Estimating the over-involvement of suspended, revoked, and unlicensed drivers as at-fault drivers in California fatal crashes

The difference in quasi-induced exposure to crashes involving various hazardous driving actions

In quasi-induced exposure (QIE) theory, the presence of hazardous driving action is the typical determinant of the driver's responsibility for a crash. However, there is a lack of effort available to analyze the impacts of hazardous actions on the QIE estimate, which may result in estimation bias. Thus, the study aims to explore the difference in QIE to crashes involving various hazardous driving actions. Chi-square test is conducted to examine the consistency of non-responsible party distributions among the crashes involving various hazardous actions. Multinomial logit model and nested logit model are employed to identify the disparities of contributing factors to the actions. Results indicate that: 1) the estimated exposures appear to be inconsistent among the crashes with different hazardous actions, 2) driving cohorts have differential propensities of performing various hazardous actions, and 3) factors such as driver-vehicle characteristics, time, area, and environmental condition significantly affect the occurrence of hazardous actions while the directions and magnitude of the effects show great disparities for various actions. It can be concluded that the QIE estimates are significantly different for crashes involving various hazardous actions, which serves to highlight the importance of clarifying the specific hazardous actions for responsibility assignment in QIE theory.

Unlicensed driving among young drivers in North Carolina: a quasi-induced exposure analysis

BACKGROUND

Little is known about the prevalence of driving among teenagers who have not yet obtained a license. The primary objective of the present study was to estimate the prevalence of unlicensed driving among young drivers using the quasi-induced exposure (QIE) approach and to determine whether unlicensed driving was more common among minority and lower-income teenagers. Additionally, we examined whether unlicensed driving among adolescents increased following the implementation of a graduated driver licensing (GDL) system and whether GDL differentially affected minority and low-income adolescents.

METHODS

Using North Carolina crash and driver license data, we identified 90,267 two-vehicle crashes from 1991 through 2016 where only one driver was considered contributory and the non-contributory driver was a White or Black 16 or 17 years old. In the QIE approach, these non-contributory young drivers are assumed to be representative of all adolescents driving in the state during this time period. The prevalence of unlicensed driving among adolescents by age and year was estimated by identifying the proportion of non-contributory drivers who had never been licensed by the time of their involvement in these two-vehicle crashes. We further conducted logistic regression analyses to examine the likelihood of a non-contributory young driver being unlicensed as a function of race, neighborhood income level, and licensing era (prior to or after GDL was implemented).

RESULTS

During the 26 years for which data were available, the mean annual prevalence of unlicensed driving was 1.2% for 16-year-olds and 1.7% among 17-year-olds. Young Black drivers and individuals living in lower-income neighborhoods were somewhat more likely to drive before obtaining a license, but the rates of unlicensed driving among these groups were also quite low. Unlicensed driving increased slightly for 17-year-olds following the implementation of GDL, but returned to previous levels after a few years.

CONCLUSION

Unlicensed driving among adolescents in North Carolina is substantially less common than suggested by previous self-report studies and analyses of fatal crash data.

Does past offending behaviors catch up with you? A study examining the relationship between traffic offending history and fatal crash involvement

OBJECTIVE

The aim of the current study was to compare the traffic histories of drivers fatally injured in a road traffic crash, to alive drivers of the same age and gender in order to determine if key markers of increased fatality-risk could be identified.

METHODS

The case sample comprised 1,139 (82% male) deceased drivers, while the control sample consisted of 1,139 registered Queensland drivers (who were individually matched to the case sample on age and gender).

RESULTS

Using a logistic regression model, and adjusting for age and gender, it was found that a greater number of offenses predicted greater odds of fatal crash involvement, with each increase in offense frequency category increasing ones' odds by 1.98 (95% CI: 1.8, 2.18). When each offense type was considered individually, dangerous driving offenses were most influential, predicting a 3.44 (95% CI: 2, 5.93) increased odds of being in the case group, followed by the following offense types: learner/provisional (2.88, 95% CI: 1.75, 4.74), drink and drug driving (2.82, 95% CI: 1.97, 4.04), not wearing a seatbelt/helmet (2.63, 95% CI: 1.53, 4.51), licensing offenses (1.87, 95% CI: 1.41, 2.49), and speeding (1.48, 95% CI: 1.33, 1.66). In contrast, mobile phone and road rules offenses were not identified as significant predictors.

CONCLUSION

The findings indicate that engagement in a range of aberrant driving behaviors may result in an increased odds of future fatal crash involvement, which has multiple implications for the sanctioning and management of apprehended offenders.

Is driving without a valid license associated with an increased risk of causing a road crash?

The aim of this study was to estimate the association between each cause of driving without a valid license (DWVL) and the risk of causing a road crash, considering driver, vehicle and environmental factors. A case-control study based on data from the Spanish Register of Road Accidents with Victims was carried out between 2014 and 2017. Cases included 28,620 drivers of moving private cars, vans and off-road vehicles involved in single crashes plus 50,100 drivers deemed responsible for clean collisions (i.e. those in which only one driver was labeled as responsible). In accordance with the quasi-induce exposure approach, drivers not responsible for clean collisions comprised the control group (N = 51,656). Logistic and multinomial regression models were used to estimate crude and adjusted Odds Ratios or Relative Risk Ratios between each reason for DWVL and the risk of being a case of all, single and multi-vehicle collisions. A significant association was found between all reasons for DWVL and the risk of causing a road crash. This association was particularly high for drivers with a suspended license and drivers who had never obtained a license. In these subgroups of drivers, the proportion of the relationship explained by high-risk driving behaviors is high. Our results support the need for applying continued strategies to identify and control these subgroups of drivers.

Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework

The objective of this paper is to predict the future driving risk of crash-involved drivers in Kunshan, China. A systematic machine learning framework is proposed to deal with three critical technical issues: 1. defining driving risk; 2. developing risky driving factors; 3. developing a reliable and explicable machine learning model. High-risk (HR) and low-risk (LR) drivers were defined by five different scenarios. A number of features were extracted from seven-year crash/violation records. Drivers’ two-year prior crash/violation information was used to predict their driving risk in the subsequent two years. Using a one-year rolling time window, prediction models were developed for four consecutive time periods: 2013–2014, 2014–2015, 2015–2016, and 2016–2017. Four tree-based ensemble learning techniques were attempted, including random forest (RF), Adaboost with decision tree, gradient boosting decision tree (GBDT), and extreme gradient boosting decision tree (XGboost). A temporal transferability test and a follow-up study were applied to validate the trained models. The best scenario defining driving risk was multi-dimensional, encompassing crash recurrence, severity, and fault commitment. GBDT appeared to be the best model choice across all time periods, with an acceptable average precision (AP) of 0.68 on the most recent datasets (i.e., 2016–2017). Seven of nine top features were related to risky driving behaviors, which presented non-linear relationships with driving risk. Model transferability held within relatively short time intervals (1–2 years). Appropriate risk definition, complicated violation/crash features, and advanced machine learning techniques need to be considered for risk prediction task. The proposed machine learning approach is promising, so that safety interventions can be launched more effectively.

Pedestrian- and driver-related factors associated with the risk of causing collisions involving pedestrians in Spain

This study aimed to quantify the association between pedestrian- and driver-related factors and the risk of causing road crashes involving pedestrians in urban areas in Spain between 1993 and 2011. From the nationwide police-based registry of road crashes with victims in Spain, we analyzed all 63,205 pairs of pedestrians and drivers involved in crashes in urban areas in which only the pedestrian or only the driver was at fault. Logistic regression models were used to obtain adjusted odds ratios to assess the strength of association between each individual-related variable and the pedestrian's odds of being at fault for the crash (and conversely, the driver's odds of not being at fault). The subgroups of road users at high risk of causing a road crash with a pedestrian in urban areas were young and male pedestrians, pedestrians with psychophysical conditions or health problems, the youngest and the oldest drivers, and drivers with markers of high-risk behaviors (alcohol use, nonuse of safety devices, and driving without a valid license). These subgroups should be targeted by preventive strategies intended to decrease the rate of urban road crashes involving pedestrians in Spain.