavirus pandemic.  Methods All moving injuries (bicycle accidents, pedestrians struck, motor vehicle/motorcycle accidents) before and during the first SARS-CoV-2 wave in the US were extracted from our hospital's trauma registry. The study period was from March 1st to October 31st of 2019 and 2020, respectively. Ordinary least squares (OLS) multilinear regression models were estimated with a significance level of 0.05.  Results In both periods, the Glasgow coma scores (GCS), ICU LOS, injury severity scores (ISS), and admitting service had significant impacts on hospital duration. Higher GCS scores increased the hospital LOS by 0.811 days in 2019 and 0.587 days in 2020. A higher ISS resulted in an increase in LOS by 0.207 days in 2019 and 0.124 days in 2020. The ICU admissions increased LOS by 0.82 days in 2019 and 1.25 days in 2020. Admissions to trauma services increased in duration by 2.111 days in 2019 and 1.379 days in 2020. Average LOS dropped from 3.09 to 2.50 days between both periods.  Conclusion Our trauma center saw significant changes in the admission patterns of moving injuries during COVID-19. We must therefore be better prepared to handle increased volume during public health emergencies and potential reductions in trauma utilization. Local injury prevention efforts may help reduce the burden on trauma centers during such emergencies as they did during COVID-19, allowing for greater focus on non-trauma patients.

Road crash risk prediction during COVID-19 for flash crowd traffic prevention: The case of Los Angeles

Road crashes are a major problem for traffic safety management, which usually causes flash crowd traffic with a profound influence on traffic management and communication systems. In 2020, the sudden outbreak of the novel coronavirus disease (COVID-19) pandemic led to significant changes in road traffic conditions. In this paper, by analyzing crash data from 2016 to 2020 and new COVID-19 case data in 2020, we find that the average crash severity and crash deaths during this period (a rapid increase of new COVID-19 cases in 2020) are higher than those in previous four years. Hence, it is necessary to exploit a novel road crash risk prediction model for such an emergency. We propose a novel data-adaptive fatigue focal loss (DA-FFL) method by fusing fatigue factors to establish a road crash risk prediction model under the scenario of large-scale emergencies. Finally, the experimental results demonstrate that DA-FFL performs better than the other typical methods in terms of area under curve (AUC) and false alarm rate (FAR) for imbalanced data. Furthermore, DA-FFL has better prediction performance in convolutional neural networks-long short-term memory (CNN-LSTM).

Characteristics, likelihood and challenges of road traffic injuries in China before COVID-19 and in the postpandemic era

Through a review of previous studies, this paper analysed the epidemiological characteristics and attempts to determine the various trends of road traffic injuries (RTIs) in China before and after the coronavirus disease 2019 (COVID-19). This paper proposed effective measures and suggestions for responding to RTIs in China. Moreover, this paper aimed to provide some references for studies on RTIs in the future. According to a reference review, 50 articles related to RTIs were published and viewed in the China National Knowledge Infrastructure (CNKI), Wanfang database, Weipu (VIP) database and PubMed/MEDLINE database. Articles were selected according to the exclusion and inclusion criteria and then classified and summarized. Regarding cases, RTIs in China were highest in summer, autumn, and in rural areas and lowest in February. Men, elderly individuals and people living in rural areas were more susceptible to RTIs. In addition, thanks to effective and proactive policies and measures, the number of RTIs and casualties in China has substantially decreased, while there has been a growing number of traffic accidents along with the increase in nonmotor vehicles. However, it is worth noting that the number of RTIs obviously fell during the COVID-19 pandemic due to traffic lockdown orders and home quarantine policies. Nevertheless, accidents related to electric bicycles increased unsteadily because of the reduction in public transportation use at the same time. The factors that cause RTIs in China can be divided into four aspects: human behaviours, road conditions, vehicles and the environment. As a result, measures responding to RTIs should be accordingly proposed. Moreover, the road traffic safety situation in developing countries was more severe than that in developed countries. RTIs in China showed a downward trend attributed to road safety laws and various policies, and the downward trend was more significant during the COVID-19 pandemic owing to traffic lockdowns and home quarantine measures. It is urgent and necessary to promote road traffic safety, reduce injuries, and minimize the burden of injuries in developing countries.

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 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.

Injury mortality and morbidity changes due to the COVID-19 pandemic in the United States

Introduction

The COVID-19 pandemic significantly changed society. We aimed to examine the systematic impact of the COVID-19 on injury burden in the United States.

Methods

We extracted mortality and morbidity data from CDC WONDER and WISQARS. We estimated age-standardized injury mortality rate ratio and morbidity rate ratio (MtRR and MbRR) with 95% confidence interval (95% CI) for all injuries, all unintentional injuries, homicide/assault by all methods, suicide/self-harm by all methods, as well as other 11 specific unintentional or intentional injury categories. Injury rate ratios were compared for 2020 vs. 2019 to those of 2019 vs. 2018 to demonstrate the influence of the COVID-19 pandemic on fatal and nonfatal injury burden. The ratio of MtRRs (RMtRR) and the ratio of MbRRs (RMbRR) with 95% CI between 2020 vs. 2019 and 2019 vs. 2018 were calculated separately.

Results

The COVID-19 pandemic was associated with an increase in injury mortality (RMtRR = 1.12, 95% CI: 1.11, 1.13) but injury morbidity decreased (RMbRR = 0.88, 95% CI: 0.88, 0.89) when the changes of these rates from 2019 to 2020 were compared to those from 2018 to 2019. Mortality disparities between the two time periods were primarily driven by greater mortality during the COVID-influenced 2020 vs. 2019 from road traffic crashes (particularly motorcyclist mortality), drug poisoning, and homicide by firearm. Similar patterns were not present from 2019 vs. 2018. There were morbidity reductions from road traffic crashes (particularly occupant and pedestrian morbidity from motor vehicle crashes), unintentional falls, and self-harm by suffocation from 2019 to 2020 compared to the previous period. Change patterns in sexes and age groups were generally similar, but exceptions were observed for some injury types.

Conclusions

The COVID-19 pandemic significantly changed specific injury burden in the United States. Some discrepancies also existed across sex and age groups, meriting attention of injury researchers and policymakers to tailor injury prevention strategies to particular populations and the environmental contexts citizens face.

Modeling the impact of the COVID-19 pandemic on speeding at rural roadway facilities in Maine using short-term speed and traffic count data

The COVID-19 pandemic caused a significant change in traffic operations and safety. For instance, various U.S. states reported an increase in the rate of fatal and severe injury crashes over this duration. In April and May of 2020, comprehensive stay-at-home orders were issued across the country, including in Maine. These orders resulted in drastic reductions in traffic volume. Additionally, there is anecdotal evidence that speed enforcement had been reduced during pandemic. Drivers responded to these changes by increasing their speed. More importantly, data show that speeding continues to occur, even one year after the onset of the pandemic. This study develops statistical models to quantify the impact of the pandemic on speeding in Maine. We developed models for three rural facility types (i.e., major collectors, minor arterials, and principal arterials) using a mixed effect Binomial regression model and short duration speed and traffic count data collected at continuous count stations in Maine. Our results show that the odds of speeding by more than 15 mph increased by 34% for rural major collectors, 32% for rural minor arterials, and 51% for rural principal arterials (non-Interstates) during the stay-at-home order in April and May of 2020 compared to the same months in 2019. In addition, the odds of speeding by more than 15 mph, in April and May of 2021, one year after the order, were still 27% higher on rural major collectors and 17% higher on rural principal arterials compared to the same months in 2019.

Trends in the number of patients from traffic accidents and the state of emergency

Aim

During the coronavirus disease 2019 pandemic, the number of traffic accidents and injured patients was reported to be lower than that before the pandemic. However, little is known regarding the relationship between periods of the state of emergency and the number of patients who met with traffic accidents.

Methods

The numbers of trauma patients and deaths due to traffic accidents in Tokyo and Osaka were collected monthly from the statistics published by the police department. A state of emergency was declared four times in both cities. The number of trauma patients and deaths was compared between the emergency and other periods.

Results

The number of monthly patients per 100,000 due to traffic accidents during the state of emergency was significantly lower than that during other periods in Tokyo (16.56 versus 18.20; P = 0.008) and Osaka (24.12 versus 28.79; P = 0.002). However, the monthly number of deaths during the state of emergency was not significantly different compared with those during the other periods in Tokyo (0.08 versus 0.08; P = 0.65) and Osaka (0.10 versus 0.14; P = 0.082). A decrease in the number of trauma patients was observed before the emergency period; however, the reduction rate dropped as the period passed.

Conclusion

There were significantly fewer trauma patients due to traffic accidents during the state of emergency than during the other periods, with no significant difference in the number of deaths.

Older Adults' concerns regarding Hurricane-Induced evacuations during COVID-19: Questionnaire findings

The COVID-19 pandemic has drastically affected our day-to-day life in the last few years. This problem becomes even more challenging when older adults are considered due to their less powerful immune system and vulnerability to infectious diseases, especially in Florida where 4.5 million people aged 65 and over reside. With its long coastline, large and rapidly growing of older adult population, and geographic diversity, Florida is also uniquely vulnerable to hurricanes, which significantly increases the associated risks of COVID-19 even further. This study investigates older adults' evacuation-related concerns during COVID-19 using statistical analysis of a questionnaire conducted among 389 older adult Florida residents. The questionnaire includes questions concerning demographic information and older adults' attitudes toward hurricane-induced evacuations during the COVID-19 pandemic. Ordered Probit regression models were developed to investigate the impacts of demographic parameters on older adults' tendencies toward evacuating as well as their preferences to stay at home or shelter during the pandemic. The model results reveal that male participants felt safer to evacuate compared to females. Also, any decrease in the level of income was associated with an increase in the need for help for evacuation by 18%. Findings indicated that the participants who found the evacuation safe normally also had a positive attitude toward staying in their vehicle, hotel, or even shelters if maintaining social distance was possible. Emergency management policies can utilize these findings to enhance hurricane preparations for dealing with the additional health risks posed by the pandemic for older adults, a situation that could be exacerbated by the upcoming hurricane season in Florida.

Safety analytics at a granular level using a Gaussian process modulated renewal model: A case study of the COVID-19 pandemic

With the advance of intelligent transportation system technologies, contributing factors to crashes can be obtained in real time. Analyzing these factors can be critical in improving traffic safety. Despite many crash models having been successfully developed for safety analytics, most models associate crash observations and contributing factors at the aggregate level, resulting in potential information loss. This study proposes an efficient Gaussian process modulated renewal process model for safety analytics that does not suffer from information loss due to data aggregations. The proposed model can infer crash intensities in the continuous-time dimension so that they can be better associated with contributing factors that change over time. Moreover, the model can infer non-homogeneous intensities by relaxing the independent and identically distributed (i.i.d.) exponential assumption of the crash intervals. To demonstrate the validity and advantages of this proposed model, an empirical study examining the impacts of the COVID-19 pandemic on traffic safety at six interstate highway sections is performed. The accuracy of our proposed renewal model is verified by comparing the areas under the curve (AUC) of the inferred crash intensity function with the actual crash counts. Residual box plot shows that our proposed models have lower biases and variances compared with Poisson and Negative binomial models. Counterfactual crash intensities are then predicted conditioned on exogenous variables at the crash time. Time-varying safety impacts such as bimodal, unimodal, and parabolic patterns are observed at the selected highways. The case study shows the proposed model enables safety analytics at a granular level and provides a more detailed insight into the time-varying safety risk in a changing environment.

Impacts of COVID-19 Travel Restriction Policies on the Traffic Quality of the National and Provincial Trunk Highway Network: A Case Study of Shaanxi Province

According to recent research, the COVID-19 pandemic has impacted road traffic quality. This study aims to analyze the impacts of COVID-19 travel restriction policies on the traffic quality of the national and provincial trunk highway network (NPTHN) in Shaanxi Province. We collected the traffic data of the NPTHN for three consecutive years (from 2019 to 2021), before and after the COVID-19 outbreak, including weekly average daily traffic, weekly traffic interruption times, weekly traffic control time, weekly traffic accidents, weekly traffic injuries, and weekly traffic deaths. Using descriptive statistics and dynamic analysis methods, we studied the safety and service levels of the NPTHN. We set up an assessment model of the NPTHN operational orderliness through dissipative structure theory and entropy theory to study the operational orderliness of the NPTHN. Results show that in 2020, the service level, safety level, and operational orderliness of the NPTHN dropped to the lowest levels. The pandemic was gradually brought under control, and the travel restriction policies were gradually reduced and lifted. The adverse impacts on the operational orderliness of the NPTHN decreased, but the operational orderliness did not yet recover to the pre-pandemic level. Meanwhile, the service and safety levels of the NPTHN did not recover. Taken together, the COVID-19 travel restriction policies had adverse impacts on the traffic quality of the NPTHN in Shaanxi Province.