How did COVID-19 impact driving behaviors and crash Severity? A multigroup structural equation modeling

Modeling crash avoidance behaviors in vehicle-pedestrian near-miss scenarios: Curvilinear time-to-collision and Mamba-driven deep reinforcement learning

Interactions between vehicle-pedestrian at intersections often lead to safety-critical situations. This study aims to model the crash avoidance behaviors of vehicles during interactions with pedestrians in near-miss scenarios, contributing to the development of collision avoidance systems and safety-aware traffic simulations. Unmanned aerial vehicles were leveraged to collect high-resolution trajectory data of vehicle-pedestrian at urban intersections. A new surrogate safety measure, curvilinear time-to-collision (CurvTTC), was employed to identify vehicle-pedestrian near-miss scenarios. CurvTTC takes into account the curved trajectories of road users instead of assuming straight-line future trajectories, making it particularly suitable for safety analysis at intersections, where turning vehicles usually follow curved paths. An effective algorithm considering predicted trajectories and collision types was designed to compute CurvTTC. When CurvTTC was applied to capture vehicle-pedestrian conflicts at intersections, it demonstrated superior performance in identifying risks more accurately compared to other surrogate safety measures, emphasizing the importance of considering the curved trajectories of road users. Further, a novel deep deterministic policy gradient based on the Mamba network (Mamba-DDPG) approach was used to model vehicles' crash avoidance behaviors during the vehicle-pedestrian conflicts captured. Results revealed that the Mamba-DDPG approach effectively learned the vehicle behaviors sequentially in both lateral and longitudinal dimensions during near-miss scenarios with pedestrians. The Mamba-DDPG approach achieved superior predictive accuracy by utilizing Mamba's dynamic data reweighting, which prioritizes critical states. This resulted in better performance compared to both the standard DDPG and the Transformer-enhanced DDPG (Transformer-DDPG) methods. The Mamba-DDPG approach was employed to reconstruct evasive trajectories of vehicles when approaching pedestrians and its effectiveness in capturing the underlying policy of crash avoidance behaviors was validated.

Exploring behavior shifts and sample selectivity issues among speeding single-vehicle crash-injury severities before-and-after the stay-at-home order

This study systematically explores the cause of the increase in single-vehicle speeding crash injury severities in California during and after the stay-at-home order. 27,696 speeding crashes on both highways and non-highways before-and-after the order are selected from the California Highway Patrol system. Specific countermeasures and implications of heterogeneity in means and variances are analyzed based on marginal effects. Out-of-sample simulations are employed to address two fundamental causes of the rise in injury severities: a shift in driver behaviors and the overrepresentation of riskier drivers. Results indicate that a shift towards more aggressive driving behaviors is the main reason for the increments of injury severities on highways after the order. The overrepresentation of riskier drivers is identified as the main cause during the order (both roadways) and on non-highways after the order. Since the predicted proportions on non-highway models before and during the order are closer compared to highways, this further suggests that local drivers are more inclined to violate the restriction and travel within neighborhoods during the order, which could contribute to the selectivity of riskier drivers. The findings of behavior shifts and sample selectivity issues provide valuable insights for future stay-at-home order practice, restriction improvement, and complementary policy development.

Driving under viral impairment: Linking acute SARS-CoV-2 infections to elevated car crash risks

This study explores the linkage between acute SARS-CoV-2 and car crashes across U.S. states, correlating with COVID-19 mitigation strategies, vaccination rates, and Long COVID prevalence. This investigation analyzed aggregate COVID-19 and car crash data spanning 2020-2023, with data collection occurring between March and May 2024. Analysis was done via a Poisson regression model, adjusted for population. Key variables included vaccination status, month-specific effects relating to initial pandemic shutdowns, and Long COVID rates. Results demonstrated a significant association between acute COVID-19 infections and an increase in car crashes, independent of Long COVID status to the tune of an OR of 1.25 [1.23-1.26]. This association was observed despite varying mitigation efforts and vaccination rates across states. The study found no protective effect of vaccination against car crashes, challenging prior assumptions about the benefits of vaccination. Notably, the risk associated with COVID-19 was found to be analogous to driving impairments seen with alcohol consumption at legal limits. Findings suggest significant implications for public health policies, especially in assessing the readiness of individuals recovering from COVID-19 to engage in high-risk activities such as pilots or nuclear plant employees. Further research is necessary to establish causation and explore the exact effects of COVID-19 within the CNS affecting cognition and behavior.

Kazakhstani Drivers and Substance Abuse During COVID-19: A Study of Patterns and Disaster Readiness

Background/Objectives: The COVID-19 pandemic has significantly affected public health and social behavior, contributing to increased psychoactive substance (PAS) use due to social isolation, economic stress, and uncertainty. This study aims to assess the impact of the pandemic on alcohol, cannabinoid, and opioid consumption among drivers involved in road traffic accidents (RTAs) in Kazakhstan. Understanding these patterns is essential for improving public health policies and road safety measures during crises. Methods: This retrospective cross-sectional study analyzed medical records from the Digital System of Medical Examination, a national database of drivers involved in traffic accidents in Kazakhstan. This study included 157,490 anonymized records from 1 January 2019, to 31 December 2020, categorizing cases into pre-COVID-19 and COVID-19 groups on the basis of the first nationwide lockdown on 16 March 2020. Statistical analyses, including prevalence rates and relative changes, were conducted via SPSS 20, while spatial distributions were visualized via QGIS software. Results: An analysis of all the records revealed a 12.9% decline in traffic accidents during the pandemic, with male drivers predominating during both periods. The mean age of the drivers in the compared groups was 36. Alcohol and cannabinoid use significantly increased during the COVID-19 period by 3.71% and 11.51%, respectively. In contrast, opioid use declined by 10.00%, but the difference was not statistically significant. The greatest increase in positive alcohol tests among drivers was observed in the Atyrau (94.80%), Pavlodar (35.43%), and North Kazakhstan (31.02%) regions, and Atyrau also presented the greatest increase in cannabinoid-positive cases. Conclusions: The results indicate that the COVID-19 pandemic and related lockdown measures have affected PAS consumption patterns among drivers. These findings are crucial for informing policies and developing strategies to improve road safety during future public health emergencies.

Partially temporally constrained modeling of speeding crash-injury severities on freeways and non-freeways before, during, and after the stay-at-home order

Speeding crashes remain high injury severities after the stay-at-home order in California, highlighting a need for further investigation into the fundamental cause of this increment. To systematically explore the temporal impacts of the stay-at-home order on speeding behaviors and the corresponding crash-injury outcomes, this study utilizes California-reported single-vehicle speeding crashes on freeways (access-controlled) and non-freeways (non-access-controlled) before, during, and after the order. Significant injury factors and in-depth heterogeneity across observations are identified by random parameter logit models with heterogeneity in means and variances. Without segmenting the data by periods, the partially temporally constrained approach is employed to statistically determine varying and stabilized parameters over time through the whole dataset. Different likelihood ratio tests reveal significant temporal instabilities and stabilities of factors between two roadways and three periods. The potential impacts of observation selection issues on the marginal effect calculations of the partially constrained models are also systematically investigated. Significant variations in the probability of severe injury rate per week after the order are also found based on the Mann-Whitney U tests. The hysteretic effects of the order on the crash frequency and severity are observed on both freeways and non-freeways. Overall, seven variables are found to have stable effects, while fifteen variables exhibit unstable effects over time. Significant temporal variations in driver behaviors, including driving under the influence, cell phone usage, hit-and-run, failure to use seat belt, entering or leaving the ramp, and reaction to previous collisions, are observed before, during, or after the order. Specific countermeasures and effects of heterogeneity in means and variances are also discussed. These findings provide insights into understanding the temporal impacts of the stay-at-home order on injury severities, which are valuable to decision-makers and researchers for future order practice, restriction improvement, and complementary policy development.

Temporal shifts in safety states through the COVID-19 pandemic: Insights from hidden semi-Markov models

The COVID-19 pandemic significantly impacted transportation safety, with an increase in risky driving behaviors observed during the initial lockdown period, leading to a higher likelihood of severe crashes. However, there is limited research on the post-pandemic effects on driving behaviors and safety. This study addresses this gap by analyzing open data from the state of Virginia to examine shifts in safety states from 2016 to 2024, covering the pre-, during-, and post-pandemic periods. Structural equation modeling (SEM) was utilized to measure latent variables representing aggressive and inattentive driving behaviors and to model their impacts on crash severity. Additionally, hidden semi-Markov models (HSMMs) were applied to infer shifts in safety states associated with these risky driving behaviors and the proportion of severe crashes. The strength of HSMM models lies in the ability to distinguish meaningful pattern changes from random noise. Compared with hidden Markov models (HMMs), HSMMs provide greater flexibility by accommodating arbitrary state duration distributions, contributing to better model performance and more reliable inferences. The HSMMs with four hidden states were utilized to reveal shifts in safety states over the eight-year analysis period in Virginia. Results suggested that safety states related to risky driving behaviors and the proportion of severe crashes were at lower-risk levels pre-pandemic from 2016 to 2019, then escalated to the highest-risk levels during the pandemic in 2020 and remained at higher-risk levels in 2021, 2022 and 2023. By 2024, safety states have returned to lower-risk levels similar to those inferred in the pre-pandemic period. A seasonal pattern was also identified in safety states, with lower-or-lowest-risk levels occurring in winter near the holiday season.

Mechanisms of Injury for Traumatic Brain Injury Among U.S. Military Service Members Before and During the COVID-19 Pandemic

OBJECTIVE

To understand the mechanisms of injury and demographic risk factors associated with traumatic brain injury (TBI) patients among active and reserve service members in the U.S. Military before and during the COVID-10 pandemic.

METHODS

Active and reserve service members diagnosed with an incident TBI from January 2019 through September 2021 were selected. Traumatic brain injury patients diagnosed before March 1, 2020 were categorized as pre-COVID (PC), and patients diagnosed on or after March 1, 2020 were categorized as the intra-COVID (IC) group, aligning closely with the date when the World Health Organization officially proclaimed the pandemic. We determined the frequency of causes of injuries associated with TBI separate by sex, age, occupation, and TBI severity. In addition, we conducted multivariate logistic regression analyses to assess the demographic risk factors associated with TBI severity during the PC and IC eras.

RESULTS

Our cohort included 48,562 TBI patients: 22,819 (47.0%) diagnosed during the PC era and 25,743 (53.0%) diagnosed during the IC era. The major mechanisms of injury within our TBI cohort were being struck by/against objects, falls/slips/trips, and motor vehicle traffic accidents before and during the pandemic. The most common causes of TBI were not impacted by COVID, but motor vehicle accidents did increase during the IC era. The mechanisms of injury associated with TBI differed by TBI severity: being struck by or against an object caused more mild and moderate TBI; motor vehicle accidents caused more severe TBI; and firearms was a major cause of penetrating TBI. In addition, the percentage of severe TBI because of firearms rose sharply during the IC era. Further, women were more likely to be diagnosed with mild TBI compared to men.

CONCLUSION

Military leaders should consider how different causes of injury are associated with differing TBI severities and how certain demographic groups were vulnerable to specific TBI severities when developing injury prevention programs.

Contrasting Summertime Trends in Vehicle Combustion Efficiency in Los Angeles, CA and Salt Lake City, UT

Policy interventions and technological advances are mitigating emissions of air pollutants from motor vehicles. As a result, vehicle fleets are expected to progressively combust fuel more efficiently, with a declining ratio of carbon monoxide to carbon dioxide (CO/CO2) in their emissions. We assess trends in traffic combustion efficiency in Los Angeles (LA) and Salt Lake City (SLC) by measuring changes in summertime on-road CO/CO2 between 2013 and 2021 using mobile observations. Our data show a reduction in CO/CO2 in LA, indicating an improvement in combustion efficiency that likely resulted from stringent regulation of CO emissions. In contrast, we observed an increase in CO/CO2 values in SLC. While slower progress in SLC compared to LA may be partially due to a later adoption of vehicle emission regulations in Utah compared to California, differing driving conditions and fleet composition may also be playing a role. This is evidenced by increased CO/CO2 in LA during the COVID-19 pandemic, which led to faster driving speeds and changes to the fleet composition. Our results demonstrate the success of California's CO-reducing policy interventions and illustrate the impacts of traffic characteristics on vehicle combustion efficiency and air pollutant emissions.

COVID-19 and speeding: Results of population-based survey of ontario drivers

INTRODUCTION

During COVID-19, increased speeding was observed in many jurisdictions. Yet, evidence is limited on what factors predicted increased speeding during the pandemic. This study's purpose was to examine speeding, and person and situation factors associated with increased speeding since the start of the pandemic.

METHODS

An online panel survey sampled 1,595 drivers using sex, age, and region quota sampling and weighting to approximate the Ontario, Canada adult population. Measures included: (1) person factors: socio-demographics (age, sex, region); psychological trait of risk propensity (Competitive Attitudes Toward Driving Scale (CATDS)); psychological states (distress - general and COVID-19-related); and behaviors (kilometers driven, alcohol use, police stops and collisions); and (2) COVID-19-related situation factors: perceived changes in (traffic volume, police enforcement).

RESULTS

67.2% of respondents reported speeding; 7.2% reported increased speeding since the start of the pandemic. Bivariate analyses indicated that person factors of younger age, male sex, higher CATDS, higher distress, more alcohol use, more kilometers traveled, police stops, and collisions since the start of the pandemic were associated with increased speeding. Situation factor of perceived less traffic volume since the start of the pandemic was associated with increased speeding. Logistic regression analysis identified odds of reported increased speeding during the pandemic was significantly higher for drivers with higher scores on the CATDS, higher kilometers traveled, and more alcohol use during the pandemic.

CONCLUSIONS

These findings suggest that higher risk propensity as well as the more kilometers driven and increased alcohol consumption were risk factors for increased speeding.

PRACTICAL APPLICATIONS

COVID-19-related factors of lower traffic volume and enforcement are less predictive of increased speeding than driver personality and pandemic-related behaviors of more driving and drinking. Interventions to reduce speeding still need to focus on these person factors through education, enforcement, and strong sanctions for speeding.

A hybrid Machine learning and statistical modeling approach for analyzing the crash severity of mobility scooter users considering temporal instability

One of the main objectives in improving the quality of life for individuals with disabilities, especially those experiencing mobility issues such as the elderly, is to enhance their day-to-day mobility. Enabling easy mobility contributes to their independence and access to better healthcare, leading to improvements in both physical and mental well-being. Mobility Scooters have become increasingly popular in recent years as a means of facilitating mobility, yet traffic safety issues such as crash severity have not been adequately investigated in the literature. This study addresses this knowledge gap by employing a hybrid method that combines a machine learning approach using the eXtreme Gradient Boosting (XGBoost) algorithm with Shapley Additive exPlanations (SHAP) and an advanced statistical model called Random Parameters Binary Logit accounting for heterogeneity in means and variances. Analyzing the United Kingdom mobility scooter crash data from 2018 to 2022, the study examined temporal instability using a likelihood ratio test. The results revealed that there was instability over the three distinct periods of time based on the coronavirus (COVID) pandemic, namely, pre-COVID, during COVID, and post-COVID. Moreover, the results revealed that mobility scooter crashes occurring at a give-way or uncontrolled junctions has a random effect on the severity, while factors such as mobility scooter riders aged over 80, rear-end and sideswipe crashes, and crashes during winter months increase the risk of severe injuries. Conversely, mobility scooter riders involved in crashes while riding on the footway are less likely to experience severe injuries. These findings offer valuable insights for enhancing road safety measures that can be utilized to effectively reduce the crash severity of mobility scooter riders.

Modeling the influence of connected vehicles on driving behaviors and safety outcomes in highway crash scenarios across varied weather conditions: A multigroup structural equation modeling analysis using a driving simulator experiment

Equipped with advanced sensors and capable of relaying safety messages to drivers, connected vehicles (CVs) hold the potential to reduce crashes. The goal of this study is to assess the impacts of CV technologies on driving behaviors and safety outcomes in highway crash scenarios under diverse weather conditions, including clear and foggy weather. A driving simulator experiment was conducted and the multigroup structural equation modeling (SEM) was employed to explore the complex interrelationships between the propensity of traffic conflicts, utilization of CV alerts, weather, psychological factors, driving behaviors, and other relevant variables for two different crash locations, namely a straight section and a horizontal curve. Two latent psychological factors including aggressiveness and unawareness were constructed from driving behavior as vehicles passed by crash scenes such as brake, throttle, steering angle, lane offset, and yaw. The SEM can measure latent psychological factors and model interrelationships concurrently through a single statistical estimation procedure. Results of the multigroup SEM showed that CV alerts could significantly reduce the unawareness on a horizontal curve and thus lower the propensity of traffic conflicts. Additionally, the overall effect of foggy weather on conflicts was found to be positive on a horizontal curve, despite the potential benefit of improving situational awareness. In contrast, the single group SEM failed to reveal any significant interrelationships in its structural model by pooling data from both crash locations. The obtained insights can guide the development of driving assistance systems, highlighting the necessity of customization considering weather conditions and location-specific factors.

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.

Are ride-hailing services safer than taxis? A multivariate spatial approach with accommodation of exposure uncertainty

Despite many research efforts on ride-hailing services and taxis, limited studies have compared the safety performance of the two modes. A major challenge is the need for reliable mode-specific exposure data to model their safety outcomes. Moreover, crash frequencies of the two modes by injury severities tend to be spatially and inherently correlated. To fully address these issues, this study proposes a novel multivariate conditional autoregressive model considering measurement errors in mode-specific exposures (MVCARME). More specially, a classical measurement error structure accommodates the uncertainty of estimated mode-specific exposures, and a multivariate spatial specification is adopted to capture potential spatial and inherent correlations. The model estimation is accelerated by an integrated nest Laplace approximation method. The census tracts in the city of Chicago are set as the spatial analysis unit. The mode-specific exposures (vehicle-mile-traveled) in each census tract are estimated by trip assignments using ride-hailing and taxi trip data in 2019. The modeling results indicate that both ride-hailing crashes and taxi crashes are positively associated with transportation factors (e.g., vehicle-mile-traveled, mode-specific vehicle-mile-traveled, and traffic signal numbers), land use factors (i.e., number of educational and alcohol-related sites), and demographic factors (e.g., median household income, transit ratio, and walk ratio). By comparison, the proposed model outperforms the others (i.e., negative binomial models and multivariate conditional autoregressive model) by yielding the lowest deviance information criterion (DIC), Watanabe-Akaike information criterion (WAIC), mean absolute error (MAE), and root-mean-square error (RMSE). According to the results of t-tests, ride-hailing services are found to be prone to a higher risk of minor injury crashes compared with taxis, despite no significant difference between the risks of severe injury crashes. Methodologically, this study adds a robust safety evaluation approach for comparing crash risks of different modes to the literature. At the same time, practically, it provides researchers, practitioners, and policy-makers insights into the safety management of various mobility alternatives.

Exploring weather-related factors affecting the delay caused by traffic incidents: Mitigating the negative effect of traffic incidents

BACKGROUND

Existing studies mainly focus on the relationship between real-time weather and traffic crash injury severity, while few scholars have investigated the operation risk levels caused by traffic incidents. Identifying weather-related factors that affect the incident-induced delay is helpful for estimating the delay levels when an incident occurs. Accordingly, the present study profoundly explores the relationship between weather conditions and traffic delays caused by traffic incidents.

METHODS

The traffic incident and weather datasets from January 1 to December 31, 2020, in New York State are used. To that end, the hazard-based duration and multinomial logit modeling frameworks are employed to determine the effect of weather conditions on the duration of traffic delay and the delay severity, respectively. More importantly, to account for multiple layers of unobserved heterogeneity, a random parameter with heterogeneity in means approach is introduced into the above two models.

RESULTS

(1) The strong breeze (wind speed over 8 m/s) and low visibility (visibility under 5 km) significantly affect the duration of delay. (2) Hot day (between 20 and 30 °C) has a 344.03 % greater probability of minor delay. A strong breeze has a higher probability of severe delay. The low visibility is found to increase the estimated odds of moderate delay and severe delay by 51.15 % and 13.39 %, respectively. In comparison, the normal visibility (between 10 and 20 km) significantly decreases the estimated odds of severe delay by 119.17 %.

CONCLUSIONS

Compared with other weather factors, wind speed, temperature, and visibility have the greatest impact on the traffic delay levels after a traffic accident, and there are significant differences in the impact under different delay severity. Findings from this study will help policymakers to establish comprehensive differentiating security measures to resolve traffic delays.

Trends in alcohol-impaired crashes in California, 2016 to 2021: A time series analysis for alcohol involvement and crash distribution among demographic subgroups

BACKGROUND

In 2020, the COVID-19 pandemic and control measures changed alcohol consumption in the United States (US) and globally. Before the pandemic, alcohol-impaired crashes contributed to approximately one-third of all road traffic crash injuries and fatalities nationally. We examined the impact of the COVID-19 pandemic on crashes and examined differences in alcohol-involved crashes across various subgroups.

METHODS

The University of California Berkeley Transportation Injury Mapping Systems provided information on all crashes reported to the California Highway Patrol from January 1, 2016 through December 31, 2021. Using autoregressive integrated moving average (ARIMA) models applied to weekly time series data, we estimated the effect of California's first mandatory statewide shelter-in-place order (March 19, 2020) on crashes per 100,000 population. We also examined crash subgroups according to crash severity, sex, race/ethnicity, age, and alcohol involvement.

RESULTS

In California, the mean crash rate per week before the pandemic (January 1, 2016-March 18, 2020) was 9.5 crashes per 100,000 population, and 10.3% of those were alcohol-involved. After the initiation of the COVID-19 stay-at-home order, the percentage of crashes that were alcohol-involved rose to 12.7%. Overall, the crash rate across California decreased significantly (-4.6 crashes per 100,000; 95% CI: -5.3, -3.9), including across all examined subgroups, with the greatest decrease among the least severe crashes. However, there was a 2.3% absolute increase in the proportion of crashes that were alcohol-involved (0.02 crashes per 100,000; 95% CI: 0.02, 0.03).

CONCLUSIONS

The initiation of a COVID-19 stay-at-home ordinance in California was associated with a substantial decrease in overall crash rates. While crashes have returned to pre-pandemic levels, alcohol-involved crashes remain elevated. The initiation of the stay-at-home order significantly increased alcohol-impaired driving, which has remained elevated.

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.

Biomimetic method of emergency life channel urban planning in Wuhan using slime mold networks

This study investigated a bio-inspired approach to planning optimal routes for urban hospital life channels to enable better responses to urban public security incidents. An experimental slime mold network and an origin-destination (OD) network model in which the nodes were tertiary hospitals in Wuhan were constructed. Correlation metrics of the two network models were used for network analysis and visualization. The experimental results showed that the slime mold network was better than the OD network in terms of global optimization. Furthermore, significant polarization of the influence value of urban hospital nodes resulted in a power-law distribution. This paper presents an urban planning method in which the biological mechanism of slime mold foraging is applied to construct shortest path networks in an emergency life channels. The results can be used to examine the relationship between urban roads and hospital nodes and the rational of global optimization distribution when planning the locations of new hospitals. A set of replicable and sustainable methods for conducting a biomimetic slime mold experiment to model real environments are presented. This approach provides a novel perspective for modeling emergency life channels.

Impact of COVID-19 on traffic safety from the "Lockdown" to the "New Normal": A case study of Utah

During the past several years, the COVID-19 pandemic has had pronounced impacts on traffic safety. Existing studies found that the crash frequency was reduced and the severity level was increased during the earlier "Lockdown" period. However, there is a lack of studies investigating its impacts on traffic safety during the later stage of the pandemic. To bridge such a gap, this study selects Salt Lake County, Utah as the study area and employs statistical methods to investigate whether the impact of COVID-19 on traffic safety differs among different stages. Negative binomial models and binary logit models were utilized to study the effects of the pandemic on the crash frequency and severity respectively while accounting for the exposure, environmental, and human factors. Welch's t-test and Pairwise t-test are employed to investigate the possible indirect effect of the pandemic by influencing other non-pandemic-related factors in the statistical models. The results show that the crash frequency is significantly less than that of the pre-pandemic during the whole course of the pandemic. However, it significantly increases during the later stage due to the relaxed restrictions. Crash severity levels were increased during the earlier pandemic due to the increased traffic speed, the prevalence of DUI, reduced use of seat belts, and increased presence of commercial vehicles. It reduced to a level comparable to the pre-pandemic later, owing to the reduction of speed and increased seat-belt-used to the pre-pandemic level. As for the incoming "New Normal" stage, stakeholders may need to take actions to deter DUI and reduce commercial-vehicle-related crashes to improve traffic safety.

Leveraging probe data to model speeding on urban limited access highway segments: Examining the impact of operational performance, roadway characteristics, and COVID-19 pandemic

Stay-at-home orders - imposed to prevent the spread of COVID-19 - drastically changed the way highways operate. Despite lower traffic volumes during these times, the rate of fatal and serious injury crashes increased significantly across the United States due to increased speeding on roads with less traffic congestion and lower levels of speed enforcement. This paper uses a mixed effect binomial regression model to investigate the impact of stay-at-home orders on odds of speeding on urban limited access highway segments in Maine and Connecticut. This paper also establishes a link between traffic density and the odds of speeding. For this purpose, hourly speed and volume probe data were collected on limited access highway segments for the U.S. states of Maine and Connecticut to estimate the traffic density. The traffic density was then combined with the roadway geometric characteristics, speed limit, as well as dummy variables denoting the time of the week, time of the day, COVID-19 phases (before, during and after stay-at-home order), and the interactions between them. Density, represented in the model as Level of Service, was found to be associated with the odds of speeding, with better levels of service such as A, or B (low density) resulting in the higher odds that drivers would speed. We also found that narrower shoulder width could result in lower odds of speeding. Furthermore, we found that during the stay-at-home order, the odds of speeding by more than 10, 15, and 20 mph increased respectively by 54%, 71% and 85% in Connecticut, and by 15%, 36%, and 65% in Maine during evening peak hours. Additionally, one year after the onset of the pandemic, during evening peak hours, the odds of speeding greater than 10, 15, and 20 mph were still 35%, 29%, and 19% greater in Connecticut and 35% 35% and 20% greater in Maine compared to before pandemic.

Forecasting time trend of road traffic crashes in Iran using the macro-scale traffic flow characteristics

Background

The serial correlation in the time series datasets should be considered to prevent biased estimates for coefficients. Nonetheless, the current models almost cannot explicitly handle autocorrelation and seasonality, and they focus mainly on the discrete nature of data. Nonetheless, the crash time series follows a normal distribution at the macro-scale. Moreover, the influential exogenous variables have been overlooked in Iran, employing univariate models. There are also contradictory results in the literature regarding the effect of average speed on crash frequency.

Objective

This study is aimed to evaluate the distinct impacts of mean speed on total and fatal accident time series at the national level. Besides, the SARIMAX modeling framework is introduced as a robust multivariate method for short-term crash frequency prediction.

Method

To this end, monthly total and fatal crash counts were aggregated for all rural highways in Iran. Besides, the time trends of traffic exposure, and average speed recorded by loop detectors, were aggregated at the same level as covariates. The Box-Jenkins methodology was employed for time series analysis.

Results

The results illustrated that the seasonal autoregressive integrated moving average with explanatory variable (SARIMAX) model outperformed the univariate ARIMA and SARIMA models. Also, SARIMA was more appropriate than the simple ARIMA when seasonality existed in the time series. Besides, the average speed had a negative linear association with the total crashes. In contrast, it revealed an increasing effect on fatal crashes.

Conclusion

Average speed has a dissimilar effect on the different traffic crash severities. Besides, the seasonal nature of data and the dynamic effects of the influential underlying factors should be considered to prevent underfitting issues and to predict future time trends accurately.

Applications

The developed instruments could be employed by policymakers to evaluate the intervention's effectiveness and to forecast the future time trends of accidents in Iran.

Investigating the Effect of COVID-19 on Driver Behavior and Road Safety: A Naturalistic Driving Study in Malaysia

The spread of the novel coronavirus COVID-19 resulted in unprecedented worldwide countermeasures such as lockdowns and suspensions of all retail, recreational, and religious activities for the majority of 2020. Nonetheless, no adequate scientific data have been provided thus far about the impact of COVID-19 on driving behavior and road safety, especially in Malaysia. This study examined the effect of COVID-19 on driving behavior using naturalistic driving data. This was accomplished by comparing the driving behaviors of the same drivers in three periods: before COVID-19 lockdown, during COVID-19 lockdown, and after COVID-19 lockdown. Thirty people were previously recruited in 2019 to drive an instrumental vehicle on a 25 km route while recording their driving data such as speed, acceleration, deceleration, distance to vehicle ahead, and steering. The data acquisition system incorporated various sensors such as an OBDII reader, a lidar, two ultrasonic sensors, an IMU, and a GPS. The same individuals were contacted again in 2020 to drive the same vehicle on the same route in order to capture their driving behavior during the COVID-19 lockdown. Participants were approached once again in 2022 to repeat the procedure in order to capture their driving behavior after the COVID-19 lockdown. Such valuable and trustworthy data enable the assessment of changes in driving behavior throughout the three time periods. Results showed that drivers committed more violations during the COVID-19 lockdown, with young drivers in particular being most affected by the traffic restrictions, driving significantly faster and performing more aggressive steering behaviors during the COVID-19 lockdown than any other time. Furthermore, the locations where the most speeding offenses were committed are highlighted in order to provide lawmakers with guidance on how to improve traffic safety in those areas, in addition to various recommendations on how to manage traffic during future lockdowns.