Quasi-induced Exposure Method Research Articles

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.

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.

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.

Identification of contributing factors for interchange crashes based on a quasi-induced exposure method

Interchanges are important highway facilities by connecting two or more freeways/expressways; however, it has been considered more dangerous than basic segments because of drivers’ decision-making to stay or exit, weaving, variations in speeds, etc. In this study, we aim at contributing to the literature by using a quasi-induced exposure method and logistic regression modeling approach to identify contributing factors associated with the risk of causing an interchange crash. To this end, the 2014 traffic crash data, roadway and drivers’ characteristics were collected from Florida. The modeling results indicate that drivers’ age, gender, distraction, alcohol, and other factors have statistically significant effects. In addition, the finding suggests that drivers are more likely to cause crashes at cloverleaf and direct connection interchanges than at diamond interchanges. Furthermore, a support vector machine (SVM) model was applied to compare its predictability with the logistic regression model, and a sensitivity analysis was conducted. From the comparison of the areas under the receiver operating characteristic curve (AUC) of the two approaches, it shows that the SVM model outperforms the logistic regression model. It is expected that the findings would help establish effective strategies to reduce traffic crashes at interchanges by targeted education, engineering, and enforcement.

Vehicle safety characteristics in vulnerable driver populations

Objective National data suggest drivers who are younger, older, and have lower socioeconomic status (SES) have heightened crash-related injury rates. Ensuring vulnerable drivers are in the safest vehicles they can afford is a promising approach to reducing crash injuries in these groups. However, we do not know the extent to which these drivers are disproportionately driving less safe vehicles. Our objective was to obtain population-based estimates of the prevalence of important vehicle safety criteria among a statewide population of drivers. Methods We analyzed data from the NJ Safety and Health Outcomes warehouse, which includes all licensing and crash data from 2010–2017. We borrowed the quasi-induced exposure method’s fundamental assumption—that non-responsible drivers in clean (i.e., only one responsible driver) multi-vehicle crashes are reasonably representative of drivers on the road—to estimate statewide prevalence of drivers’ vehicle characteristics across four driver age groups (17–20; 21–24; 25–64, and ≥65) and quintiles of census tract median household income (n = 983,372). We used NHTSA’s Product Information Catalog and Vehicle Listing platform (vPIC) to decode the VIN of each crash-involved vehicle to obtain model year, presence of electronic stability control (ESC), vehicle type, engine horsepower, and presence of front, side, and curtain air bags. Results The youngest and oldest drivers were more likely than middle-aged drivers to drive vehicles that were older, did not have ESC, and were not equipped with side airbags. Additionally, across all age groups drivers of higher SES were in newer and safer vehicles compared with those of lower SES. For example, young drivers living in lowest-income census tracts drove vehicles that were on average almost twice as old as young drivers living in highest-income tracts (median [IQR]: 11 years [6–14] vs. 6 years [3–11]). Conclusions Vehicle safety is an important component of seminal road safety philosophies that aim to reduce crash fatalities. However, driver groups that are overrepresented in fatal crashes—young drivers, older drivers, and those of lower SES—are also driving the less safe vehicles. Ensuring drivers are in the safest car they can afford should be further explored as an approach to reduce crash-related injuries among vulnerable populations.

Incorporating Demographic Proportions into Crash Count Models by Quasi-Induced Exposure Method

Quasi-induced exposure (QIE) is an effective technique for estimating the exposure of a specific driving or vehicle population when real exposure data are not available. Typically crash prediction models are carried out at the site level, that is, segment or intersection. Driving population characteristics are generally not available at this level, however, and thus are omitted from count models. Because of the sparsity of traffic crashes, estimating driving population distributions at the site level using crash data at individual sites is challenging. This study proposes a technique to obtain demographic proportions to incorporate in the count models as an exposure at each site by aggregating similar adjacent sites until significant demographic proportions are obtained. Information on driver gender, age, and vehicle type are obtained by QIE using five years (2010–2014) of crash data; and road inventories are obtained for 1,264 urban four-lane divided highway segments in California. Count models including only site level factors were compared with models including both crash level and site level factors. The latter outperformed the former in relation to mean prediction bias and mean absolute deviation statistics on holdout sample predictions. Results indicate that teen drivers are more crash prone in total and in fatal plus injury severity crashes. For senior drivers, crash risk increases with the increase in severity level. The presence of vehicles other than passenger cars and trucks reduces total and property damage only crash counts. Female drivers are associated with higher total and fatal plus injury crash counts.

Use of codes data to improve estimates of at-fault risk for elderly drivers

The fastest-growing demographic in the United States is people aged 65 and over. Because elderly drivers may experience decline in the physical and mental faculties required for driving (which could lead to unsafe driving behaviors), it is critical to determine whether elderly drivers are more likely than younger drivers to be at fault in a crash. This study uses Kentucky crash data and linked hospital and emergency department records to evaluate whether linked data can more accurately estimate the crash propensity of elderly drivers to be at-fault in injury crashes. The Kentucky crash data is edited to conform to the General Use Model (GUM), with crash propensities for linked data compared to propensities developed using the GUM dataset alone. The quasi-induced exposure method is used to determine crash exposure. Factors such as age, gender, and crash location are explored to assess their influence on the risk of a driver being at fault in an injury crash. The overall findings are consistent with previous research — elderly drivers are more likely than younger drivers to be at fault in a crash. Linking crash with hospital and emergency department records could also establish a clearer understanding of the injury crash propensity of all age groups. Equipped with this knowledge, transportation practitioners can design more targeted and effective countermeasures and safety programs to improve the safety of all motorists.

Validation of not-at-fault driver representativeness assumption for quasi-induced exposure using U.S. national traffic databases

Introduction:The quasi-induced exposure (QIE) method has been widely implemented into traffic safety research. One of the key assumptions of QIE method is that not-at-fault drivers represent the driving population at the time of a crash. Recent studies have validated the QIE representative assumption using not-at-fault drivers from three-or-more vehicle crashes (excluding the first not-at-fault drivers; D3_other) as the reference group in single state crash databases. However, it is unclear if the QIE representativeness assumption is valid on a national scale and is a representative sample of driving population in the United States. The aims of this study were to assess the QIE representativeness assumption on a national scale and to evaluate if D3_other could serve as a representative sample of the U.S. driving population. Method: Using the Fatality Analysis Reporting System (FARS) and the National Occupant Protection Use Survey (NOPUS), distributions of driver gender, age, vehicle type, time, and roadway type among the not-at-fault drivers in clean two-vehicle crashes, the first not-at-fault drivers in three-or-more-vehicle crashes, and the remaining not-at-fault drivers in three-or-more vehicle crashes were compared to the driver population observed in NOPUS. Results: The results showed that with respect to driver gender, vehicle type, time, and roadway type, drivers among D3_other did not show statistical significant difference from NOPUS observations. The age distribution of D3_other driver was not practically different to NOPUS observations. Conclusions: Overall, we conclude that D3_other drivers in FARS represents the driving population at the time of the crash. Practical applications: Our study provides a solid foundation for future studies to utilize D3_other as the reference group to validate the QIE representativeness assumption and has potential to increase the generalizability of future FARS studies.

Identifying motorist characteristics associated with youth bicycle–motor vehicle collisions

Objective: The objective of this study was to identify driver characteristics associated with youth bicycle–motor vehicle collisions in Alberta, Canada.Methods: Edmonton and Calgary police collision report data from the years 2010–2014 were used. From these data, motor vehicle collisions involving youth (<18 years old) were identified (cases). The controls were drivers who, over the same period, were involved in separate motor vehicle–only collisions but deemed not at fault using an automated culpability analysis. Control selection used the quasi-induced exposure method, assuming that not-at-fault drivers in collisions are representative of the typical driver (source population). Descriptive statistics, including proportions, medians, and interquartile ranges (as appropriate) were used to describe the characteristics of the case and control drivers. Purposeful variable selection techniques were used to inform multivariable logistic regression models and results are presented as adjusted odds ratios (aORs) and 95% confidence intervals (CIs).Results: Four hundred twenty-three drivers involved in youth bicycle–motor vehicle collisions were identified, as were 243,927 not-at-fault control drivers. Drivers >54 years old had higher odds of involvement in youth bicycle–motor vehicle collisions than drivers between 25 and 39 years old (aOR = 1.37; 95% CI, 1.03, 1.82). Compared to driving between 3:01 p.m. and 6:00 p.m., driving between 12:01 a.m. and 6:00 a.m. (aOR = 0.27; 95% CI, 0.11, 0.66), between 6:01 a.m. and 9:00 a.m. (aOR = 0.61; 95% CI, 0.44, 0.85), or between 9:01 a.m. and 12:00 p.m. (aOR = 0.26; 95% CI, 0.16, 0.41) had lower odds of bicyclist collision, whereas driving between 6:01 p.m. and 12:00 a.m. had higher odds (aOR = 1.34; 95% CI, 1.01, 1.79). Driving a truck/van had lower odds of bicyclist collision compared to driving a passenger car (aOR = 0.67; 95% CI, 0.48, 0.94).Conclusions: Culpability analysis is typically applied to motorists to identify transient exposures; however, this study used culpability analysis to select control drivers who could be compared with drivers involved in youth bicycle–motor vehicle collisions. This study highlights motorist characteristics in youth bicycle–motor vehicle collisions. In doing so, we hope to inform primary prevention strategies for motorists and the environment that will reduce collisions.

Evaluation of Not-At-Fault Assumption in Quasi-Induced Exposure Method using Traffic Crash Data at Varied Geographical Levels

Acquiring real-world driver distribution data on roadways is a challenge. The quasi-induced exposure (QIE) method is a promising alternative as it only requires the available crash data. The question to be answered through this study is whether the not-at-fault driver assumption of the QIE still holds when the population is broken down to smaller geographical levels, such as counties, towns, or routes. This is important because the result will provide statistical support to choose for or against the application of QIE at disaggregate levels. In this study, the distributions of driver gender, age, and vehicle type between four groups of drivers in the crash data were examined, using data obtained from the state of Connecticut from 2015 to 2017. Namely, they are the not-at-fault drivers and at-fault drivers in two-vehicle crashes (NF2 and AF2) and the not-at-fault drivers and at-fault drivers in three-or-more vehicle crashes (NF3 and AF3). Chi-square tests and Wilcoxon Mann–Whitney tests were used to provide statistical evidence of whether the driver groups come from the same population. The evidence shows that there are no statistical differences between the distributions of NF2 and NF3. The QIE assumption of not-at-fault drivers is valid at all tested geographical levels. Driver characteristic distribution in the NF2 (and NF3) groups in the crash data should be a good representation of the driving population. The results also revealed the similarities of distributions between AF2 and AF3 and the significant differences between the not-at-fault drivers (NF2 and NF3) and at-fault-drivers (AF2 and AF3).

Effects of Signalization at Rural Intersections Considering the Elderly Driving Population

The main objective of this study is to quantify the safety impacts of signalization at Florida’s rural three-leg and four-leg stop-controlled intersections by estimating crash modification factors. The intersections are those in which stop signs are provided for the minor approaches or all-way stop-controlled intersections. The crash modification factors (CMF) are estimated using the cross-sectional method. Generalized linear models (GLM) and multivariate adaptive regression spline models (MARS) are employed with four years of Florida crash data. The K-nearest neighbor (KNN) and K-means clustering algorithms are implemented to identify the comparison sites which are sites having similar characteristics as those of the converted intersections. Furthermore, the quasi-induced exposure method is used to evaluate separately the safety effects of signalization for elderly and non-elderly drivers. According to the results, signalization contributes to an increase in property damage only (PDO) and rear-end crashes. In addition, elderly drivers are more at risk of being involved in such crashes than non-elderly drivers. In particular, at rural four-leg two-way stop-controlled intersections, signalization decreases crash severity, and a greater percentage of the decrease is observed for the elderly drivers than non-elderly especially when the intersection has a high level of major-road average annual daily traffic (AADT) and elderly driver proportion. This study also demonstrates that the MARS model shows a better model fit than the GLM model due to its strength in capturing nonlinear relationships and interaction effects among variables. This study’s findings have implications for both practitioners and researchers.

Relative Crash Involvement Ratio Associated with Different Sources of Young Drivers’ Distraction

This paper aims to identify crash risk factors associated with young drivers’ distractions. Many factors were implicated including using mobile phones while driving or driving with passengers. Data needed for this study were collected from an online questionnaire survey. Beside young drivers’ distraction data, records on drivers’ demographics including age, gender and educational level were collected. Safety-related data on seat belt use were also collected. Each of the variables, contributing to young drivers’ distraction, safety, and educational level, was sorted into four categories according to young driver age (≤18, 19-22, 23-26, and 27-30 years old). The Relative Crash Involvement Ratio (RCIR) was estimated using the Quasi Induced Exposure Method (QIEM) using Microsoft Excel 2007. The results were then exported to the Statistical Package for Social Science Version 19 software. Paired t-test and ANOVA analysis were used to find the statistical significance in the RCIR values. Among the general findings, five outcomes were most prominent. The higher the educational level, the less likely young drivers would get involved in a crash. Young drivers ≤18 years old were almost 4.5 times more likely to get involved in a crash when 3 passengers travelled with them compared to 27-30 year-old drivers. Young drivers of all age groups were more likely to get involved in a crash when using mobile phones. Crash risk was reduced by up to 83 % when young drivers stopped using their mobile phone while driving. Crash risk was reduced by up to 93.43 % when young drivers used seat belt while driving. Finally, countermeasures to improve young drivers’ safety were proposed.

Does crash risk increase when emergency vehicles are driving with lights and sirens?

IntroductionEmergency vehicles, such as police, ambulances, and fire vehicles, need to arrive at the scene of emergencies as quickly as possible, and thus they often travel in emergency mode – using their lights and sirens and often bypassing traffic signals. We examined whether travelling in emergency mode increased crash risk among police, ambulance and fire vehicles. MethodsWe conducted a quasi-induced exposure analysis using data from the Iowa Crash Database for the period of 2005 through 2013. The data are maintained by the Iowa Department of Transportation (IADOT), Office of Driver Services (ODS) and includes all investigating police officer's reports of motor vehicle crashes. The quasi-induced exposure method is an approach to calculate crash risk in the absence of exposure data using vehicles without a contributing cause (did not contribute to the crash) as a proxy for the baseline driving population. ResultsFrom 2005 – 2013, police vehicles were involved in 2406 crashes and ambulances and fire vehicles were involved in 528 crashes. Police vehicles were 1.8 times more likely to crash while driving in emergency mode than usual mode; this was a statistically significant increase. Ambulance and fire vehicles were not more likely to crash in emergency mode compared with usual mode. For police, other factors that contributed to crash risk included gender, age, icy/snowy roads, unpaved roads, and intersections. For ambulances and fire vehicles, other factors that contributed to crash risk included gender, age, weekends, icy/snowy roads and urban locations. ConclusionCrash risk increased when police vehicles drove with lights and sirens but did not increase for ambulance and fire vehicles. Further research is necessary to develop and evaluate strategies to mitigate crash risk among police vehicles. Cultural approaches which prioritize transportation safety in conjunction with reaching the scene as quickly as possible may be warranted.

Young Driver Compliance With Graduated Driver Licensing Restrictions Before and After Implementation of a Decal Provision

PurposeIn May 2010, New Jersey implemented the first-in-the-nation decal provision to increase intermediate drivers' compliance with Graduated Driver Licensing restrictions and ultimately reduce young driver crashes. We previously found that the provision was associated with a 9.5% decline in crash rates. This study evaluates whether the decal provision was associated with an increase in compliance with passenger and nighttime restrictions. MethodsWe analyzed New Jersey driver licensing and crash data from 2008 through 2012. We used the quasi-induced exposure method to estimate prevalence of noncompliance among 20,593 nonresponsible 17- to 20-year-old intermediate drivers involved in crashes. Multivariate log-binomial regression models compared the monthly prevalence of noncompliance with restrictions pre and post implementation, adjusted for age, sex, season, and area income and population density. Analyses were conducted in 2016–2017. ResultsOverall estimated noncompliance with the nighttime restriction was 1.75% before and 1.71% after the decal provision (p = .83). Noncompliance with the passenger restriction was 8.68% before and 8.31% after (p = .35). Introduction of the decal provision was not associated with a change in noncompliance rates. ConclusionsCompliance rates among New Jersey intermediate drivers were high both before and after the decal provision. Findings do not suggest that the decline in crash rates following implementation was because of increased compliance with nighttime or passenger driving restrictions. Additional research is needed to understand mechanisms by which decal provisions may reduce young driver crashes.

Compliance With and Enforcement of Graduated Driver Licensing Restrictions.

Graduated Driver Licensing (GDL) is the most effective strategy to reduce the burden of young driver crashes, but the extent to which young intermediate (newly licensed) drivers comply with, and police enforce, important GDL passenger and night-time restrictions is largely unknown. Population-level rates of intermediate drivers' compliance were estimated as well as police enforcement among crash-involved drivers who were noncompliant. New Jersey's statewide driver licensing and crash databases were individually linked. The quasi-induced exposure method's fundamental assumption-that nonresponsible young intermediate drivers in clean (i.e., only one responsible driver) multivehicle crashes are reasonably representative of young intermediate drivers on the road-was borrowed. Incidence was then estimated among the 9,250 nonresponsible intermediate drivers who were involved in clean multivehicle crashes from July 2010 through June 2012. The proportion of crash-involved noncompliant intermediate drivers who were issued a GDL citation, by crash responsibility, was calculated. Data were collected in 2013 and analyzed in 2015. Overall, 8.3% (95% CI=7.8%, 8.9%) of intermediate drivers' trips were noncompliant with New Jersey's passenger restriction and 3.1% (95% CI=2.8%, 3.5%) with its night-time restriction; compliance was significantly lower among those residing in low-income and urban areas, among male drivers, on weekends, and in summer months. The proportion of crash-involved noncompliant intermediate drivers who were issued a GDL citation was low (nonresponsible drivers, 10.3%; responsible drivers, 19.0%). The vast majority of intermediate driver trips are in compliance with GDL restrictions. Outreach activities should consider focusing on higher-risk situations and groups with higher noncompliance rates.

Estimating young novice drivers' compliance with graduated driver licensing restrictions: A novel approach

ABSTRACTObjectives: Current methods of estimating compliance with graduated driver licensing (GDL) restrictions among young drivers with intermediate driver's licenses—which include surveys, direct observations, and naturalistic studies—cannot sufficiently answer many critical foundational questions: What is the extent of noncompliance among the population of young intermediate drivers? How does compliance change over the course of licensure? How does compliance differ by driver subgroup and in certain driving environments? This article proposes an alternative and complementary approach to estimating population-level compliance with GDL nighttime and passenger restrictions via application of the quasi-induced exposure (QIE) method.Methods: The article summarizes the main limitations of previous methods employed to estimate compliance. It then introduces the proposed method of borrowing the fundamental assumption of the QIE method—that young intermediate drivers who are nonresponsible in clean (i.e., one and only one responsible driver) multivehicle crashes are reasonably representative of young intermediate drivers on the road—to estimate population-based compliance. I describe formative work that has been done to ensure this method can be validly applied among young intermediate drivers and provide a practical application of this method: an estimate of compliance with New Jersey's passenger restrictions among 8,006 nonresponsible 17- to 20-year-old intermediate drivers involved in clean 2-vehicle crashes from July 2010 through June 2012.Results: Over the study period, an estimated 8.4% (95% confidence interval, 7.8%, 9.0%) of intermediate drivers' trips were not in compliance with New Jersey's GDL passenger restriction. These findings were remarkably similar to previous estimates from more resource-intensive naturalistic studies (Goodwin et al. 2006; Klauer et al. 2011).Conclusion: Studies can practically apply proposed methods to estimate population-level compliance with GDL passenger and night restrictions; examine how compliance varies by relevant driver, vehicle, and environmental factors; and evaluate the implementation of a GDL provision or other intervention aimed at increasing compliance with these restrictions. Important considerations and potential limitations and challenges are discussed.

Quasi-induced exposure method for pedestrian safety at signalized intersections

ABSTRACTTo investigate pedestrian exposure when only pedestrian-crash data are available, the quasi-induced exposure method is used to identify the factors that contributed to pedestrian-vehicle crashes in Las Vegas from 2004 to 2008. The results show that overall crash severity, light conditions, and weather conditions are potentially risky factors in pedestrian crashes, and the time of day or day of the week are less risky. Multiple-correspondence analysis (MCA) is then used to investigate how pedestrian crash severity is influenced by the interactions among a range of variables. The results indicate that among instances of pedestrian-vehicle crashes cases of property damage only tend to occur during off-peak daytime hours and under dark or dawn light conditions in clear weather. Crashes resulting in injuries usually occur during weekday peak hours in daylight and under rainy conditions. Most fatal crashes occur during off-peak hours, at night on the weekends.