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.

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.

Public health crisis and risky road behaviors.

This study investigates how exposure to riskier environments influences risky road behaviors, using the COVID-19 pandemic as a natural experiment. Utilizing administrative individual traffic violation records from Taipei, where neither mandatory lockdown nor mobility restrictions were imposed, we find that pandemic-induced risk decreased speeding violations and that the effect was transitory. However, no significant changes were observed concerning violations with a minimal risk of casualties, such as illegal parking. These findings suggest that experiencing a higher level of life-threatening risk discourages risky behaviors concerning human life but has little spillover effect on those concerning only financial costs.

One year of COVID-19: Impacts on safe driving behavior and policy recommendations.

INTRODUCTION: In the unprecedented year of 2020, the rapid spread of COVID-19 disrupted everyday activities worldwide, leading the majority of countries to impose lockdowns and confine citizens in order to minimize the exponential increase in cases and casualties. To date, very few studies have been concerned with the effect of the pandemic on driving behavior and road safety, and usually explore data from a limited time span. METHOD: This study presents a descriptive overview of several driving behavior indicators as well as road crash data in correlation with the strictness of response measures in Greece and the Kingdom of Saudi Arabia (KSA). A k-means clustering approach was also employed to detect meaningful patterns. RESULTS: Results indicated that during the lockdown periods, speeds were increased by up to 6%, while harsh events were increased by about 35% in the two countries, compared to the period after the confinement. However, the imposition of another lockdown did not cause radical changes in Greek driving behavior during the late months of 2020. Finally, the clustering algorithm identified a "baseline," a "restrictions," and a "lockdown" driving behavior cluster, and it was shown that harsh braking frequency was the most distinctive factor. POLICY RECOMMENDATIONS: Based on these findings, policymakers should focus on the reduction and enforcement of speed limits, especially within urban areas, as well as the incorporation of active travelers in the current transport infrastructure.

Factors associated with COVID-19 preventive behaviors among taxi drivers in Bangkok.

Objectives: This paper aimed to identify factors associated with COVID-19 preventive behaviors among taxi drivers in Bangkok. Methods: This cross-sectional study included 401 taxi drivers. Data were analyzed using descriptive statistics. The association between predisposing factors, enabling factors, and reinforcing factors with COVID-19 preventive behaviors was analyzed by using analysis of variance and Pearson's Product Moment Correlation. Multiple linear regression analysis was used to determine the influencing factors in predicting COVID-19 preventive behaviors of taxi drivers. Results: The present findings revealed that income adequacy, support from family, co-workers, and healthcare professionals, perceived susceptibility, severity, benefits, barriers, and health motivation, accessibility to personal protective equipment for COVID-19 and preventative measures against COVID-19 from other agencies were associated with good COVID-19 preventive behaviors among taxi-driver in Bangkok during COVID-19 pandemic (R 2 = 0.349, p = 0.008). The model could predict 34.9% of variance in COVID-19 preventative behavior among taxi drivers. Conclusion: Taxi drivers should be encouraged to engage in appropriate preventive behaviors against COVID-19, emphasizing the individual and organizational levels. There should be a policy by organizations to promote the implementation of COVID-19 safety control standards to ensure safe working conditions. In addition, appropriate welfare benefits should be provided for taxi drivers, such as loans, personal protective equipment, and access to health services to improve COVID-19 preventive behaviors.

Ironic effects of political ideology and increased risk-taking in Ohio drivers during COVID-19 shutdown.

In March 2020, Ohio, along with many other states, enacted a stay-at-home order (i.e., "shutdown") to limit the spread of COVID-19. As a result of lower traffic, crashes should also have declined. We investigated whether crash rates declined in Ohio during the stay-at-home order and explore possible predictors for the decrease, such as reduced travel in compliance with the order, along with speeding, alcohol, and drug use. In addition, we examined whether support for President Trump would relate to greater travel and greater crashes (particularly during the stay-at-home order, when greater travel indicated lower compliance). The overall rate of crashes fell as people stayed home, mainly due to a decline in minor crashes. In contrast, the rate of serious crashes did not fall. Instead, percentage of alcohol-related crashes increased during the stay-at-home order, and the reduction in travel was associated with greater speeding-related crashes. Because alcohol and speeding tend to increase crash severity, these two factors may explain why severe crash rates were not reduced by lower traffic. Instead, it appears that those drivers remaining on the roads during the shutdown may have been more prone to risky behaviors, evidenced by a greater percentage of alcohol-related crashes across the state during the shutdown and greater speed-related crashes in counties with less traffic. In addition, county-level support for President Trump indirectly predicted greater rates of crashes (of all types) via increased travel (i.e., lower compliance with the shutdown), even while controlling for county-level income, rurality, and Appalachian region. Importantly, this mediated effect was stronger during the weeks of the shutdown, when greater travel indicated lower compliance. Thus, lower compliance with the stay-at-home order and increased risky driving behaviors by remaining drivers may explain why lower traffic did not lead to lower serious crashes.

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.

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.

Driving Impairment Cases Involving Flualprazolam.

Flualprazolam is a novel psychoactive substance in the benzodiazepine class that is increasing in prevalence in the USA. This study describes 19 cases of drivers stopped for impaired driving where flualprazolam was detected. This represents ∼9% of the total cases submitted to the Sedgwick County Regional Forensic Science Center toxicology laboratory between July 2019 and May 2020. Blood concentrations of flualprazolam ranged from 4 to 69 ng/mL, with mean and median concentrations of 20.9 ng/mL and 15 ng/mL, respectively. The increased prevalence in which laboratories are detecting flualprazolam along with the low concentrations necessary for pharmacological effects illustrates the importance of laboratories to remain vigilant in testing for novel psychoactive substances.

Self-reported changes in aggressive driving within the past five years, and during COVID-19.

Aggressive driving is a significant road safety problem and is likely to get worse as the situations that provoke aggression become more prevalent in the road network (e.g. as traffic volumes and density increase and the grey fleet expands). In addition, driver frustration and stress, also recognised as triggers for aggression, are likely to stay high because of the COVID-19 pandemic and associated burdens, leading to increased aggression. However, although drivers report that other drivers are becoming more aggressive, self-report data suggests that the prevalence of aggression has not changed over time. This may be due to the methods used to define and measure aggression. This study sought to clarify whether self-reported aggression has increased over a five-year period and across three different types of aggression: verbal aggression, aggressive use of the vehicle and personal physical aggression. The influence of COVID-19 lockdowns on own and others' driving styles was also investigated. A total of 774 drivers (males = 66.5%, mean age = 48.7; SD = 13.9) who had been licensed for at least five years (M = 30.6, SD = 14.3), responded to an online survey and provided retrospective frequencies for their current aggression (considered pre-COVID-19 lockdowns) and five years prior. Two open ended questions were included to understand perceived changes in driving styles (own and others) during the COVID-19 pandemic. One third (33%) of drivers believed they were more aggressive now than five years ago but 61% of the sample believed other drivers were more aggressive now than five years ago. Logistic regression analyses on changes in self-reported aggression (same or decreased vs increased) showed the main factor associated with increases in aggressive driving was the perception that other drivers' aggression had increased. Further, almost half the sample (47%) reported that other drivers had become riskier and more dangerous during, and soon after, the COVID-19 lockdowns. These results show that the driving environment is seen as becoming more aggressive, both gradually and as a direct result of COVID-19 lockdowns. The data indicate that this perceived increase in aggression is likely to provoke higher levels of aggression in some drivers. Campaigns to reduce aggression on the roads need to focus on changing road culture and improving interactions, or perceived interactions, among road users.

Saving lives through road safety risk factor interventions: global and national estimates.

Global road mortality is a leading cause of death in many low-income and middle-income countries. Data to support priority setting under current resource constraints are urgently needed to achieve Sustainable Development Goal (SDG) 3.6. This Series paper estimates the potential number of lives saved if each country implemented interventions to address risk factors for road injuries. We did a systematic review of all available evidence-based, preventive interventions for mortality reduction that targeted the four main risk factors for road injuries (ie, speeding, drink driving, helmet use, and use of seatbelt or child restraint). We used literature review variables and considered three key country-level variables (gross domestic product per capita, population density, and government effectiveness) to generate country-specific estimates on the potential annual attributable number of lives that would be saved by interventions focusing on these four risk factors in 185 countries. Our results suggest that the implementation of evidence-based road safety interventions that target the four main road safety risk factors could prevent between 25% and 40% of all fatal road injuries worldwide. Interventions addressing speed could save about 347 258 lives globally per year, and at least 16 304 lives would be saved through drink driving interventions. The implementation of seatbelt interventions could save about 121 083 lives, and 51 698 lives could be saved by helmet interventions. We identify country-specific estimates of the potential number of lives saved that would be attributable to these interventions. Our results show the potential effectiveness of the implementation and scaling of these interventions. This paper presents key evidence for priority setting on road safety interventions and shows a path for reaching SDG 3.6.

Pandemic effects to autonomous vehicles test operations in California.

Companies developing automated driving system (ADS) technologies have spent heavily in recent years to conduct live testing of autonomous vehicles operating in real world environments to ensure their reliable and safe operations. However, the unexpected onset and ongoing resurgent effects of the Covid-19 pandemic starting in March 2020 has serve to halt, change, or delay the achievement of these new product development test objectives. This study draws on data obtained from the California automated vehicle test program to determine the extent that testing trends, test resumptions, and test environments have been affected by the pandemic. The importance of government policies to support and enable autonomous vehicles development during pandemic conditions is highlighted.

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.

Impact of the COVID-19 Pandemic on Older Adult Driving in the United States.

OBJECTIVES: To examine how the COVID-19 pandemic affected driving and health outcomes in older adults. METHODS: We compared Advancing Understanding of Transportation Options (AUTO) study participants enrolled before (December 2019 to March 2020) versus during the pandemic (May 2020 to June 2021). Participants were English-speaking, licensed drivers (≥70 years) who drove weekly and had a primary care provider at a study site and ≥1 medical condition potentially associated with driving cessation. We used baseline self-reported measures on mobility and health. RESULTS: Compared to those enrolled pre-COVID-19 (n = 61), more participants enrolled during COVID-19 (n = 240) reported driving reductions (26% vs. 70%, p < .001) and more often for personal preference (vs. medical/emotional reasons). While mean social isolation was higher during than pre-COVID-19, self-reported depression, stress, and overall health PROMIS scores did not differ significantly. DISCUSSION: Our findings highlight the resiliency of some older adults and have implications for mitigating the negative effects of driving cessation.

The role of schools in driving SARS-CoV-2 transmission: Not just an open-and-shut case.

Keeping schools open without permitting COVID-19 spread has been complicated by conflicting messages around the role of children and schools in fueling the pandemic. Here, we describe methodological limitations of research minimizing SARS-CoV-2 transmission in schools, and we review evidence for safely operating schools while reducing overall SARS-CoV-2 transmission.

Factors influencing changes in medication-taking and driving behavior after warnings about prescription medications that prohibit driving: an online survey.

BACKGROUND: This study examined warning messages as a strategy for preventing automobile crashes by drivers on medications. We investigated the degree of awareness regarding the effects of medication on automobile driving and changes in medication-taking and driving behavior. We also assessed associations between socio-environmental factors and the driving and medication-taking behavior adopted by individuals after being warned about driving-related risks. METHODS: Responses to an online questionnaire from 1200 people with a driving license who were taking prescription medications at the time of inquiry (March 2019) were collected and analyzed. The items surveyed were sex, age, educational history, health literacy, current medications, and medication-taking and driving behavior after being warned. RESULTS: Of the total respondents, 30% were taking medicine that prohibited driving. Of those taking prohibited medications, 25.7% did not receive a warning about driving from healthcare professionals. Most respondents taking prohibited medications received euphemistic warnings, such as "practice caution" (30%), "refrain from calling attention" (29.4%), and "avoid driving" (19.8%); 16% of the direct warnings were about not driving. Medication's effects on driving were recognized by 80% of the total respondents. The degree of awareness was significantly higher among respondents taking medications that prohibit driving than among those taking medications that did not prohibit driving or those taking unknown medications. Awareness of medicine's influence on driving was associated with health literacy. No association was found between age, gender, health literacy, history of side effects, and driving and medication-taking behavior. Approximately 22% of respondents adjusted their medication use at their discretion and 39% maintained treatment compliance but continued driving. Among respondents taking medications that prohibit driving, whether driving was required for work was a significant factor in their driving and medication-taking behavior after being warned. CONCLUSIONS: Healthcare professionals do not always fully inform patients about the driving-related risks of medications. To encourage patients who are taking medications that have a significant impact on their driving to either stop driving or consult a healthcare professional, healthcare professionals must first understand the patient's social environment, such as whether driving is required for work, and then create an environment conducive to advice-seeking.

"Getting There": Transportation as a Barrier to Research Participation Among Older Adults.

Recruiting from a large university registry of older adults who have consented to be contacted for research, the Getting There study (n = 500) asked about willingness to participate in research and obstacles posed by transportation. In the period before the pandemic (12/2019-3/2020), 88% of participants in the community registry were willing to travel to the research site. Driving and living closer to the research site, with better access to public transportation, were associated with significantly greater willingness to come to the medical center for research even after adjustment for age, difficulty getting in and out of a vehicle, and number of days leaving one's home each week. A qualitative inquiry drawing on a long-term care registry (n = 23) showed a similar role for transportation challenges. Findings suggest transportation challenges among older people are a major source of unwillingness to participate in research even among highly motivated people participating in research registries.

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

Risky driving behaviors such as speeding and failing to signal have been witnessed more frequently during the COVID-19 pandemic, resulting in higher rates of severe crashes. This study aims to investigate how the COVID-19 pandemic impacts the likelihood of severe crashes via changing driving behaviors. Multigroup structural equation modeling (SEM) is used to capture the complex interrelationships between crash injury severity, the context of COVID-19, driving behaviors, and other risk factors for two different groups, i.e., highways and non-highways. The SEM constructs two latent variables, namely aggressiveness and inattentiveness, which are indicated by risk driving behaviors such as speeding, drunk driving, and distraction. One great advantage of SEM is that the measurement of latent variables and interrelationship modeling can be achieved simultaneously in one statistical estimation procedure. Group differences between highways and non-highways are tested using different equality constraints and multigroup SEM with equal regressions can deliver the augmented performance. The smaller severity threshold for the highway group indicates that it is more likely that a crash could involve severe injuries on highways as compared to those on non-highways. Results suggest that aggressiveness and inattentiveness of drivers increased significantly after the outbreak of COVID-19, leading to a higher likelihood of severe crashes. Failing to account for the indirect effect of COVID-19 via changing driving behaviors, the conventional probit model suggests an insignificant impact of COVID-19 on crash severity. Findings of this study provide insights into the effect of changing driving behaviors on safety during disruptive events like COVID-19.

Use and Perceptions of Advanced Driver Assistance Systems by Older Drivers With and Without Age-Related Macular Degeneration.

Purpose: Advanced driver assistance systems (ADAS) have been reported to improve the safety of elderly and normally sighted drivers. The purpose of this study was to assess exposure to, perceived safety of, comfort level with, and interest in using ADAS among drivers with age-related macular degeneration (AMD). Methods: Current drivers aged 60+ years were recruited at four US sites to complete a survey about ADAS and driving habits. Frequency of use and/or perceptions of eight ADAS were investigated. An avoidance score was generated using questions about difficult driving situations. Results: The survey was completed by 166 participants (80 with AMD vs. 86 without). Participants with AMD had worse self-rated vision than those without (34% vs. 2% poor or fair rating), and drove fewer weekly miles (median [interquartile range [IQR] 30 [15 to 75] vs. 60 [30 to 121] miles, P = 0.002). Participants with AMD reported more avoidance of difficult driving situations (P < 0.001). There was no difference in the number of ADAS used by AMD status (median [IQR for AMD = 2.5 [1 to 5] vs. 3 [2 to 4] without, P = 0.87). Greater reported number of ADAS used was associated with less avoidance of difficult situations (P = 0.02). The majority perceived improved safety with most ADAS. Conclusions: Many drivers with AMD utilize common ADAS, which subjectively improve their road safety and may help to reduce self-imposed restrictions for difficult situations and mileage. Translational Relevance: Drivers with AMD are adopting readily available ADAS, for which they reported potential benefits, such as safety and less restrictive driving.