ABSTRACT
Research in highway safety continues to struggle to address two potentially important issues;the role that unobserved factors may play on resulting crash and injury-severity likelihoods, and the issue of identification in safety modeling caused by the self-selective sampling inherent in commonly used safety data (the fact that drivers in observed crashes are not a random sample of the driving population, with riskier drivers being over-represented in crash data bases). This paper addresses unobserved heterogeneity using mixing distributions and attempts to provide insight into the potential sample-selection problem by considering data before and during the COVID-19 pandemic. Based on a survey of vehicle usage (vehicle miles traveled) and subsequent statistical modeling, there is evi-dence that riskier drivers likely made up a larger proportion of vehicle miles traveled dur-ing the pandemic than before, suggesting that the increase in injury severities observed during COVID-19 could potentially be due to the over-representation of riskier drivers in observed crash data. However, by exploring Florida crash data before and during the pan-demic (and focusing on crashes where risky behaviors were observed), the empirical anal-ysis of observed crash data suggests (using random parameters multinomial logit models of driver-injury severities with heterogeneity in means and variances) that the observed increase in injury severity during the COVID-19 pandemic (calendar year 2020) was likely due largely to fundamental changes in driver behavior and less to changes in the sample selectivity of observed crash data. The findings of this paper provide some initial guidance to future work that can begin to more rigorously explore and assess the role of selectivity and resulting identification issues that may be present when using observed crash data.(c) 2022 Elsevier Ltd. All rights reserved.
ABSTRACT
Factors associated with continued driving during shelter in place orders have been examined in a community sample of typically developing teen drivers, but not in teens diagnosed with Attention/Deficit-Hyperactivity Disorder (ADHD). Our objective was to examine psychosocial factors that predicted risky driving during shelter in place orders in teens with ADHD, which is important since teens with ADHD are at particular risk for poor driving outcomes. The present study is also novel in that it uses naturalistic data of risky driving rather than self-report of continued driving. Naturalistic in-car data from 56 ADHD participants (M age = 16.875 years, SD = 0.955;55.400 % were male) enrolled in an on-going study were used in the present study. Teens had an average of 26.915 months (SD = 14.343) of driving experience. Risky driving was defined as experiencing an event exceeding 0.600 g-force during the first month of COVID-19 pandemic shelter in place ordinances in Ohio, Kentucky, and Indiana, USA. A hierarchical logistic regression with a post-COVID driving event as the dependent variable was conducted. Baseline ratings of ADHD and oppositional defiant disorder/conduct disorder (ODD/CD) severity were entered in the first step of the model, while anxiety severity and parent behaviors regarding teen driving safety (monitoring and limit setting) were entered in the second step of the model. The first step of the model reached statistical significance (χ2(2, 54) = 7.577, p =.023), with only greater symptoms of ODD/CD significantly predicting a post-COVID driving event (B = 0.144, p =.020). With each point increase in ODD/CD symptoms, there was a 15.5 % increase in the probability of experiencing a high g-force event during COVID-19 restrictions. The model was no longer significant at step 2 when anxiety severity and parent behaviors were added to the model (χ2(3, 55) = 10.97p =.052). We conclude that ODD/CD symptom severity was the strongest predictor of risky driving during COVID-19 restrictions within a sample of teen drivers with ADHD. Study implications may be beneficial for clinicians who work with families of teens with ADHD;suggestions for strategies mitigating this risk are discussed. These findings also have implications for which teens with ADHD may be less positively impacted by other government mitigation strategies such as Graduated Drivers Licensing (GDL) regulations. © 2022
ABSTRACT
Research in highway safety continues to struggle to address two potentially important issues;the role that unobserved factors may play on resulting crash and injury-severity likelihoods, and the issue of identification in safety modeling caused by the self-selective sampling inherent in commonly used safety data (the fact that drivers in observed crashes are not a random sample of the driving population, with riskier drivers being over-represented in crash data bases). This paper addresses unobserved heterogeneity using mixing distributions and attempts to provide insight into the potential sample-selection problem by considering data before and during the COVID-19 pandemic. Based on a survey of vehicle usage (vehicle miles traveled) and subsequent statistical modeling, there is evidence that riskier drivers likely made up a larger proportion of vehicle miles traveled during the pandemic than before, suggesting that the increase in injury severities observed during COVID-19 could potentially be due to the over-representation of riskier drivers in observed crash data. However, by exploring Florida crash data before and during the pandemic (and focusing on crashes where risky behaviors were observed), the empirical analysis of observed crash data suggests (using random parameters multinomial logit models of driver-injury severities with heterogeneity in means and variances) that the observed increase in injury severity during the COVID-19 pandemic (calendar year 2020) was likely due largely to fundamental changes in driver behavior and less to changes in the sample selectivity of observed crash data. The findings of this paper provide some initial guidance to future work that can begin to more rigorously explore and assess the role of selectivity and resulting identification issues that may be present when using observed crash data.
ABSTRACT
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.
Subject(s)
Automobile Driving , COVID-19 , Accidents, Traffic , Humans , Latent Class Analysis , Pandemics , Risk FactorsABSTRACT
Young drivers are generally associated with risky driving behaviors that can lead to crash involvement. Many self-report measurement scales are used to assess such risky behaviors. This study is aimed to understand the risky driving behaviors of young adults in Qatar and how such behaviors are associated with crash involvement. This was achieved through the usage of validated self-report measurement scales adopted for the Arabic context. A nationwide cross-sectional and exploratory study was conducted in Qatar from January to April 2021. Due to the Covid-19 pandemic, the survey was conducted online. Therefore, respondents were selected conveniently. Hence, the study adopted a non-probability sampling method in which convenience and snowball sampling were used. A total of 253 completed questionnaires were received, of which 57.3% were female, and 42.7% were male. Approximately 55.8% of these young drivers were involved in traffic accidents after obtaining their driving license. On average, most young drivers do have some risky driving behavior accompanied by a low tendency to violate traffic laws, and their driving style is not significantly controlled by their personality on the road. The older young drivers are more involved in traffic accidents than the younger drivers, i.e., around 1.5 times more likely. Moreover, a young male driver is 3.2 times less likely to be involved in traffic accidents than a female driver. In addition, males are only 0.309 times as likely as females to be involved in an accident and have approximately a 70% lower likelihood of having an accident versus females. The analysis is complemented with the association between young drivers' demographic background and psychosocial-behavioral parameters (linking risky driving behavior, personality, and obligation effects on crash involvement). Some interventions are required to improve driving behavior, such as driving apps that are able to monitor and provide corrective feedback.