Driver Crash Research Articles

Driver's age and rear-end crashes associated with distraction

Background and Objectives: Distraction is one of the affective factors in the occurrence of traffic accidents, which, in its turn, is the second leading cause of death and the main cause of years of life lost due to premature death and disability in Iran. The aim of the present study was to investigate the distracting behaviors of male drivers in rear-end crashes. Materials and Methods: This descriptive cross-sectional study was conducted on male drivers who had experienced a rear-end crash in Tabriz in 2018. In total, 395 samples (whose main reason for the crashes was inattention to the forward field of view) were selected through the information and files available at the traffic police, and after contacting the subjects, the required information was entered in the checklist. Nine distraction factors were classified and ranked into three age groups of young, middle-aged, and older drivers. Results: Findings showed that distractions caused by mobile phone use and passengers were ranked first to third in all three age groups and distracting factors such as eating or drinking while driving, alcohol consumption, and sleepiness were in the last ranks. In addition, older drivers were ranked first in not identifying the distraction factors, and in the middle-aged group, the distraction caused by billboards was the leading cause of crashes. Conclusions: Adopting appropriate policies, such as law enforcement and cultural interventions to prevent driver distractions, and to control the in-car conversation, and cell phone use while driving can be considered as effective interventions to prevent rear-end crashes.

Alcohol Compliance Checks and Underage Alcohol-Involved Crashes: Evaluation of a Statewide Enforcement Program in South Carolina from 2006 to 2016.

This research evaluated the South Carolina Alcohol Enforcement Team impact for reducing retail alcohol access to underage persons to decrease drinking and driving crashes among that population. The natural research experiment used interrupted time-series (ITS) analyses of drinking and driving crashes involving under 21-year-old drivers from July 2006 through December 2016 (126-month period=4,782 Driving Under Influence [DUI] crashes for under 21-year-old drivers, µ=38 crashes per month). Additional data analyzed included the monthly total number of retail compliance checks (total during 126-month period=64,954 compliance checks completed, µ=515.5 checks per month), the average percentage of underage alcohol purchases (total completed during 126-month period=8,814 purchases, µ=70 purchases per month), and a calculated measure of the percent of the population under 21years old exposed to compliance checks each month. We used drinking and driving crashes for 21-year-old and over drivers as a control time series (total number over a 126-month period=52,180 DUI crashes for 21 and older drivers, µ=414.1 crashes per month). The results show a decline in drinking and driving crashes for drivers under 21 when compliance checks increase, and when compliance checks decline, traffic crashes increase. Stable Alcohol Enforcement Team implementation over 78months produced an overall 18 to 29% decline in such crashes. A visual examination of the crash time series demonstrated that under-21-age-driver crashes declined during the first wave of implementation and increased following a lag when enforcement declined, which provided additional empirical support for a South Carolina Alcohol Enforcement Team impact on retail alcohol availability. An ITS analysis of the prestable period compared to the stable period was statistically significant (T=-3.78, p<0.001). A cross-check of these results using single-vehicle nighttime crashes using identical Autoregressive Integrated Moving Average models was also statistically significant (T=-8.18, p<0.001). This longitudinal study provides strong evidence of sustained reductions in alcohol availability to underage youth can subsequently reduce alcohol-related traffic crashes. Reductions found in this study continued over several years, considerably longer than any previous equivalent research has shown.

Mid-term prediction of at-fault crash driver frequency using fusion deep learning with city-level traffic violation data

Traffic violations and improper driving are behaviors that primarily contribute to traffic crashes. This study aimed to develop effective approaches for predicting at-fault crash driver frequency using only city-level traffic enforcement predictors. A fusion deep learning approach combining a convolution neural network (CNN) and gated recurrent units (GRU) was developed to compare predictive performance with one econometric approach, two machine learning approaches, and another deep learning approach. The performance comparison was conducted for (1) at-fault crash driver frequency prediction tasks and (2) city-level crash risk prediction tasks. The proposed CNN-GRU achieved remarkable prediction accuracy and outperformed other approaches, while the other approaches also exhibited excellent performances. The results suggest that effective prediction approaches and appropriate traffic safety measures can be developed by considering both crash frequency and crash risk prediction tasks. In addition, the accumulated local effects (ALE) plot was utilized to investigate the contribution of each traffic enforcement activity on traffic safety in a scenario of multicollinearity among predictors. The ALE plot illustrated a complex nonlinear relationship between traffic enforcement predictors and the response variable. These findings can facilitate the development of traffic safety measures and serve as a good foundation for further investigations and utilization of traffic violation data.

Association between Crash Attributes and Drivers' Crash Involvement: A Study Based on Police-Reported Crash Data.

Understanding the association between crash attributes and drivers’ crash involvement in different types of crashes can help figure out the causation of crashes. The aim of this study was to examine the involvement in different types of crashes for drivers from different age groups, by using the police-reported crash data from 2014 to 2016 in Shenzhen, China. A synthetic minority oversampling technique (SMOTE) together with edited nearest neighbors (ENN) were used to solve the data imbalance problem caused by the lack of crash records of older drivers. Logistic regression was utilized to estimate the probability of a certain type of crashes, and odds ratios that were calculated based on the logistic regression results were used to quantify the association between crash attributes and drivers’ crash involvement in different types of crashes. Results showed that drivers’ involvement patterns in different crash types were affected by different factors, and the involvement patterns differed among the examined age groups. Knowledge generated from the present study could help improve the development of countermeasures for driving safety enhancement.

Preventing Drowsy Driving Crashes Among Night Shift Nurses: An Evaluation of Educational and Technological Interventions

Preventing Drowsy Driving Crashes Among Night Shift Nurses: An Evaluation of Educational and Technological Interventions

Validity of failure-caused traffic conflicts as surrogates of rear-end collisions in naturalistic driving studies

Although the frequency and severity of crashes are direct measures of road safety, crash data are typically of limited quality and they require long data collection periods to produce conclusive results. Surrogates of crashes that would allow a quick and accurate estimation of safety have been an active topic for years. Among multiple alternatives, traffic conflicts have been established as a promising surrogate measure. This paper is aimed to demonstrate the validity of failure-caused traffic conflicts by applying a recently proposed Lomax-based method to estimate the expected number of crashes from observed traffic conflicts.The data collected in the naturalistic driving program, the Second Strategic Highway Research Program (SHRP2), were used in the validation task. The rear-end crashes recorded during the SHRP2 program and the corresponding rear-end traffic conflicts were analyzed for three categories of drivers: young male, mature male, and mature female. Past research has indicated that these three categories have a distinctively different proneness to involvement in crashes. Out of all rear-end traffic conflicts included in the SHRP2 database, 1.4 % were used to estimate the crash frequencies and rates for each studied type of driver. The Lomax distribution was applied within the counterfactual framework. Then, the conflict-based crash rate estimates were compared to the crash rates of the studied types of drivers calculated from all the rear-end crashes observed in the SHRP2 study period.The conflict-based rate estimates followed well the crash-based rates and the existing knowledge about the safety performance of the studied drivers. The conflict-based results confirmed the over-representation of young male drivers in crashes. It was also confirmed that mature male drivers are involved in rear-end crashes more frequently than mature female drivers. The results demonstrate both the validity of the Lomax-based analysis of failure-caused traffic conflicts and the benefit of traffic conflicts analysis that considerably reduces a period of data collection from years for crashes to days or weeks for traffic conflicts.

The impact of sex on motor vehicle crash injury outcomes

Female and male drivers have different size proportions and anthropometry. However, in the United States and throughout the world, male crash dummies are the norm in vehicle testing and female crash dummies with representative anthropometric characteristics has not yet been used in testing to date. Given this discrepancy, female driver safety is not considered at the same level as male driver safety in the vehicle design and testing process. This research investigates the gap in literature of injury locations experienced by female and male drivers in comparable crashes using a newly created linked dataset of crash and emergency medical services (EMS) data in the Commonwealth of Massachusetts. Generalized linear regression and k-fold cross validation of over 26,000 unique crash cases were analyzed using explanatory and predictive methods. These analyses revealed that female drivers are more likely to experience their primary anatomic injury as an abdomen, chest, lower extremity, and upper extremity injury than male drivers. The results of this research can be used by researchers, vehicle designers, and policymakers to act upon these differences in injury outcomes and consider them in the development of a safe environment for all roadway users.

A systematic review of statistical models and outcomes of predicting fatal and serious injury crashes from driver crash and offense history data

BackgroundExpenditure on driver-related behavioral interventions and road use policy is often justified by their impact on the frequency of fatal and serious injury crashes. Given the rarity of fatal and serious injury crashes, offense history, and crash history of drivers are sometimes used as an alternative measure of the impact of interventions and changes to policy. The primary purpose of this systematic review was to assess the rigor of statistical modeling used to predict fatal and serious crashes from offense history and crash history using a purpose-made quality assessment tool. A secondary purpose was to explore study outcomes.MethodsOnly studies that used observational data and presented a statistical model of crash prediction from offense history or crash history were included. A quality assessment tool was developed for the systematic evaluation of statistical quality indicators across studies. The search was conducted in June 2019.ResultsOne thousand one hundred and five unique records were identified, 252 full texts were screened for inclusion, resulting in 20 studies being included in the review. The results indicate substantial and important limitations in the modeling methods used. Most studies demonstrated poor statistical rigor ranging from low to middle quality. There was a lack of confidence in published findings due to poor variable selection, poor adherence to statistical assumptions relating to multicollinearity, and lack of validation using new data.ConclusionsIt was concluded that future research should consider machine learning to overcome correlations in the data, use rigorous vetting procedures to identify predictor variables, and validate statistical models using new data to improve utility and generalizability of models.Systematic review registrationPROSPERO CRD42019137081

Older Driver Crash Involvement and Fatalities, by Age and Sex, 2000-2017.

Federal reporting of crash fatalities has limited age-by-sex stratification, but both age and sex are associated with driving reduction and cessation. We described older driver fatal crash involvement and fatalities using Fatality Analysis Reporting System data to calculate rates (per 100,000 licensed drivers, per 100,000 population) with age-by-sex stratifications. Nationally from 2000 through 2017, 110,422 drivers 65+ were involved in crashes resulting in at least one death within 30 days, and 67,843 of these older drivers died. Involvement and fatality rates per 100,000 licensed drivers in 2017 were lowest for females 65-69 (7.7 and 3.6, respectively) and highest for males age 85+ (34.3 and 25.5, respectively). Females had lower driver fatal crash involvement and fatality rates throughout the lifespan, even when rates generally decreased over time. Elaborating fatal crash trends and rates by age and sex helps to differentiate the public health burden of older driver crashes and fatalities.

Personality traits as predictors of cyclist behaviour

Road user behaviour and personality traits are important determinants of driver crash risk. While a great deal of research has been undertaken to understand the relationships between crash involvement, behaviours and personality traits for motor vehicle drivers, comparatively few studies have considered these factors for cyclists. This manuscript presents the findings of a study conducted amongst a sample of six hundred and fifteen (615) Australian cyclists, investigating these issues.The aim of this research was to establish a structure for a cycling behaviour questionnaire applicable to a cohort of Australian cyclists. Using the dimensions identified from the questionnaire, the research investigated the relationship between self-reported crashes, behaviours and personality traits, in order to further develop our understanding of risk factors associated with cycling.Personality traits (agreeableness, extroversion, conscientiousness, neuroticism and openness to experience) were measured using the Big Five Inventory. While cyclist behaviour was measured using a modified version of the cyclist behaviour questionnaire developed by the Dutch national road safety research centre (SWOV).Principal Components Analysis (PCA) was performed on the cycling behaviour questionnaire to identify underlying subscales of behaviour. The PCA identified a two dimension model representing violations (α = 0.74) and errors (α = 0.65), consisting of 16 items from the original 22 item cyclist behaviour questionnaire.Linear regressions for each of the cyclist behaviour factors identified that age was negatively associated with errors and violations, indicating that older cyclists report fewer errors or violations. Similarly, there was a negative association with average weekly kilometres travelled. Gender was a significant predictor of errors, but not violations, with male cyclists reporting fewer errors than females.When considering personality traits, there was a positive association between extroversion and both errors and violations. Significant negative associations were identified for agreeableness and conscientiousness. Neither neuroticism nor openness to experience were associated with the frequency of errors or violations.The research identified that demographics, travel characteristics and personality traits provide insight into engagement in aberrant cycling behaviours and these behaviours are associated with self-reported crash involvement. The research provides insight into behaviours that could be targeted with appropriate education and enforcement strategies.

Injury Severity among Young Drivers: A combination of Parametric and Non-parametric Analysis

Young drivers among the group recorded highest fatalities index in road traffic crashes. The objective of this study was to identify factors affecting injury severity of young drivers using 5-years crash data (2008-2012) in Sabah, Malaysia. This study used a combination of parametric and non-parametric analysis to allows the specification of nonlinearities and interactions in addition to main effects. The results indicate that crashes on nighttime, federal roads and involved with a single-vehicle are positively associated with injury among young drivers. Interestingly, municipal roads, female drivers, crashes on the roundabout and T/Y junction are less likely to involve injury. A higher-order interaction suggests that not-at-fault young drivers involved with out-of-control or hit the object are more likely to be severe. On the other hand, young passenger car drivers involved in overturn and sideswipe collisions are negatively associated with the injury. It was also found that young drivers with driving too close behaviour are less likely to injure when involved in rear-end collisions for passenger car and four-wheel drive. Findings of this study will help relate authorities to design well-targeted restrictive measures in reducing the severity level of young drivers in traffic crashes.

Injury severity analysis of familiar drivers and unfamiliar drivers in single-vehicle crashes on the mountainous highways

Mountainous highways suffer from high crash rates and fatality rates in many countries, and single-vehicle crashes are overrepresented along mountainous highways. Route familiarity has been found greatly associated with driver behaviour and traffic safety. This study aimed to investigate and compare the contributory factors that significantly influence the injury severities of the familiar drivers and unfamiliar drivers involved in mountainous highway single-vehicle crashes. Based on 3037 cases of mountainous highway single-vehicle crashes from 2015 to 2017, the characteristics related to crash, environment, vehicle and driver are included. Random-effects generalized ordered probit (REGOP) models were applied to model injury severities of familiar drivers and unfamiliar drivers that are involved in the single-vehicle crashes on the mountainous highways, given that the single-vehicle crashes had occurred. The results of REGOP models showed that 8 of the studied factors are found to be significantly associated with the injury severities of the familiar drivers, and 10 of the studied factors are found to significantly influence the injury severities of unfamiliar drivers. These research results suggest that there is a large difference of significant factors contributing to the injury severities between familiar drivers and unfamiliar drivers. The results shed light on both the similar and different causes of high injury severities for familiar and unfamiliar drivers involved in mountainous highway single-vehicle crashes. These research results can help develop effective countermeasures and proper policies for familiar drivers and unfamiliar drivers targetedly on the mountainous highways and alleviate injury severities of mountainous highway single-vehicle crashes to some extent. Based on the results of this study, some potential countermeasures can be proposed to minimize the risk of single-vehicle crashes on different mountainous highways, including tourism highways with a large number of unfamiliar drivers and other normal mountainous highways with more familiar drivers.

Prevalence and factors associated with turn signal neglect-related crashes among motorcyclists and car drivers in Vietnam

Road traffic crashes and injuries are a major societal challenge around the world. However, the majority of injuries and fatalities occur disproportionately in low-income and medium-income countries. Previous studies have concluded that risky behaviours were the main contributing factor of traffic crashes. Turn signal neglect (TSN) when making a turn is one of those risky behaviours. Unfortunately, research on TSN has been very limited. This study investigates the prevalence of TSN-related crashes and factors affecting TSN among motorcyclists and car drivers. Data was obtained from self-administered questionnaires conducted in Vietnam. The findings showed that 17.54% of motorcyclists and 14.76% of car drivers have experienced at least one crash caused by their failure to turn signals in the last three years. Additionally, fewer years having a riding/driving license, longer daily travelling time, lower frequency of turn signal use, and having received a fine due to TSN were found to be positively associated with TSN-related crashes for both motorcyclists and car drivers. The findings highlight the importance of TSN for road safety and the need for authorities to develop more effective educational strategies and to increase traffic law enforcement.

A matter of style? Testing the moderating effect of driving styles on the relationship between job strain and work-related crashes of professional drivers

Different empirical studies suggest that both job strain and driving styles are significant contributors to the work-related traffic crashes suffered by professional drivers. Nevertheless, the current evidence falls considerably short when explaining why driving styles may modify (or not) the relationship between occupational stressors and professional drivers’ safety outcomes. The aim of this study was to examine whether driving styles moderate the effect of job strain on professional drivers’ Work Traffic Crashes (WTCs). This research was performed using the data collected from a sample of 753 professional drivers, responding to a self-report questionnaire on job strain (work stress indicator of the Job Demand-Control model), driving styles and work-traffic safety outcomes suffered in the past two years. Regression-based moderation analyses suggest that the job strain of professional drivers is positively associated with the occupational traffic crashes they suffer, and that such association is stronger in drivers with “reckless & careless”, “anxious”, and “angry & hostile” driving styles. Meanwhile, the “patient & careful” (positive) driving style was not associated with a lower risk of suffering a WTC, nor with a lower vulnerability to stress-related WTCs. The results of this study support the hypothesis that driving styles exert a statistical moderation between the job strain and the occupational traffic crashes suffered by professional drivers. These findings may support the design of evidence-based interventions in both the organizational and individual levels, focused stress-related factors and driving styles as predictors of work traffic crashes.

Role of Passengers in Single-Vehicle Drunk-Driving Crashes: An Injury-Severity Analysis

Background: Drunk-driving is a major crash risk factor, and crashes resulting from this risky behavior tend to be serious and have significant economic and societal impacts. The presence of passengers and their demographics and activities can influence risky driving behaviors such as drunk-driving. However, passengers could either be an “enabling” factor to take more risks or could be an “inhibiting” factor by ensuring safe driving by a drunk-driver. Objective: This study examines whether the presence of passengers affects the contributing factors of single-vehicle (SV) drunk-driving crashes, by presenting a severity analysis of single- and multi-occupant SV drunk-driving crashes, to identify risk factors that contribute to crash severity outcomes, for the effective implementation of relevant countermeasures. Method: A total of 7407 observations for 2012–2016 from the crash database of the State of Alabama was used for this study. The variables were divided into six classes: temporal, locational, driver, vehicle, roadway, and crash characteristics and injury severities into three: severe, minor, and no injury. Two latent class multinomial logit models—one each for single- and multi-occupant crashes—were developed, to analyze the effects of significant factors on injury severity outcomes using marginal effects. Results: The estimated results show that collision with a ditch, run-off road, intersection, winter season, wet roadway, and interstate decreased the probability of severe injuries in both single- and multi-occupant crashes, whereas rural area, road with downward grade, dark and unlit roadway, unemployed driver, and driver with invalid license increased the likelihood of severe injuries for both single- and multi-occupant crashes. Female drivers were more likely to be severely injured in single-occupant crashes, but less likely in multi-occupant crashes. A significant association was found between severe injuries and weekends, residential areas, and crash location close (&lt;25 mi ≈40.23 km) to the residence of the at-fault driver in multi-occupant crashes. Sport utility vehicles were found to be safer when driving with passengers. Conclusions: The model findings show that, although many correlates are consistent between the single- and multi-occupant SV crashes that are associated with locational, roadway, vehicle, temporal, and driver characteristics, their effect can vary across the single- and multi-occupant driving population. The findings from this study can help in targeting interventions, developing countermeasures, and educating passengers to reduce drunk-driving crashes and consequent injuries. Such integrated efforts combined with engineering and emergency response may contribute in developing a true safe systems approach.

Impact of limited rest areas on truck driver crashes in Saskatchewan: a mixed-methods approach

BackgroundLong-haul truck drivers (LHTDs) suffer from long work hours often resulting in fatigue. Although several studies have reported that fatigue can contribute to crashes, no study has identified the location and patterns of fatigue-related crashes and solicited truck driver feedback on potential mitigation strategies. The purpose of this study is 1) to map the location of fatigue-related crashes and 2) examine the perceptions of truck drivers concerning fatigue-related crashes.MethodsUsing databases from the Saskatchewan Government Insurance, information on LHTD demographics, crashes and their causes, as well as crash location was analyzed. All fatigue-related crashes were then documented and mapped. Additionally, we interviewed 67 LHTDs (mean age = 53.0 ± 12.9; range 23–89; 95% were men) asking questions about fatigue, access to truck stops/rest areas, and provided recommendations for improvement. All interviews were subsequently analyzed using thematic analyses.ResultsOn average, there were 20 fatigue-related crashes per year over the 10-year period. Fatigue-related crashes were common across Saskatchewan, however, there was a concentration of crashes along major roadways between major cities. There was a significant association between crashes with age and experience. Despite many LHTDs being fatigued, there was a lack of truck stops/rest areas along highway routes. LHTDs suggested having access to truck stops/rest areas 250–400 km apart with running water and washrooms available.ConclusionsAdditional truck stops and rest areas are needed in Saskatchewan to ensure LHTDs have more opportunities for rest to reduce fatigue in general, as well as to reduce the risk of fatigue-related crashes.

Should airbags be deactivated for wheelchair-seated drivers?

Objective Field data have shown significant benefit from driver airbag for occupant protection in frontal crashes. However, vehicle modifiers almost always permanently deactivate airbags for wheelchair-seated drivers. The objective of this study was to conduct sled tests and computational simulations to answer whether driver airbags should be deactivated for drivers seated in wheelchairs. Methods Five sled tests were conducted under a 48-kph 20-g frontal crash pulse all with driver airbag. Seat-belt fit (good, poor, and unbelted), airbag deployment time (proper and late), and occupant size (midsize male and small female HIII ATDs) were varied in the tests. The 2006 Chrysler Town-and-Country minivan was selected as the nominal vehicle environment, and a surrogate wheelchair with a docking securement system was used for all sled tests. ATD injury measures for the head, neck, chest, and lower extremities were recorded in each test, and were used for validating a set of MADYMO models. Parametric studies with a total of 28 MADYMO simulations were then conducted to investigate the airbag effects on occupant injury risks with varied occupant size, belt fit, and impact angle (0, 15, and 30 deg). Results The sled tests showed potential safety concerns for wheelchair-seated drivers with a poor belt fit or without a belt. Specifically, the unbelted midsize male ATD sustained high femur forces and the small female ATD with poor belt fit sustained high chest deflections. The parametric simulations showed that airbag generally improved the protection for wheelchair-seated drivers. It is especially useful for unbelted wheelchair-seated drivers, and is also helpful for reducing the head and neck injury risks regardless of the belt condition, occupant size, and impact angle. Conclusions This is the first study using sled tests and computational simulations to investigate the effects of airbag deployment on injury risks of wheelchair-seated drivers. Overall, the results showed little basis for concern that the energy of deploying a driver airbag in today’s vehicles will cause serious-to-fatal injuries to drivers seated in wheelchairs. The results of this study therefore support the idea that driver airbags generally offer tangible safety benefits for a wide range of wheelchair-seated drivers in frontal crashes.

Comparing distance and time as driving exposure measures to evaluate fatal crash risk ratios

BackgroundThe use of an appropriate driving exposure measure is essential to calculate traffic crash rates and risks. Commonly used exposure measures include driving distance and the number of licensed drivers. These measures have some limitations, including the unavailability of disaggregated estimates for consecutive years, low data quality, and the failure to represent the driving population when the crash occurred. However, the length of driving time, available annually from the American Time Use Survey (ATUS), can be disaggregated by age, gender, time-of-day, and day-of week, and addresses the temporal discontinuity limitation of driving distance on the United States (U.S.) national scale. ObjectivesThe objective of this study is to determine if the length of driving time as a driving exposure measure is comparable to driving distance by comparing distance-based and time-based fatal crash risk ratios by driver age category, gender, time-of-day, and day-of-week. MethodsThe 2016–2017 National Household Travel Survey (NHTS) provided driving distance, and 2016–2017 Fatality Analysis Reporting System provided the number of drivers in fatal crashes. The distributions of driving distance and length of driving time by driver age category (16−24, 25−44, 45−64, and 65 years or older), gender, time-of-day, day-of-week were compared. Two negative binomial regression models were used to compute the distance-based and time-based fatal crash risk ratios. ResultsThe distributions of driving-distance were not different from the length-of-driving-time distributions by driver age category, gender, time-of-day, and day-of-week. Driving distance and the length of driving time provide similar fatal crash risk ratio estimates. ConclusionsThe length of driving time can be an alternative to driving distance as a measure of driving exposure. The primary advantage of driving time over driving distance is that, starting from 2003, the disaggregated estimates of the length of driving time are available from ATUS over consecutive years, curtailing the discontinuity limitation of driving distance. Furthermore, the length of driving time is related to drivers’ perceived risks about their driving conditions and as a result, may be a better exposure measure than driving distance in comparing crash risks between drivers whose likelihood of traveling in hazardous driving conditions (e.g., nighttime) varies substantially.

Crash Data-Based Investigation into How Injury Severity Is Affected by Driver Errors

Unsafe driving behaviors, driver limitations, and conditions that lead to a crash are usually referred to as driver errors. Even though driver errors are widely cited as a critical reason for crash occurrence in crash reports and safety literature, the discussion on their consequences is limited. This study aims to quantify the effect of driver errors on crash injury severity. To assist this investigation, driver errors were categorized as sequential events in a driving task. Possible combinations of driver error categories were created and ranked based on statistical dependences between error combinations and injury severity levels. Binary logit models were then developed to show that typical variables used to model injury severity such as driver characteristics, roadway characteristics, environmental factors, and crash characteristics are inadequate to explain driver errors, especially the complicated ones. Next, ordinal probit models were applied to quantify the effect of driver errors on injury severity for rural crashes. Superior model performance is observed when driver error combinations were modeled along with typical crash variables to predict the injury outcome. Modeling results also illustrate that more severe crashes tend to occur when the driver makes multiple mistakes. Therefore, incorporating driver errors in crash injury severity prediction not only improves prediction accuracy but also enhances our understanding of what error(s) may lead to more severe injuries so that safety interventions can be recommended accordingly.

A Longitudinal Investigation of Falls and Motor Vehicle Crashes in Older Drivers.

Objective: To assess the longitudinal association between fall history reported at a driver's license screening visit and the likelihood of subsequent vehicle crashes. Method: A total of 1,127 older adults were recruited from Maryland State Motor Vehicle Administration sites and interviewed annually over 15 years. Results: Individuals who reported a previous fall were more likely to be female, perform worse on physical functioning and divided attention tasks, and report more situational driving avoidance compared with non-fallers at baseline. Females who reported a fall at baseline had a 2.6× greater likelihood of subsequently reporting a crash over the 15 years than males. Among those who reported a fall at baseline, greater weekly driving exposure over the 15 years was associated with a 23% higher likelihood of a subsequent crash. Discussion: These findings support the utility of investigating nontraditional driver screening methods to identify drivers who may be at increased risk of future driving difficulties.