Quasi-induced Exposure Research Articles

Analyzing the factors influencing speeding behavior based on quasi-induced exposure and random parameter logit model with heterogeneity in means

Introduction: Speeding behavior is a major threat to road traffic safety, which can increase crash risks and result in severe injury outcomes. Although several studies have been conducted to analyze speeding crashes and relevant influential factors, the heterogeneity of variables has not been fully explored. Based on the traffic crash data extracted from the Crash Report Sampling System, the study aims to identify the factors that influence speeding driving with the consideration of variable heterogeneity. Method: Quasi-induced exposure technique is adopted to identify the disparities in the propensities of speeding for various driving cohorts. The random parameter logit model with heterogeneity in means is employed to examine the factors impacting speeding behavior. Results: Results indicate that: (a) driving cohorts such as young drivers, male drivers, passenger cars, and pickups appear to have higher propensities of engaging in speeding driving; (b) the propensity of speeding is higher when the driver is drinking, distracted, changing lanes, negotiating a curve, driving in lighted condition, and on curved roads; and (c) the random parameter logit model with heterogeneity in means has better performance as opposed to that without heterogeneity in means. Conclusions: Speeding behavior can be influenced by various factors in terms of driver-vehicle characteristics, physical condition, driving actions, and environmental conditions. Practical Applications: The findings could serve to develop effective countermeasures to reduce speeding behavior and improve traffic safety.

Looking back in the rearview: Insights into Queensland’s rear-end crashes

Objective Rear-end crashes cost the most out of any crash type to the Compulsory Third Party insurance scheme of Queensland. Rear-end crashes are also one of the most common types of crashes, peaking as the most common type of crash in Queensland in 2019. This study investigated the environmental, driver, and vehicle characteristics associated with rear-end collisions in Queensland, Australia. Methods A totle of 367,230 crashes in the Queensland crash database between 2001 and 2021 were used to identify crash trends in Queensland, particularly related to rear-end crashes. For crashes between 2015 and 2021, additional information was gathered and allowed further analysis of factors contributing to rear-end crashes using the quasi-induced exposure method. Two binary classification tree models were used. Model 1 investigated the environmental characteristics that predicted a crash being a rear-end or not. Model 2 investigated the controller and vehicle characteristics that predict a rear-end crash-involved vehicle to be either the striking (at-fault) or struck (not-at-fault) vehicle. Results Model 1 indicated that rear-end crashes were overrepresented in urban areas and major cities and where speed limits exceeded 50 km/h. Model 2 indicated that occupants of struck vehicles (not at-fault) were more likely to be severely injured than the striking (at-fault) vehicles in rear-end crashes. License type and vehicle type also influenced the frequency and severity of rear-end crashes. Conclusion Rear-end crashes can be unjust in that the not-at-fault party is typically more severely injured. Results from the current study suggest that keeping speed limits no higher than 50 km/h could help reduce the severity of rear-end crashes. Increased enforcement of safe following distances could also assist in preventing road trauma as a result of rear-end crashes.

Informing traffic enforcement leniency and discretion: Crash culpability and the effectiveness of written warnings versus citations

ObjectivesDeterrence of risky driving behavior is important for the prevention of crashes and injuries. Traffic law enforcement is a key strategy used to decrease risky driving, but there is little evidence on the deterrent effect of issuing warnings versus citations to drivers regarding the prevention of future crashes. The purpose of this study was to 1) investigate the difference between citations and written warnings in their association with future crash culpability and 2) investigate whether drivers who were issued written warnings or citations have different associations with future crash culpability likelihood than those without prior citations or written warnings. MethodsData for this study included Iowa Department of Transportation crash data for 2016 to 2019 linked to data from the Iowa Court Case Management System. A quasi-induced exposure method was used based on driver pairs involved in the same collision in which one driver was deemed culpable and one was non-culpable. Conditional logistic regression models were constructed to examine predictors of crash culpability. The main independent variable was traffic citation and warnings history categorized into moving warning, non-moving warning, moving citation, non-moving citation, or no citation or warning in the 30 days prior to the crash. ResultsThe study sample included a total of 152,986 drivers. Among drivers with moving violations, previously cited drivers were more likely to be crash culpable than previously warned drivers (OR = 1.64, 95% CI = 1.29–2.08). Drivers with prior non-moving citations were less likely to be the culpable party in a crash than a driver who had no recent warnings or citations (OR = 0.72, 95% CI = 0.58–0.89). Drivers with prior warnings (moving or non-moving) did not appreciably differ in crash culpability relative to drivers who had not received any citations or warnings in the previous 30 days. ConclusionsDrivers with prior moving citations were more likely to be culpable in a future crash than drivers with prior moving warnings, which may relate to overall driving riskiness as opposed to effectiveness of citations in deterring risky driving behaviors. Results from this study also suggest that officer discretion was being appropriately applied by citing the riskiest drivers, while giving lower risk drivers warnings. Results from this study may be useful to support strengthening of state driver improvement programming.

Driving under the influence of substances and motor vehicle fatalities among older adults in the United States

Objective This study examines contribution of substance use (including alcohol, cannabinoids, stimulants, narcotics, depressants, and hallucinogens) on the probability of drivers being at-fault for a crash on U.S. public roads, with specific emphasis on older adult drivers. Methods Data from the National Highway Traffic Safety Administration’s Fatality Analysis Reporting System (FARS) for the years 2010–2018 were employed for 87,060 drivers (43,530 two-vehicle crash pairs) involved in two moving vehicle crashes. The quasi-induced exposure (QIE) method was used to compute the relative crash involvement ratios (CIRs) for each relevant substance and illicit drug. Mixed-effect generalized linear regression models were fit to examine the effect of substance use on the probability of a driver being at-fault for a crash. Results There were 75.51% males and 73.88% Non-Hispanic Whites in our sample. The CIR for those aged 70–79 years was 1.17, and more than double (2.56) for the ≥80 years old drivers, while being relatively low among drivers of ages 20 to 69. Substance use, in general, disproportionately increased the probability of being at-fault during a crash, regardless of driver’s age. Though older drivers are less likely than other age groups to report substance use, presence of substances among older drivers increased the probability of their being at-fault two to four times during a crash across almost all substances. The regression models, after adjusting for driver’s sex, road grade, weather, light conditions, distraction, and speeding at time of crash, revealed that older drug-impaired drivers were twice as likely to be at fault in a fatal crash (aOR = 1.947; 95% CI = 1.821, 2.082; <0.0001) compared to their middle-aged counterparts. Similarly, most substance use categories were responsible for the probabilities of higher CIRs among the drivers. Conclusion These findings necessitate continued efforts to bring awareness to the deadly consequences of “drugged driving,” especially among older adult drivers.

Variation in drivers’ seat belt use by indicators of community-level vulnerability

Introduction: Examining crash reports with linked community-level indicators may optimize efforts aimed at improving traffic safety behaviors, like seat belt use. To examine this, quasi-induced exposure (QIE) methods and linked data were used to (a) estimate trip-level seat belt non-use of New Jersey (NJ) drivers and (b) determine the degree to which seat belt non-use is associated with community-level indicators of vulnerability. Method: Driver-specific characteristics were identified from crash reports (age, sex, number of passengers, vehicle type) and licensing data (license status at the time of the crash). Geocoded residential addresses were leveraged within the NJ Safety and Health Outcomes warehouse to create quintiles of community-level vulnerability. QIE methods were applied to estimate trip-level prevalence of seat belt non-use in non-responsible, crash-involved drivers between 2010–2017 (n = 986,837). Generalized linear mixed models were then conducted to calculate adjusted prevalence ratios and 95 % confidence intervals for being unbelted for driver-specific variables and community-level indicators of vulnerability. Results: Drivers were unbelted during 1.2 % of trips. Males, those with suspended licenses, and those without passengers had higher rates of being unbelted than their counterparts. An increase was observed in traveling unbelted with increasing quintiles of vulnerability, such that drivers in the most vulnerable communities were 121 % more likely to be unbelted than those in the least vulnerable communities. Conclusions: Prevalence of driver seat belt non-use may be lower than previously estimated. Additionally, communities with the highest amount of the population living with three or more indicators of vulnerability have higher rates of seat belt non-use; this may be a particularly useful metric to inform future translational efforts improving seat belt use. Practical Applications: As evidenced by the findings that risk of being unbelted increased as drivers’ community vulnerability increased, novel communication efforts tailored to drivers from vulnerable neighborhoods may optimize efforts.

Effects of forward collision warning and automatic emergency braking on rear-end crashes involving pickup trucks

Objective Forward collision warning (FCW) and automatic emergency braking (AEB) have found to be effective on cars, SUVs, and large trucks. The objective of this study was to extend prior work and estimate the effects of FCW and AEB on pickups. Methods Rear-end crashes where a pickup with optional FCW or AEB was the striking vehicle were identified in 25 U.S. states during 2017–2020. Quasi-induced exposure was performed with logistic regression to compare rear-end-striking crashes between vehicles with and without the systems relative to being struck in the rear. Rear-end-striking crash rates per registered vehicle year were also compared between equipped and non-equipped pickups with Poisson regression. The association of the systems with severity in the rear-end-striking crashes that occurred was examined with logistic regression. Results In the quasi-induced exposure analysis, AEB was associated with statistically significant 34% reductions in the risk of a rear-end-striking crash of any severity and with any injuries, and a 76% reduction in the risk of a rear-end-striking crash with serious or fatal injuries (p = 0.09). FCW was associated with statistically significant declines of 22% in the risk of a rear-end-striking crash with any injuries and 71% in the risk of a rear-end-striking crash with serious or fatal injuries, but FCW was not associated with a change in all rear-end-striking crashes. Results were similar in Poisson regression models for all but FCW’s effect on all rear-end-striking crashes. The odds that rear-end-striking crashes involving pickups with FCW resulted in any injuries were 26% lower, and serious/fatal injuries were 75% lower, than those involving pickups without the system. AEB was associated with a nonsignificant reduction in the odds of serious/fatal injuries when a rear-end strike occurred and no change in the odds of any injuries. Discussion Consistent benefits for front crash prevention systems have been demonstrated for a wide range of vehicle types, now including a sample of pickups. Gaps in proposed U.S. regulations should be filled so that AEB is required equipment on all new vehicles.

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

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

Effects of automatic emergency braking systems on pedestrian crash risk

ObjectiveAutomatic emergency braking (AEB) that detects pedestrians has great potential to reduce pedestrian crashes. The objective of this study was to examine its effects on real-world police-reported crashes. MethodsTwo methods were used to assess the effects of pedestrian-detecting AEB on pedestrian crash risk. Vehicles with and without the system were examined on models where it was an optional feature. Poisson regression was used to estimate the effects of AEB on pedestrian crash rates per insured vehicle year, and quasi-induced exposure using logistic regression compared involvement in pedestrian crashes to a system-irrelevant crash type. ResultsAEB with pedestrian detection was associated with significant reductions of 25%–27% in pedestrian crash risk and 29%–30% in pedestrian injury crash risk. However, there was not evidence that that the system was effective in dark conditions without street lighting, at speed limits of 50 mph or greater, or while the AEB-equipped vehicle was turning. ConclusionsPedestrian-detecting AEB is reducing pedestrian crashes, but its effectiveness could be even greater. For the system to make meaningful reductions in pedestrian fatalities, it is crucial for it to work well in dark and high-speed conditions. Other proven interventions to reduce pedestrian crashes under challenging circumstances, such as improved headlights and roadway-based countermeasures, should continue to be implemented in conjunction with use of AEB to prevent pedestrian crashes most effectively.

GIS techniques to analyze factors associated with crash occurrence

Fatality rates in Kentucky have been higher than the national average for the past decade. Prior research postulated that the region’s unique socioeconomic conditions could provide a compelling explanation of the higher cash rates of the southeast in the US. This study examines the relationship between safety and socioeconomic characteristics using an extensive spatial analysis of crashes in Kentucky. Quasi-induced exposure technique was utilized to determine the crash propensity of different driver groups. Through a series of GIS techniques and spatial analysis, the relative accident involvement ratios were calculated for each group of drivers. The findings of the study concur with the previous findings between driving behavior and demographic factors such as age and gender. The study also attempted to explain the regional disparities in crash occurrence across the state in terms of economic status. The study concluded that the drivers residing in the Appalachian regions have a higher propensity to cause a crash, regardless of the age and gender of the driver. The findings of the study can be used to identify high risk counties for the Safety Circuit Rider (SCR) program in Kentucky.

An ensemble machine learning method for crash responsibility assignment in quasi-induced exposure theory

Quasi-induced exposure theory requires the clear-cut assignment of crash responsibility for individual crash-involved drivers. The assignment method based on the citation by police officers poses a concern that the citation would be issued due to the nonmoving violations rather than the driving actions that directly contribute to the crash. Thus, the objective of the study is to improve the accuracy of citation-based responsibility assignments. Binary logistic regression is employed to identify the factors affecting the citation decision of the police officers. An ensemble machine learning method that combines random forest, neural network, and extreme gradient boosting classifiers is established to allocate the crash responsibility. The findings include that (1) the police citation is closely related to the presence of hazardous driving behavior, but it can also be influenced by several factors such as driver age, drinking status, and the collision impact point of the vehicle; and (2) compared to the conventional models, the ensemble machine learning methods have better performance for crash responsibility assignment in terms of accuracy, Kappa coefficient, and area under the curve. The study serves to provide a reliable crash responsibility assignment approach to improve the accuracy of exposure estimation.

Prevalence of and Factors Associated with Driving a Vehicle with Timed-Out Inspection Certificate in Spain.

This study aimed to estimate the prevalence of vehicles on the road with a timed-out vehicle inspection certificate (TOVIC) and the associations of driver, vehicle, and environmental factors with this infraction. A quasi-induced exposure approach was used in this cross-sectional study to analyze a case series comprising 51,305 drivers passively involved in clean collisions (only one infractor driver involved) between two or more vehicles registered in the Spanish National Register of Road Crashes with Victims from 2014 to 2017. The prevalence of TOVIC was estimated in the whole sample and in subgroups defined by the variables considered. Multivariate logistic regression modeling was used to obtain adjusted odds ratios for the association between TOVIC and each category of the variables. The prevalence of TOVIC was low, although significant differences were found for certain subcategories of drivers, vehicles, and environmental factors. Significant positive adjusted associations were found between TOVIC and license-related infractions, vans (compared to cars), vehicle age, and vehicle defects. Several vehicle-related factors potentially associated with a high risk of involvement in a crash were clearly related with TOVIC, which suggests the need for measures to control this non-negligible number of high-risk vehicles on the road.

Sex Differences in the Amount and Patterns of Car-Driving Exposure in Spain, 2014 to 2017: An Application of a Quasi-Induced Exposure Approach.

We designed a cross-sectional study in Spain, from 2014 to 2017. Our objective was to assess sex-related differences in the amount of driving exposure of car drivers, overall and stratified by the main environment-related driving conditions. We compared the sex distribution across three populations: (1) total number of person-years aged > 18 years; (2) total number of person-years aged > 18 years holding a valid car-driving license; and (3) total number of non-responsible car drivers involved in crashes with another offending driver, stratified by different environmental variables. The quasi-induced exposure approach was applied: the non-responsible drivers were considered as representative of the entire population of drivers on the road at the place and time at which the crash occurred. We calculated the female-to-male odds ratio (OR) by comparing population 2 versus 1, and population 3 versus 2. Finally, we performed separate regression models in population 3 for each environment-related variable as the dependent variable and driver’s age and sex as the independent variables. The female-to-male OR for the first comparison was 1.12, but values below 1 were found for extreme age groups. In the second comparison, an OR of 0.50 (0.49–0.51) was found, with progressively lower OR values as age increased. In population 3, women were found to drive less than men in environments known to be high risk (i.e., open roads, night-time, poor light conditions, and weekends). A significant gender gap exists in the amount and type of driving exposure. Although women obtain a driving license more frequently than men, they drive much less and tend to avoid high-risk environments. These results emphasize the need to incorporate a gender perspective in the development and implementation of road safety interventions.

Effect of Socioeconomic and Demographic Factors on Crash Occurrence

The number of crashes and fatalities rate in the Commonwealth of Kentucky, U.S., have been higher than the national average for the past decades. It has been hypothesized that the distinct socioeconomic conditions of the state could be contributing to and explaining these trends. This study investigated the influence of socioeconomic characteristics on highway safety in Kentucky and attempted to identify the high-risk driver groups, based on crash data and the socioeconomic and demographic features of their residence zip codes. The quasi-induced exposure technique and binary logistic regression were employed to develop a predictive modeling approach for determining the probability of being the at-fault driver in a single- and two-unit crashes, based on socioeconomic characteristics of the driver residence zip code. The study identified that socioeconomic features such as income, poverty level, employment, age, gender, rurality, and number of traffic-related convictions of a driver’s zip code influence their likelihood to be at fault in a two-unit crash, while for single-unit crashes, in addition to these variables, educational attainment had also an impact. Younger and older drivers living in zip codes with low socioeconomic conditions have a higher probability to be the at-fault driver in both single- and two-unit crashes. The conclusions of the study can be used to determine the regions (zip codes) and driver groups with higher likelihood to be the at-fault driver in a crash and develop effective safety programs for the target groups.

Field effectiveness of general motors advanced driver assistance and headlighting systems

This study examined the field effectiveness of General Motors advanced driver assistance and headlighting systems. A total of 8,311,707 Model Year 2013–2019 vehicles were matched to police-reported crashes from 12 states. The quasi-induced exposure method was used to compare system-relevant and system-irrelevant (control) crash counts for equipped and unequipped vehicles. Logistic regression was used to adjust for ten covariates. Results indicated fusion/radar Automatic Emergency Braking, camera Automatic Emergency Braking, and Forward Collision Alert systems reduced rear-end striking crashes by 45%, 38%, and 20%, respectively. When restricting data to crashes in which someone in the General Motors striking vehicle was injured, these reductions were elevated to 59%, 54%, and 31%, respectively, providing evidence of additional crash mitigation benefits. Similarly, the Lane Keep Assist with Lane Departure Warning and Lane Departure Warning (alone) systems provided 12% and 10% reductions in lane departure crashes, respectively, with corresponding benefits in the injury analysis increasing to 19% and 18%, respectively. The Lane Change Alert with Side Blind Zone Alert system reduced lane change crashes by 16%. Reverse Automatic Braking, Rear Cross Traffic Alert, Rear Park Assist, and Rear Vision Camera (where each of these systems generally included all of the preceding systems) produced, respectively, an 82%, 55%, 36%, and 24% reduction in backing crashes. For Front Pedestrian Braking, a non-significant 14% reduction was observed for the limited set of available pedestrian crash cases. Intellibeam (auto high beam headlighting), High-Intensity Discharge headlights, and the combination of these two systems provided 26%, 11%, and 32% reductions (relative to halogen headlights) in a combined set of (unlighted) nighttime animal, pedestrian, and bicyclist crashes, respectively. These results provide widespread evidence of the substantial crash avoidance and injury reduction opportunities afforded by the production systems evaluated, as well as identify untapped system opportunities for moving toward a zero crashes vision.

Evaluation of the effectiveness of vehicle roll stability control (RSC) for high center of gravity light passenger vehicles in Australasia

Objective Rollover crashes, which occur when the vehicle’s side or roof makes impact with the ground, present particularly serious injury risk. Higher rollover risk has been found for high riding vehicles – those with a relatively high center of gravity compared to the width of the wheel track. Electronic Stability Control (ESC), which automatically applies brakes to individual wheels and reduces engine power to help drivers regain control when traction is lost, has been shown to be effective in preventing a proportion of rollovers. A newer safety technology, Roll Stability Control (RSC), uses similar technology aimed specifically to reduce rollover risk. This study sought to estimate rollover crash rates associated with the fitment of RSC compared to non-fitment for high center of gravity (CG) light passenger vehicles using an induced exposure analysis. Methods Police-recorded Australasian crash data were studied for the years 2008-2017. A quasi-induced exposure analysis was restricted to vehicles already equipped with ESC as vehicles fitted with RSC always have ESC fitted. Rollover risk associated with RSC fitment was assessed, controlling for year of crash, speed limit at crash location, year of vehicle manufacture, vehicle market group, driver age, driver gender and jurisdiction identifier. Results The analysis found a statistically significant rollover risk ratio of 0.76 (95% CI 0.62-0.93), representing a 24% reduction in rollover risk, associated with RSC fitment for vehicles manufactured between 2008 and 2017. Analysis by particular market groups found significant risk ratio reductions for commercial utilities and large SUVs, but not for the other high CG market groups individually. Conclusions These results suggest that RSC is a highly effective safety feature for high CG vehicles. Fleet data from Australia and New Zealand showed declining rates of RSC fitment over recent years for SUVs, meaning the potential road safety benefits of the technology are not being fully realized.

Driver Liability Assessment in Vehicle Collisions in Spain.

An accurate estimation of exposure is essential for road collision rate estimation, which is key when evaluating the impact of road safety measures. The quasi-induced exposure method was developed to estimate relative exposure for different driver groups based on its main hypothesis: the not-at-fault drivers involved in two-vehicle collisions are taken as a random sample of driver populations. Liability assignment is thus crucial in this method to identify not-at-fault drivers, but often no liability labels are given in collision records, so unsupervised analysis tools are required. To date, most researchers consider only driver and speed offences in liability assignment, but an open question is if more information could be added. To this end, in this paper, the visual clustering technique of self-organizing maps (SOM) has been applied to better understand the multivariate structure in the data, to find out the most important variables for driver liability, analyzing their influence, and to identify relevant liability patterns. The results show that alcohol/drug use could be influential on liability and further analysis is required for disability and sudden illness. More information has been used, given that a larger proportion of the data was considered. SOM thus appears as a promising tool for liability assessment.

Validating the representativeness assumption of the quasi-induced exposure method using a national representative field observation survey

Objective The quasi-induced exposure (QIE) method was developed to estimate relative crash risk exposure. A fundamental assumption often made in applying the QIE method is that not-at-fault drivers in clean two-vehicle crashes (i.e., one and only one driver is at-fault) represent the general exposure of the driving population to crash risk in the absence of the intervention being studied. Our study used direct field observation data to test the representativeness of the assumption for not-at-fault drivers obtained from the General Estimating System (GES) crash data, a national crash database in the United States. Methods Distributions of driver gender, age group, vehicle type, and time-of-crash among the not-at-fault drivers in clean two-vehicle crashes (D2) and the ones in two-or-more-vehicle crashes (i.e., all not-at-fault drivers) from the GES data were compared to the driving population estimated from the National Occupant Protection Use Survey (NOPUS), a national representative field observation survey. Results The gender and vehicle-type distributions of D2 and all not-at-fault drivers were not statistically significantly different from the ones in the NOPUS data. Age-group distributions for both not-at-fault driving populations were marginally similar to the ones estimated from NOPUS. Conclusion By system-wide comparisons on gender, age group, vehicle type, and period, our study suggests that the not-at-fault drivers in crash databases with crashes ranging from no injury to fatal injury reflect the general driving population when the collision occurred. Future study should evaluate the representativeness assumption among other important factors, including roadway type, road geometry, and level of urbanization. Our study supports the credibility of applying the QIE method in traffic safety research using crash databases of all crashes with all severities.

Validating the underlying assumption of quasi-induced exposure technique disaggregated by crash injury severity

Introduction: Quasi-induced exposure (QIE) technique has been popularly applied in the field of traffic safety research for decades. One of the basic assumptions of QIE theory is that the not-at-fault driving parties (D2s) involved in the crashes are the random selection of overall driving population at the event of crash occurrence. Very few literatures, however, can be identified to validate the assumption for crashes with specific injury severities that may not be satisfied in reality. Method: The study aims to check the validity of the assumption categorized by crash injury severity with the use of Michigan crash data. Latent class analysis is employed to generate several latent classes for the crashes with specific injury outcomes. Chi-square test is adopted to identify the significance of the similarity of D2 distributions among the latent classes. Results: The results indicate that: (a) for fatal crashes the statistical tests do not identify the significant discrepancies for D2 distributions of driver gender, age, and vehicle type between latent classes; (b) for injury crashes, both D2 driver gender and age have the similar distributions between/among various classes, while the D2 vehicle types show the inconsistent distributions; and (c) with respect to property damage only crashes, the distributions of three vehicle-driver characteristics are significantly different among the latent classes. It implies that the underlying assumption may not entirely hold true for all the injury severities and driver-vehicle characteristics. Practical Applications: The findings pinpoint the applicability of the QIE technique under specific scenarios and highlight the importance of validating the underlying assumption of QIE prior to its application.

Explaining the Association between Driver's Age and the Risk of Causing a Road Crash through Mediation Analysis.

It has been widely reported that younger and older drivers have an excess risk of causing a road crash. Two casual hypotheses may coexist: the riskier driving behaviors and age-related mechanisms in extreme age groups (direct path) and the different environmental and vehicle circumstances (indirect path). Our aim was to quantify, through a mediation analysis, the percentage contribution of both paths. A case-control study was designed from the Spanish Register of Road Crashes with victims from 2014 to 2017. Assuming a quasi-induced exposure approach, controls were non-responsible drivers involved in clean collisions between two or more vehicles (n = 52,131). Responsible drivers for these collisions plus drivers involved in single crashes constituted the case group (n = 82,071). A logit model in which the outcome was the log (odds) of causing a road crash and the exposure was age groups was adjusted for driver, vehicle and environmental factors. The highest crash risk was observed in extreme age groups, compared to the 35–44 year old age group: the youngest (18–24 years old, odds ratio = 2.14, 95% confidence interval: 2.06–2.24) and the oldest drivers (>74 years old, odds ratio = 3.30, 95% confidence interval: 3.04–2.58). The mediation analysis identified the direct path as the main explanatory mechanism for these increases: 89% in the youngest and 93% in the oldest drivers. These data support the hypothesis that the excess crash risk observed for younger and older drivers is mainly related to their higher frequency of risky driving behaviors and age-related loss of capabilities. Preventive strategies in extreme-aged drivers should focus on decreasing these behaviors.