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

Zhao, Shanshan; Wang, Kai; Jackson, Eric

doi:10.1177/0361198119841036

Cited by 13 publications

(12 citation statements)

References 33 publications

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“…It was found that both two variables are highly consistent in identifying the at-fault drivers. These findings are also consistent with previous research ( 38 ). Only multiple vehicle crashes were taken into consideration because of the very low chance of a driver being not-at-fault in single vehicle crashes ( 44 ).…”

Section: Datasupporting

confidence: 94%

“…The QIE method described above is an effective technique used for estimating exposure of a specific driver or vehicle population when real exposure data are not available (9). The basic idea of this method, that the proportion of a driver/vehicle category not-at-fault in collisions with more than one vehicle is related to the exposure of that driver/vehicle category in the driving population, has been found to hold for the not-at-fault drivers in crashes with more than two vehicles (38). This method is also capable of estimating exposure for finely disaggregated contexts, that is, directly from the crash data (36).…”

Section: Site Level Demographic and Vehicle Datamentioning

confidence: 99%

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

Sharmin

Ivan

Zhao

et al. 2020

Transportation Research Record: Journal of the Transportation R

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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.

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Section: Datasupporting

confidence: 94%

Section: Site Level Demographic and Vehicle Datamentioning

confidence: 99%

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

Sharmin

Ivan

Zhao

et al. 2020

Transportation Research Record: Journal of the Transportation R

Self Cite

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“…Other limitations are linked to our application of the quasi-induced exposure approach, in particular with regard to the method followed to assign crash responsibility (and therefore to identify the subpopulation of non-responsible drivers) [ 55 ], and the validity of the assumptions by which these NRDs can be considered representative of the entire population of drivers on the road. The latter issue has been extensively discussed in previous works by our research team and other authors [ 56 , 57 , 58 , 59 , 60 ]. Briefly, the validity of this method depends on the correct identification of responsibility by the police officers (based on the commission of infractions).…”

Section: Discussionmentioning

confidence: 75%

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

Mateos-Granados

Martín-delosReyes

Rivera-Izquierdo

et al. 2021

IJERPH

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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.

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“…Most US states have adopted GDL systems to reduce the high crash rates of young, inexperienced drivers. Extensive research has documented the benefits of GDL for 16- and 17-year-old novice drivers (Foss et al 2001 ; Shope 2007 ; Shope et al 2001 ; Williams 2017 ; Williams & Shults 2010 ; Williams et al 2012 ). Nonetheless, some have speculated that GDL might contribute to the prevalence of unlicensed driving.…”

Section: Introductionmentioning

confidence: 99%

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

2022

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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.

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

Cited by 13 publications

References 33 publications

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

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

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

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

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