Near crash characteristics among risky drivers using the SHRP2 naturalistic driving study

Problem: Previous research have focused extensively on crashes, however near crashes provide additional data on driver errors leading to critical events as well as evasive maneuvers employed to avoid crashes. The Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study contains extensive data on real world driving and offers a reliable methodology to study near crashes. The current study utilized the SHRP2 database to compare the rate and characteristics associated with near crashes among risky drivers. Methods: A subset from the SHRP2 database consisting of 4,818 near crashes for teen (16–19 yrs), young adult (20–24 yrs), adult (35–54 yrs), and older (70+ yrs) drivers was used. Near crashes were classified into seven incident types: rear-end, road departure, intersection, head-on, side-swipe, pedestrian/cyclist, and animal. Near crash rates, incident type, secondary tasks, and evasive maneuvers were compared across age groups. For rear-end near crashes, near crash severity, max deceleration, and time-to-collision at braking were compared across age. Results: Near crash rates significantly decreased with increasing age (p < 0.05). Young drivers exhibited greater rear-end (p < 0.05) and road departure (p < 0.05) near crashes compared to adult and older drivers. Intersection near crashes were the most common incident type among older drivers. Evasive maneuver type did not significantly vary across age groups. Near crashes exhibited a longer time-to-collision at braking (p < 0.01) compared to crashes. Summary: These data demonstrate increased total near crash rates among young drivers relative to adult and older drivers. Prevalence of specific near crash types also differed across age groups. Timely execution of evasive maneuvers was a distinguishing factor between crashes or near crashes. Practical Applications: These data can be used to develop more targeted driver training programs and help OEMs optimize ADAS to address the most common errors exhibited by risky drivers.     

Analysis of Driver Evasive Maneuvering Prior to Intersection Crashes Using Event Data Recorders

Objective: Intersection crashes account for over 4,500 fatalities in the United States each year. Intersection Advanced Driver Assistance Systems (I-ADAS) are emerging vehicle-based active safety systems that have the potential to help drivers safely navigate across intersections and prevent intersection crashes and injuries. The performance of an I-ADAS is expected to be highly dependent upon driver evasive maneuvering prior to an intersection crash. Little has been published, however, on the detailed evasive kinematics followed by drivers prior to real-world intersection crashes. The objective of this study was to characterize the frequency, timing, and kinematics of driver evasive maneuvers prior to intersection crashes.

Methods: Event data recorders (EDRs) downloaded from vehicles involved in intersection crashes were investigated as part of NASS-CDS years 2001 to 2013. A total of 135 EDRs with precrash vehicle speed and braking application were downloaded to investigate evasive braking. A smaller subset of 59 EDRs that collected vehicle yaw rate was additionally analyzed to investigate evasive steering. Each vehicle was assigned to one of 3 precrash movement classifiers (traveling through the intersection, completely stopped, or rolling stop) based on the vehicle's calculated acceleration and observed velocity profile. To ensure that any significant steering input observed was an attempted evasive maneuver, the analysis excluded vehicles at intersections that were turning, driving on a curved road, or performing a lane change. Braking application at the last EDR-recorded time point was assumed to indicate evasive braking. A vehicle yaw rate greater than 4° per second was assumed to indicate an evasive steering maneuver.

Results: Drivers executed crash avoidance maneuvers in four-fifths of intersection crashes. A more detailed analysis of evasive braking frequency by precrash maneuver revealed that drivers performing complete or rolling stops (61.3%) braked less often than drivers traveling through the intersection without yielding (79.0%). After accounting for uncertainty in the timing of braking and steering data, the median evasive braking time was found to be between 0.5 to 1.5 s prior to impact, and the median initial evasive steering time was found to occur between 0.5 and 0.9 s prior to impact. The median average evasive braking deceleration for all cases was found to be 0.58 g. The median of the maximum evasive vehicle yaw rates was found to be 8.2° per second. Evasive steering direction was found to be most frequently in the direction of travel of the approaching vehicle.

Conclusions: The majority of drivers involved in intersection crashes were alert enough to perform an evasive action. Most drivers used a combination of steering and braking to avoid a crash. The average driver attempted to steer and brake at approximately the same time prior to the crash.     

Analysis of effects of driver’s evasive action time on rear-end collision risk using a driving simulator

Introduction: Driver’s evasive action is closely associated with collision risk in a critical traffic event. To quantify collision risk, surrogate safety measures (SSMs) have been estimated using vehicle trajectories. However, vehicle trajectories cannot clearly capture presence and time of driver’s evasive action. Thus, this study determines the driver’s evasive action based on his/her use of accelerator and brake pedals, and analyzes the effects of the driver’s evasive action time (i.e., duration of evasive action) on rear-end collision risk. Method: Fifty drivers’ car-following behavior on a freeway was observed using a driving simulator. An SSM called “Deceleration Rate to Avoid Crash (DRAC)” and the evasive action time were determined for each driver using the data from the driving simulator. Each driver tested two traffic scenarios – Cars and Trucks scenarios where conflicting vehicles were cars and trucks, respectively. The factors related to DRAC were identified and their effects on DRAC were analyzed using the Generalized Linear Models and random effects models. Results: DRAC decreased with the evasive action time and DRAC was closely related to drivers’ gender and driving experience at the road sections where evasive action to avoid collision was required. DRAC was also significantly different between Cars and Trucks scenarios. The effect of the evasive action time on DRAC varied among different drivers, particularly in the Trucks scenario. Conclusions: Longer evasive action time can significantly reduce crash risk. Driver characteristics are more closely related to effective evasive action in complex driving conditions. Practical Applications: Based on the findings of this study, driver warning information can be developed to alert drivers to take specific evasive action that reduces collision risk in a critical traffic event. The information is likely to reduce the variability of the driver’s evasive action and the speed variations among different drivers.     

Analyses of rear-end crashes based on classification tree models

OBJECTIVE: Signalized intersections are accident-prone areas especially for rear-end crashes due to the fact that the diversity of the braking behaviors of drivers increases during the signal change. The objective of this article is to improve knowledge of the relationship between rear-end crashes occurring at signalized intersections and a series of potential traffic risk factors classified by driver characteristics, environments, and vehicle types. METHODS: Based on the 2001 Florida crash database, the classification tree method and Quasi-induced exposure concept were used to perform the statistical analysis. Two binary classification tree models were developed in this study. One was used for the crash comparison between rear-end and non-rear-end to identify those specific trends of the rear-end crashes. The other was constructed for the comparison between striking vehicles/drivers (at-fault) and struck vehicles/drivers (not-at-fault) to find more complex crash pattern associated with the traffic attributes of driver, vehicle, and environment. RESULTS: The modeling results showed that the rear-end crashes are over-presented in the higher speed limits (45-55 mph); the rear-end crash propensity for daytime is apparently larger than nighttime; and the reduction of braking capacity due to wet and slippery road surface conditions would definitely contribute to rear-end crashes, especially at intersections with higher speed limits. The tree model segmented drivers into four homogeneous age groups: < 21 years, 21-31 years, 32-75 years, and > 75 years. The youngest driver group shows the largest crash propensity; in the 21-31 age group, the male drivers are over-involved in rear-end crashes under adverse weather conditions and the 32-75 years drivers driving large size vehicles have a larger crash propensity compared to those driving passenger vehicles. CONCLUSIONS: Combined with the quasi-induced exposure concept, the classification tree method is a proper statistical tool for traffic-safety analysis to investigate crash propensity. Compared to the logistic regression models, tree models have advantages for handling continuous independent variables and easily explaining the complex interaction effect with more than two independent variables. This research recommended that at signalized intersections with higher speed limits, reducing the speed limit to 40 mph efficiently contribute to a lower accident rate. Drivers involved in alcohol use may increase not only rear-end crash risk but also the driver injury severity. Education and enforcement countermeasures should focus on the driver group younger than 21 years. Further studies are suggested to compare crash risk distributions of the driver age for other main crash types to seek corresponding traffic countermeasures.     

Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers

Introduction: Fatal crashes that include at least one fatality of an occupant within 30 days of the crash cause large numbers of injured persons and property losses, especially when a truck is involved. Method: To better understand the underlying effects of truck-driver-related characteristics in fatal crashes, a five-year (from 2012 to 2016) dataset from the Fatality Analysis Reporting System (FARS) was used for analysis. Based on demographic attributes, driving violation behavior, crash histories, and conviction records of truck drivers, a latent class clustering analysis was applied to classify truck drivers into three groups, namely, ‘‘middle-aged and elderly drivers with low risk of driving violations and high historical crash records,” ‘‘drivers with high risk of driving violations and high historical crash records,” and ‘‘middle-aged drivers with no driving violations and conviction records.” Next, equivalent fatalities were used to scale fatal crash severities into three levels. Subsequently, a partial proportional odds (PPO) model for each driver group was developed to identify the risk factors associated with the crash severity. Results' Conclusions: The model estimation results showed that the risk factors, as well as their impacts on different driver groups, were different. Adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights and various collision manners were significantly associated with the crash severities in all driver groups, whereas driving violation behaviors such as driving under the influence of alcohol or drugs, fatigue, or carelessness were significantly associated with the high-risk group only, and fewer risk factors and minor marginal effects were identified for the low-risk groups. Practical Applications: Corresponding countermeasures for specific truck driver groups are proposed. And drivers with high risk of driving violations and high historical crash records should be more concerned.     

Crashes and near-crashes on horizontal curves along rural two-lane highways: Analysis of naturalistic driving data

IntroductionPrior research has shown the probability of a crash occurring on horizontal curves to be significantly higher than on similar tangent segments, and a disproportionally higher number of curve-related crashes occurred in rural areas. Challenges arise when analyzing the safety of horizontal curves due to imprecision in integrating information as to the temporal and spatial characteristics of each crash with specific curves.MethodsThe second Strategic Highway Research Program(SHRP 2) conducted a large-scale naturalistic driving study (NDS),which provides a unique opportunity to better understand the contributing factors leading to crash or near-crash events. This study utilizes high-resolution behavioral data from the NDS to identify factors associated with 108 safety critical events (i.e., crashes or near-crashes) on rural two-lane curves. A case-control approach is utilized wherein these events are compared to 216 normal, baseline-driving events. The variables examined in this study include driver demographic characteristics, details of the traffic environment and roadway geometry, as well as driver behaviors such as in-vehicle distractions.ResultsLogistic regression models are estimated to discern those factors affecting the likelihood of a driver being crash-involved. These factors include high-risk behaviors, such as speeding and visual distractions, as well as curve design elements and other roadway characteristics such as pavement surface conditions.ConclusionsThis paper successfully integrated driver behavior, vehicle characteristics, and roadway environments into the same model. Logistic regression model was found to be an effective way to investigate crash risks using naturalistic driving data.Practical ApplicationsThis paper revealed a number of contributing factors to crashes on rural two-lane curves, which has important implications in traffic safety policy and curve geometry design. This paper also discussed limitations and lessons learned from working with the SHRP 2 NDS data. It will benefit future researchers who work with similar type of data.     

Factors contributing to commercial vehicle rear-end conflicts in China: A study using on-board event data recorders

Introduction: In the last 30 years, China has undergone a dramatic increase in vehicle ownership and a resulting escalation in the number of road crashes. Although crash figures are decreasing today, they remain high; it is therefore important to investigate crash causation mechanisms to further improve road safety in China. Method: To shed more light on the topic, naturalistic driving data was collected in Shanghai as part of the evaluation of a behavior-based safety service. The data collection included instrumenting 47 vehicles belonging to a commercial fleet with data acquisition systems. From the overall sample, 91 rear-end crash or near-crash (CNC) events, triggered by 24 drivers, were used in the analysis. The CNC were annotated by three researchers, through an expert assessment methodology based on videos and kinematic variables. Results: The results show that the main factor behind the rear-end CNC was the adoption of very small safety margins. In contrast to results from previous studies in the US, the following vehicles' drivers typically had their eyes on the road and reacted quickly in response to the evolving conflict in most events. When delayed reactions occurred, they were mainly due to driving-related visual scanning mismatches (e.g., mirror checks) rather than visual distraction. Finally, the study identified four main conflict scenarios that represent the typical development of rear-end conflicts in this data. Conclusions: The findings of this study have several practical applications, such as informing the specifications of in-vehicle safety measures and automated driving and providing input into the design of coaching/training procedures to improve the driving habits of drivers.     

Does longevity impact the severity of traffic crashes? A comparative study of young-older and old-older drivers

Introduction: This article analyzes the effect of driver’s age in crash severity with a particular focus on those over the age of 65. The greater frequency and longevity of older drivers around the world suggests the need to introduce a possible segmentation within this group at risk, thus eliminating the generic interval of 65 and over as applied today in road safety data and in the automobile insurance sector. Method: We investigate differences in the severity of traffic crashes among two subgroups of older drivers –young-older (65–75) and old-older (75+), and findings are compared with the age interval of drivers under 65. Here, we draw on data for 2016 provided by Spanish Traffic Authority. Parametric and semi-parametric regression models are applied. Results: We identified the factors related to the crash, vehicle, and driver that have a significant impact on the probability of the crash being slight, serious, or fatal for the different age groups. Conclusions: We found that crash severity and the expected costs of crashes significantly increase when the driver is over the age of 75. Practical Applications: Our results have obvious implications for regulators responsible for road safety policies – most specifically as they consider there should be specific driver licensing requirements and driving training for elderly – and for the automobile insurance industry, which to date has not examined the impact that the longevity of drivers is likely to have on their balance sheets.     

The effect of extending graduated driver licensing to older novice drivers in Indiana

Introduction: Graduated driver licensing (GDL) systems have been shown to reduce rates of crashes, injuries, and deaths of young novice drivers. However, approximately one in three new drivers in the United States obtain their first driver’s license at age 18 or older, and thus are exempt from most or all provisions of GDL in most states. Method: In July 2015, the state of Indiana updated its GDL program, extending its restrictions on driving at night and on carrying passengers during the first 6 months of independent driving, previously only applicable to new drivers younger than 18, to all newly-licensed drivers younger than 21 years of age. The current study examined monthly rates of crashes per licensed driver under the affected conditions (driving at night and driving with passengers) among Indiana drivers first licensed at ages 18, 19, and 20 under the updated GDL system compared with drivers licensed at the same ages under the previous GDL system. We used Poisson regression to estimate the association between the GDL system and crash rates, while attempting to control for other factors that might have also influenced crash rates. We used linear regression to estimate the association between the GDL system and the proportion of all crashes that occurred under conditions restricted by the GDL program. Results: Results showed, contrary to expectations, that rates of crashes during restricted nighttime hours and with passengers were higher among drivers licensed under the updated GDL system. This mirrored a statewide increase in crash rates among drivers of all ages over the study period and likely reflected increased overall driving exposure. The proportions of all crashes that were at night or with passengers did not change. Practical Applications: More research is needed to understand how older novice drivers respond when GDL systems originally designed for younger novice drivers are applied to them.     

Crash risk by driver age,gender, and time of day using a new exposure methodology

Introduction: Concerns have been raised that the nonlinear relation between crashes and travel exposure invalidates the conventional use of crash rates to control for exposure. A new metric of exposure that bears a linear association to crashes was used as basis for calculating unbiased crash risks. This study compared the two methods – conventional crash rates and new adjusted crash risk – for assessing the effect of driver age, gender, and time of day on the risk of crash involvement and crash fatality. Method: We used police reports of single-car and multi-car crashes with fatal and nonfatal driver injuries that occurred during 2002–2012 in Great Britain. Results: Conventional crash rates were highest in the youngest age group and declined steeply until age 60–69 years. The adjusted crash risk instead peaked at age 21–29 years and reduced gradually with age. The risk of nighttime driving, especially among teenage drivers, was much smaller when based on adjusted crash risks. Finally, the adjusted fatality risk incurred by elderly drivers remained constant across time of day, suggesting that their risk of sustaining a fatal injury due to a crash is more attributable to excess fragility than to crash seriousness. Conclusions: Our findings demonstrate a biasing effect of low travel exposure on conventional crash rates. This implies that conventional methods do not yield meaningful comparisons of crash risk between driver groups and driving conditions of varying exposure to risk. The excess crash rates typically associated with teenage and elderly drivers as well as nighttime driving are attributed in part to overestimation of risk at low travel exposure. Practical Applications: Greater attention should be directed toward crash involvement among drivers in their 20s and 30s as well as younger drivers. Countermeasures should focus on the role of physical vulnerability in fatality risk of elderly drivers.     

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.     

Older drivers’ involvement in fatal RTCs. Do crashes fatal to them differ from crashes involving them but fatal to others?

In several countries, older drivers are disproportionately involved in fatal road traffic crashes (RTCs) for various reasons. This study maps the circumstances of occurrence of crashes involving older drivers that are fatal to either them or other road users and highlights differences between them. Sweden’s national in-depth studies of fatal RTCs archive was used and focus was placed on crashes in which a driver aged 65 years or older was involved between 2002 and 2004 (n = 197). Thirteen driver and crash characteristics were analyzed simultaneously and typical crash patterns (classes) were highlighted. For each pattern, the proportions of crashes fatal to the older driver vs. to someone else were compared. Four patterns were identified: (1) crashes on low-speed stretches, involving left turn and intersections; (2) crashes involving very old drivers and older vehicles, (3) rear-end collisions on high-speed stretches; and (4) head-on and single-vehicle crashes in rural areas. Older drivers dying in the crash were over-represented in classes 2 and 4. The study shows that when older drivers are involved in fatal RTCs, they are often the ones who die (60%). Typical circumstances surrounding their involvement include manoeuvring difficulties, fast-moving traffic, and colliding in an old vehicle. Preventing fatal RTCs involving older drivers requires not only age-specific but also general measures.     

Investigation of injury severities in single-vehicle crashes in North Carolina using mixed logit models

IntroductionRoadway departure (RwD) crashes, comprising run-off-road (ROR) and cross-median/centerline head-on collisions, are one of the most lethal crash types. According to the FHWA, between 2015 and 2017, an average of 52 percent of motor vehicle traffic fatalities occurred each year due to roadway departure crashes. An avoidance maneuver, inattention or fatigue, or traveling too fast with respect to weather or geometric road conditions are among the most common reasons a driver leaves the travel lane. Roadway and roadside geometric design features such as clear zones play a significant role in whether human error results in a crash. Method: In this paper, we used mixed-logit models to investigate the contributing factors on injury severity of single-vehicle ROR crashes. To that end, we obtained five years' (2010–2014) of crash data related to roadway departures (i.e., overturn and fixed-object crashes) from the Federal Highway Administration's Highway Safety Information System Database. Results: The results indicate that factors such as driver conditions (e.g., age), environmental conditions (e.g., weather conditions), roadway geometric design features (e.g., shoulder width), and vehicle conditions significantly contributed to the severity of ROR crashes. Conclusions: Our results provide valuable information for traffic design and management agencies to improve roadside design policies and implementing appropriately forgiving roadsides for errant vehicles. Practical applications: Our results show that increasing shoulder width and keeping fences at the road can reduce ROR crash severity significantly. Also, increasing road friction by innovative materials and raising awareness campaigns for careful driving at daylight can decrease the ROR crash severity.     

The role of off-path glances: A quantitative analysis of rear-end conflicts involving Chinese professional truck drivers as the striking partners

Introduction: Rear-end crashes are one of the most frequent crash types in China, leading to significant economic and societal losses. The development of active safety systems – such as Automatic Emergency Braking System (AEBS) – could avoid or mitigate the consequences of these crashes in Chinese traffic situations. However, a clear understanding of the crash causation mechanisms is necessary for the design of these systems. Method: Manually coded variables were extracted from a naturalistic driving study conducted with commercial vehicles in Shanghai. Quantitative analyses of rear-end crashes and near crashes (CNC) were conducted to assess the prevalence, duration, and location of drivers’ off-path glances, the influence of lead vehicle brake lights on drivers’ last off-path glance, and driver brake onset, and the influence of off-path glances and kinematic criticality on drivers’ response to conflicts. Results: The results indicate that the Chinese truck drivers in our study rarely engage in distracting activities involving a phone or other handheld objects while driving. Instead, they direct their off-path glances mainly toward the mirrors, and the duration of off-path glances leading to critical situations are shorter compared to earlier analyses performed in Western countries. The drivers also often keep small margins. Conclusions: Overall, the combination of short time headway with off-path glances directed toward the mirror originates visual mismatches which, associated to a rapid change in the kinematic situation, cause the occurrence of rear-end CNC. When drivers look back toward the road after an off-path glance, a fast response seems to be triggered by lower values of looming compared to previous studies, possibly because of the short time headways. Practical Application: The results have practical implications for the development of driver models, for the design of active safety systems and automated driving, and for the design of campaigns promoting safe driving.     

Creation of the Naturalistic Engagement in Secondary Tasks (NEST) distracted driving dataset

ProblemDistracted driving has become a topic of critical importance to driving safety research over the past several decades. Naturalistic driving data offer a unique opportunity to study how drivers engage with secondary tasks in real-world driving; however, the complexities involved with identifying and coding relevant epochs of naturalistic data have limited its accessibility to the general research community.MethodThis project was developed to help address this problem by creating an accessible dataset of driver behavior and situational factors observed during distraction-related safety-critical events and baseline driving epochs, using the Strategic Highway Research Program 2 (SHRP2) naturalistic dataset. The new NEST (Naturalistic Engagement in Secondary Tasks) dataset was created using crashes and near-crashes from the SHRP2 dataset that were identified as including secondary task engagement as a potential contributing factor. Data coding included frame-by-frame video analysis of secondary task and hands-on-wheel activity, as well as summary event information. In addition, information about each secondary task engagement within the trip prior to the crash/near-crash was coded at a higher level. Data were also coded for four baseline epochs and trips per safety-critical event.Results1,180 events and baseline epochs were coded, and a dataset was constructed. The project team is currently working to determine the most useful way to allow broad public access to the dataset.DiscussionWe anticipate that the NEST dataset will be extraordinarily useful in allowing qualified researchers access to timely, real-world data concerning how drivers interact with secondary tasks during safety-critical events and baseline driving.Practical applicationsThe coded dataset developed for this project will allow future researchers to have access to detailed data on driver secondary task engagement in the real world. It will be useful for standalone research, as well as for integration with additional SHRP2 data to enable the conduct of more complex research.     

Driver licensing,motor-vehicle crashes,and moving violations among older adults

IntroductionDriving is important for well-being among older adults, but age-related conditions are associated with driving reduction or cessation and increased crash risk for older drivers. Our objectives were to describe population-based rates of older drivers’ licensing and per-driver rates of crashes and moving violations.Methods: We examined individual-level statewide driver licensing, crash, and traffic citation data among all New Jersey drivers aged ≥ 65 and a 35- to 54-year-old comparison group during 2010–2014. Rate ratios (RR) of crashes and moving violations were estimated using Poisson regression.Results: Overall, 86% of males and 71% of females aged ≥ 65 held a valid driver’s license. Older drivers had 27% lower per-driver crash rates than middle-aged drivers (RR: 0.73, 95% CI: 0.73, 0.74)—with appreciable differences by sex—but 40% higher fatal crash rates (RR: 1.40 [1.24, 1.58]). Moving violation rates among older drivers were 72% lower than middle-aged drivers (RR: 0.28 [0.28, 0.28]).Conclusion: The majority of older adults are licensed, with substantial variation by age and sex. Older drivers have higher rates of fatal crashes but lower rates of moving violations compared with middle-aged drivers.Practical applications: Future research is needed to understand the extent to which older adults drive and to identify opportunities to further reduce risk of crashes and resultant injuries among older adults.     

Potential effects of lowering the BAC limit on injuries,fatalities, and costs

IntroductionPotential health and cost impacts of lowering the BAC limit for U.S. drivers below .08% were explored through analyses of reductions in crash incidence, injury severity, and costs based on five scenarios with varying assumptions about how the change to a .05% BAC limit might affect alcohol-impaired driving.MethodsDistribution of crashes by injury level and highest driver or non-occupant BAC levels for 2010, together with unit crash costs provided a base for comparison. Scenario 1 assumed all alcohol-impaired driving ceased; scenario 2 assumed all drivers obeyed the law, and scenario 3 assumed decreases in driver BAC levels would be limited to those who had been driving near the legal limit before the change. Scenario 4 was based on changes in driver BAC levels associated with a 08% to .05% BAC limit change in Australia, and scenario 5 was based on changes in alcohol-related crashes associated with the change to the .08% BAC limit in the United States. The number of casualties prevented in each scenario was estimated using relative risks of crash involvement, and changes in societal costs were estimated using the unit costs.ResultsReductions ranging from 71% to 99% in fatalities, injuries, and costs related to alcohol-impaired driving were estimated in scenarios 1 and 2. Scenarios 3–5 produced smaller reductions ranging from 4% to 16% for alcohol-impaired fatalities, injuries, and costs.ConclusionThe wide difference between the outcomes of the two sets of scenarios reflects the sensitivity of BAC policy benefits to driver compliance behavior.Practical applicationThe quantification of the reduction in the number and costs of traffic crash casualties in the set of behavioral scenarios explored in this research can inform policymakers about the extent and limits of benefits achievable by lowering the BAC limits as they consider strategies to reduce alcohol-impaired driving.     

Characteristics of crashes and injuries among 14 and 15 year old drivers,by rurality

Purpose: Motor-vehicle crashes continue to be the leading cause of death for teenagers in the United States. The United States has some of the youngest legal driving ages worldwide. The objective of this study was to determine rates and factors associated with injury crashes among 14- and 15-year-old drivers and how these varied by rurality. Methods: Data for this cross-sectional study of 14- and 15-year-old drivers were obtained from the Iowa Department of Transportation from 2001 to 2013. Crash and injury crash rates were calculated by rurality. The relationship between crash and driver factors and injury was assessed using logistic regression. Findings: Teen drivers, aged 14 and 15 years, had a statewide crash rate of 8 per 1,000 drivers from 2001 to 2013. The majority of crashes occurred in urban areas (51%), followed by in town (29%), remote rural areas (13%), and suburban areas (7%). Crash and injury crash rates increased as level of rurality increased. The odds of an injury crash increased more than 10-fold with the presence of multiple other teens as passengers, compared to no passengers (OR = 10.7, 95% CI: 7.1–16.2). Conclusions: Although 14- and 15-year-old drivers in Iowa have either limited unsupervised (school permits) or supervised only driving restrictions, they are overrepresented in terms of crashes and injury crashes. Rural roads and multiple teen passengers are particularly problematic in terms of injury outcomes. Practical applications: Results from this study support passenger restrictions and teen driving interventions designed with a rural focus.     

Combinations of factors contribute to young driver crashes

Introduction: Motor-vehicle crashes are a leading cause of death in adolescence and young adults. A multitude of factors, including skill level, inexperience, and risk taking behaviors are associated with young drivers’ crashes. This research investigated whether combinations of factors underlie crashes involving young drivers. Method: A retrospective longitudinal study was conducted on population-wide one- and two-car crashes in Great Britain during years 2005–2012 per driver age (17–20, 21–29, 30–39, 40–49) and sex. Reporting officers provided their assessment of the factors contributing to crashes. Principal components analysis was conducted to identify combinations of factors underlying young drivers’ crashes. Factor combinations, including challenging driving conditions, risk taking behaviors, and inexperience were implicated in young drivers’ crashes. Results: Combinations of factors reveal new insights into underlying causes of crashes involving young drivers. One combination revealed that slippery roads due to poor weather pose greater risk to young drivers who are inexperienced and likely to exceed the appropriate speed. The findings motivate new policy recommendations, such as educating young drivers about the importance of adjusting their speed to the road conditions.     

Multi-agent traffic simulations to estimate the impact of automated technologies on safety

Abstract

Objective: The objective of this article was to develop a multi-agent traffic simulation methodology to estimate the potential road safety improvements of automated vehicle technologies.

Methods: We developed a computer program that merges road infrastructure data with a large number of vehicles, drivers, and pedestrians. Human errors are induced by modeling inattention, aimless driving, insufficient safety confirmation, misjudgment, and inadequate operation. The program was applied to simulate traffic in a prescribed area in Tsukuba city. First, a 100% manual driving scenario was set to simulate traffic for a total preset vehicle travel distance. The crashes from this simulation were compared with real-world crash data from the prescribed area from 2012 to 2017. Thereafter, 4 additional scenarios of increasing levels of automation penetration (including combinations of automated emergency braking [AEB], lane departure warning [LDW], and SAE Level 4 functions) were implemented to estimate their impact on safety.

Results: Under manual driving, the system simulated a total of 859 crashes including single-car lane departure, car-to-car, and car-to-pedestrian crashes. These crashes tended to occur in locations similar to real-world crashes. The number of crashes predicted decreased to 156 cases with increasing level of automation. All of the technologies considered contributed to the decrease in crashes. Crash reductions attributable to AEB and LDW in the simulations were comparable to those reported in recent field studies. For the highest levels of automation, no assessment data were available and hence the results should be carefully treated. Further, in modeling automated functions, potentially negative aspects such as sensing failure or human overreliance were not incorporated.

Conclusions: We developed a multi-agent traffic simulation methodology to estimate the effect of different automated vehicle technologies on safety. The crash locations resulting from simulations of manual driving within a limited area in Japan were preliminary assessed by comparison with real-world crash data collected in the same area. Increasing penetration levels of AEB and LDW led to a large reduction in both the frequency and severity of rear-end crashes, followed by car-to-car head-on crashes and single-vehicle lane departure crashes. Preliminary estimations of the potential safety improvements that may be achieved with highly automated driving technologies were also obtained.