Naturalistic teenage driving study: Findings and lessons learned

IntroductionThis paper summarizes the findings on novice teenage driving outcomes (e.g., crashes and risky driving behaviors) from the Naturalistic Teenage Driving Study.MethodSurvey and driving data from a data acquisition system (global positioning system, accelerometers, cameras) were collected from 42 newly licensed teenage drivers and their parents during the first 18 months of teenage licensure; stress responsivity was also measured in teenagers.ResultOverall teenage crash and near-crash (CNC) rates declined over time, but were > 4 times higher among teenagers than adults. Contributing factors to teenage CNC rates included secondary task engagement (e.g., distraction), kinematic risky driving, low stress responsivity, and risky social norms.ConclusionsThe data support the contention that the high novice teenage CNC risk is due both to inexperience and risky driving behavior, particularly kinematic risky driving and secondary task engagement.Practical ApplicationsGraduated driver licensing policy and other prevention efforts should focus on kinematic risky driving, secondary task engagement, and risky social norms.     

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.     

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.     

Assessing driver behavior using shrp2 adverse weather data

IntroductionThis study explored how drivers adapt to inclement weather in terms of driving speed, situational awareness, and visibility as road surface conditions change from dry to slippery and visibility decreases. The proposed work mined existing data from the SHRP 2 NDS for drivers who were involved in weather-related crash and near-crash events. Baseline events were also mined to create related metadata necessary for behavioral comparisons. Methods: Researchers attempted, to the greatest extent possible, to match non-adverse-weather driving scenarios that are similar to the crash and near-crash event for each driver. The ideal match scenario would be at a day prior to the crash during non-adverse weather conditions having the same driver, at the same time of day, with the same traffic level on the same road on which the crash or near-crash occurred. Once the matched scenarios have been identified, a detailed analysis will be performed to determine how a driver’s behavior changed from normal driving to inclement-weather driving. Results: Data collected indicated that, irrespective of site location (i.e., state), most crashes and near-crashes occurred in rain, with only about 12% occurring in snowy conditions. Also, the number of near-crashes was almost double the number of crashes showing that many drivers were able to avoid a crash by executing an evasive maneuver such as braking or steering. Conclusions: Most types of near crashes were rear-end and sideswipe avoidance epochs, as the drivers may have had a difficult time merging or trying to change lanes due to low visibility or traffic. Hard braking combined with swerving were the most commonly used evasive maneuvers, occurring when drivers did not adjust their speeds accordingly for specific situations. Practical applications: Results from this study are expected to be utilized to educate and guide drivers toward more confident and strategic driving behavior in adverse weather.     

An improved vehicle-pedestrian near-crash identification method with a roadside LiDAR sensor

Problem: Potential conflicts between pedestrians and vehicles represent a challenge to pedestrian safety. Near-crash is used as a surrogate metric for pedestrian safety evaluations when historical vehicle–pedestrian crash data are not available. One challenge of using near-crash data for pedestrian safety evaluation is the identification of near-crash events. Method: This paper introduces a novel method for pedestrian-vehicle near-crash identification that uses a roadside LiDAR sensor. The trajectory of each road user can be extracted from roadside LiDAR data via several data processing algorithms: background filtering, lane identification, object clustering, object classification, and object tracking. Three indicators, namely, the post encroachment time (PET), the proportion of the stopping distance (PSD), and the crash potential index (CPI) are applied for conflict risk classification. Results: The performance of the developed method was evaluated with field-collected data at four sites in Reno, Nevada, United States. The results of case studies demonstrate that pedestrian-vehicle near-crash events could be identified successfully via the proposed method. Practical applications: The proposed method is especially suitable for pedestrian-vehicle near-crash identification at individual sites. The extracted near-crash events can serve as supplementary material to naturalistic driving study (NDS) data for safety evaluation.     

Peer influence predicts speeding prevalence among teenage drivers

Impact on IndustryPreventing speed-related crashes could reduce costs and improve efficiency in the transportation industry.ObjectiveThis research examined the psychosocial and personality predictors of observed speeding among young drivers.MethodSurvey and driving data were collected from 42 newly-licensed teenage drivers during the first 18 months of licensure. Speeding (i.e., driving 10 mph over the speed limit; about 16 km/h) was assessed by comparing speed data collected with recording systems installed in participants' vehicles with posted speed limits.ResultsSpeeding was correlated with elevated g-force event rates (r = 0.335, pb0.05), increased over time, and predicted by day vs. night trips, higher sensation seeking, substance use, tolerance of deviance, susceptibility to peer pressure, and number of risky friends. Perceived risk was a significant mediator of the association between speeding and risky friends.ConclusionThe findings support the contention that social norms may influence teenage speeding behavior and this relationship may operate through perceived risk.     

Driver speed selection and crash risk: Insights from the naturalistic driving study

IntroductionThis study investigates how speed limits affect driver speed selection, as well as the related crash risk, while controlling for various confounding factors such as traffic volumes and roadway geometry. Data from a naturalistic driving study are used to examine how driver speed selection varies among freeways with different posted speed limits, as well as how the likelihood of crash/near-crash events change with respect to mean speed and standard deviation.MethodRegression models are estimated to assess three measures of interest: the average speed of vehicles during the time preceding crash/near-crash and baseline (i.e., normal) driving events; the variation in travel speeds leading up to each event as quantified by the standard deviation in speeds over this period; and the probability of a specific event resulting in a crash/near-crash based on speed selection and other factors.ResultsSpeeds were relatively stable across levels-of-service A and B, within a range of 1.5 mph on average. Speeds were marginally lower (3.3 mph) on freeways posted at 65 mph versus 70 mph. In comparison, speeds were approximately 10.2 to 13.4 mph lower on facilities posted at 55 mph or 60 mph. Speeds were shown to be 2.5 mph lower in rainy weather and 11 mph lower under snow or sleet.ConclusionsSignificant correlation was observed with respect to speed selection behavior among the same individuals. Mean speeds are shown to increase with speed limits. However, these increases are less pronounced at higher speed limits. Drivers tend to reduce their travel speeds in presence of junctions and work zones, under adverse weather conditions, and particularly under heavy congestion. Crash risk increased with the standard deviation in speed, as well as on vertical curves and ramp junctions, and among the youngest and oldest age groups of drivers.     

Investigate moped-car conflicts in China using a naturalistic driving study approach

ProblemMopeds are a popular transportation mode in Europe and Asia. Moped-related traffic accidents account for a large proportion of crash fatalities. To develop moped-related crash countermeasures, it is important to understand the characteristics of moped-related conflicts.MethodNaturalistic driving study data were collected in Shanghai, China from 36 car drivers. The data included 2,878 h and 78,296 km driven from 13,149 trips. Moped-car conflicts were identified and examined from the passenger car driver's perspective using kinematic trigger algorithms and manual video reduction.ResultsA total of 119 moped-car conflicts were identified, including 74 high g-force conflicts and 45 low g-force events. These conflicts were classified into 22 on-road configurations where both similarities and differences were found as compared to Western Countries. The majority of the conflicts occurred on secondary main roads and branch roads. Hard braking was the primary response that the car drivers made to these conflicts rather than hard steering.DiscussionsThe identified on-road vehicle-moped conflict configurations in Shanghai, China may be attributed to the complicated traffic environment and risky behavior of moped riders. The lower prevalence of hard steering in Shanghai as compared to the United States may be due to the lower speeds at event onsets or less available steering space, e.g., less available shoulder area on Chinese urban roads.ConclusionsThe characteristics of moped-car conflicts may impact the design of active safety countermeasures on passenger cars. The pilot data from Shanghai urban areas suggest that countermeasures developed for China may require some modifications to those developed for the United States and European countries, although this recommendation may not be conclusive given the small sample size of the study. Future studies with large samples may help better understand the characteristics of moped-car conflicts.     

Developing and testing a hazard prediction task for novice drivers: A novel application of naturalistic driving videos

Introduction: Novice drivers’ inability to appropriately anticipate and respond to hazards has been implicated in their elevated crash risk. Our goal was to develop a driving hazard prediction task using naturalistic videos from the U.S. context that could distinguish between novice and experienced drivers. Method: Using the query builder from the SHRP 2 InSight Data Access Website, we identified a sample of 1034 videos for further review. Task criteria reduced these to 30 videos of near-crash events that were split into event and non-event segments and were used to develop the driving hazard prediction task (task). Participants, aged 16–20 years-old (22 novice and 19 experienced drivers) completed the task during which they watched event and non-event videos and were asked, “How likely was the driver of this car to get into a crash?” after each video. Overall ratings for hazardousness were calculated for experienced and novice drivers as well as a group difference score for hazardousness. Results: All participants rated event videos as more hazardous than non-event videos, but there was no main effect of group. Rather, there was a significant EventbyGroup interaction in which there were no group differences in hazard ratings for non-event videos, but experienced drivers rated event videos as more hazardous than novice drivers. Specific characteristics of the event videos, such as the hazard development period, were related to differences between novice and experienced drivers’ hazardousness ratings. Conclusion: To the best of our knowledge, this is the first use of naturalistic driving videos from an existing database as experimental stimuli. We found that the task discriminated between novice and experienced drivers’ ratings of hazardousness. This distinction suggests naturalistic driving videos may be viable stimuli for experimental studies. Practical Applications: The application of naturalistic driving video database for experimental research may hold promise.     

Driving context influences drivers' decision to engage in visual–manual phone tasks: Evidence from a naturalistic driving study

IntroductionVisual–manual (VM) phone tasks (i.e., texting, dialing, reading) are associated with an increased crash/near-crash risk. This study investigated how the driving context influences drivers' decisions to engage in VM phone tasks in naturalistic driving.MethodVideo-recordings of 1,432 car trips were viewed to identify VM phone tasks and passenger presence. Video, vehicle signals, and map data were used to classify driving context (i.e., curvature, other vehicles) before and during the VM phone tasks (N = 374). Vehicle signals (i.e., speed, yaw rate, forward radar) were available for all driving.ResultsVM phone tasks were more likely to be initiated while standing still, and less likely while driving at high speeds, or when a passenger was present. Lead vehicle presence did not influence how likely it was that a VM phone task was initiated, but the drivers adjusted their task timing to situations when the lead vehicle was increasing speed, resulting in increasing time headway. The drivers adjusted task timing until after making sharp turns and lane change maneuvers. In contrast to previous driving simulator studies, there was no evidence of drivers reducing speed as a consequence of VM phone task engagement.ConclusionsThe results show that experienced drivers use information about current and upcoming driving context to decide when to engage in VM phone tasks. However, drivers may fail to sufficiently increase safety margins to allow time to respond to possible unpredictable events (e.g., lead vehicle braking).Practical applicationsAdvanced driver assistance systems should facilitate and possibly boost drivers' self-regulating behavior. For instance, they might recognize when appropriate adaptive behavior is missing and advise or alert accordingly. The results from this study could also inspire training programs for novice drivers, or locally classify roads in terms of the risk associated with secondary task engagement while driving.     

Conscientious personality and young drivers' crash risk

Introduction: Personality characteristics are associated with many risk behaviors. However, the relationship between personality traits, risky driving behavior, and crash risk is poorly understood. The purpose of this study was to examine the association between personality, risky driving behavior, and crashes and near-crashes, using naturalistic driving research methods. Method: Participants' driving exposure, kinematic risky driving (KRD), high-risk secondary task engagement, and the frequency of crashes and near-crashes (CNC) were assessed over the first 18 months of licensure using naturalistic driving methods. A personality survey (NEO-Five Factor Inventory) was administered at baseline. The association between personality characteristics, KRD rate, secondary task engagement rate, and CNC rate was estimated using a linear regression model. Mediation analysis was conducted to examine if participants' KRD rate or secondary task engagement rate mediated the relationship between personality and CNC. Data were collected as part of the Naturalistic Teen Driving Study. Results: Conscientiousness was marginally negatively associated with CNC (path c = − 0.034, p = .09) and both potential mediators KRD (path a = − 0.040, p = .09) and secondary task engagement while driving (path a = − 0.053, p = .03). KRD, but not secondary task engagement, was found to mediate (path b = 0.376, p = .02) the relationship between conscientiousness and CNC (path c′ = − 0.025, p = .20). Conclusions: Using objective measures of driving behavior and a widely used personality construct, these findings present a causal pathway through which personality and risky driving are associated with CNC. Specifically, more conscientious teenage drivers engaged in fewer risky driving maneuvers, and suffered fewer CNC. Practical Applications: Part of the variability in crash risk observed among newly licensed teenage drivers can be explained by personality. Parents and driving instructors may take teenage drivers' personality into account when providing guidance, and establishing norms and expectations about driving.     

Naturalistic drive cycle synthesis for pickup trucks

ProblemFuture pick-up trucks are meeting much stricter fuel economy and exhaust emission standards. Design tradeoffs will have to be carefully evaluated to satisfy consumer expectations within the regulatory and cost constraints. Boundary conditions will obviously be critical for decision making: thus, the understanding of how customers are driving in naturalistic settings is indispensable. Federal driving schedules, while critical for certification, do not capture the richness of naturalistic cycles, particularly the aggressive maneuvers that often shape consumer perception of performance. While there are databases with large number of drive cycles, applying all of them directly in the design process is impractical. Therefore, representative drive cycles that capture the essence of the naturalistic driving should be synthesized from naturalistic driving data.MethodNaturalistic drive cycles are firstly categorized by investigating their micro-trip components, defined as driving activities between successive stops. Micro-trips are expected to characterize underlying local traffic conditions, and separate different driving patterns. Next, the transitions from one vehicle state to another vehicle state in each cycle category are captured with Transition Probability Matrix (TPM). Candidate drive cycles can subsequently be synthesized using Markov Chain based on TPMs for each category. Finally, representative synthetic drive cycles are selected through assessment of significant cycle metrics to identify the ones with smallest errors.SummaryThis paper provides a framework for synthesis of representative drive cycles from naturalistic driving data, which can subsequently be used for efficient optimization of design or control of pick-up truck powertrains.Impact on industryManufacturers will benefit from representative drive cycles in several aspects, including quick assessments of vehicle performance and energy consumption in simulations, component sizing and design, optimization of control strategies, and vehicle testing under real-world conditions. This is in contrast to using federal certification test cycles, which were never intended to capture pickup truck segment.     

Classifying and predicting risky driving among novice drivers: A group-based trajectory approach

Introduction: Classifying risky driving among new teenage drivers is important for efficiently targeting driving interventions. We thoroughly investigated whether novice drivers can be clustered by their driving outcome profiles over time. Methods: A sample of 51 newly licensed teen drivers was recruited and followed over a period of 20 weeks. An in-vehicle video recording system was used to gather data on dangerous driving events referred to as DDEs (elevated g-force, near-crash, and crash events), risky driving behaviors referred to as RDBs (e.g., running stop signs, cell phone use while driving), and miles traveled. The DDE and RDB weekly rates rate were determined by dividing the number of DDEs and RDBs in a week by the number of miles traveled in that week, respectively. Group-based trajectory modeling was used to map the clustering of DDE rate and RDB rate patterns over time and their associated covariates. Results: Two distinct DDE rate patterns were found. The first group (69.1% of the study population) had a lower DDE rate which was consistent over time. The second had a higher DDE rate pattern (30.9%) and characterized by a rising trend in DDE rate followed by a steady decrease (inverted U-shaped pattern). Two RDB rate patterns were also identified: a lower RDB rate pattern (83.4% of the study population) and a higher RDB rate pattern (16.6%). RDB and DDE rate patterns were positively related, and therefore, co-occurred. The results also showed that males were more likely than females to be in the higher DDE and RDB rate patterns. Conclusion: The groups identified by trajectory models may be useful for targeting driving interventions to teens that would mostly benefit as the different trajectories may represent different crash risk levels. Practical applications: Parents using feedback devices to monitor the driving performance of their teens can use the initial weeks of independent driving to classify their teens as low or high-risk drivers. Teens making a very few DDEs during their early weeks of independent driving are likely to remain in the lower risk group over time and can be spared from monitoring and interventions. However, teens making many DDEs during their initial weeks of unsupervised driving are likely to continue to make even more DDEs and would require careful monitoring and targeted interventions.     

Vehicle choices for teenage drivers: A national survey of U.S. parents

IntroductionPrevious research has shown that many newly licensed teenagers in the United States are driving vehicles with inferior crash protection. The objective of this study was to update and extend previous research on U.S. parents' choices of vehicles for their teenagers.MethodTelephone surveys were conducted with parents in May 2014 using a random sample of U.S. households likely to include teenagers. Participation was restricted to parents or guardians of teenagers who lived in the household and held either an intermediate or full driver's license. Parents were interviewed about the vehicle their teenager drives, the reason they chose the vehicle for their teenager, and the cost of purchased vehicles.ResultsTeenagers most often were driving 2000–06 model year vehicles (41%), with 30% driving a more recent model year and 19% driving an older model year. Teenagers most often were driving midsize or large cars (27%), followed by SUVs (22%), mini or small cars (20%), and pickups (14%). Far fewer were driving minivans (6%) or sports cars (1%). Forty-three percent of the vehicles driven by teenagers were purchased when the teenager started driving or later. A large majority (83%) were used vehicles. The median cost of the vehicles purchased was $5300, and the mean purchase price was $9751.ConclusionsAlthough parents report that the majority of teenagers are driving midsize or larger vehicles, many of these vehicles likely do not have key safety features, such as electronic stability control, which would be especially beneficial for teenage drivers. Many teenagers were driving older model year vehicles or vehicle types or sizes that are not ideal for novice drivers.Practical applicationsParents, and their teenage drivers, may benefit from consumer information about optimal vehicle choices for teenagers.     

Crash risk perception of sleepy driving and its comparisons with drink driving and speeding: Which behavior is perceived as the riskiest?

Objective: Driver sleepiness is a major crash risk factor but may be underrecognized as a risky driving behavior. Sleepy driving is usually rated as less of a road safety issue than more well-known risky driving behaviors, such as drink driving and speeding. The objective of this study was to compare perception of crash risk of sleepy driving, drink driving, and speeding.

Methods: Three hundred Australian drivers completed a questionnaire that assessed crash risk perceptions for sleepy driving, drink driving, and speeding. Additionally, the participants' perceptions of crash risk were assessed for 5 different contextual scenarios that included different levels of sleepiness (low, high), driving duration (short, long), and time of day/circadian influences (afternoon, nighttime) of driving.

Results: The analysis confirmed that sleepy driving was considered a risky driving behavior but not as risky as high levels of speeding (P < .05). Yet, the risk of crashing at 4 a.m. was considered as equally risky as low levels of speeding (10 km over the limit). The comparisons of the contextual scenarios revealed driving scenarios that would arguably be perceived as quite risky because time of day/circadian influences were not reported as high risk.

Conclusions: The results suggest a lack of awareness or appreciation of circadian rhythm functioning, particularly the descending phase of circadian rhythm that promotes increased sleepiness in the afternoon and during the early hours of the morning. Yet, the results suggested an appreciation of the danger associated with long-distance driving and driver sleepiness. Further efforts are required to improve the community's awareness of the impairing effects from sleepiness and, in particular, knowledge regarding the human circadian rhythm and the increased sleep propensity during the circadian nadir.     

Teens' distracted driving behavior: Prevalence and predictors

IntroductionTeen drivers' over-involvement in crashes has been attributed to a variety of factors, including distracted driving. With the rapid development of in-vehicle systems and portable electronic devices, the burden associated with distracted driving is expected to increase. The current study identifies predictors of secondary task engagement among teenage drivers and provides basis for interventions to reduce distracted driving behavior. We described the prevalence of secondary tasks by type and driving conditions and evaluated the associations between the prevalence of secondary task engagement, driving conditions, and selected psychosocial factors.MethodsThe private vehicles of 83 newly-licensed teenage drivers were equipped with Data Acquisition Systems (DAS), which documented driving performance measures, including secondary task engagement and driving environment characteristics. Surveys administered at licensure provided psychosocial measures.ResultsOverall, teens engaged in a potentially distracting secondary task in 58% of sampled road clips. The most prevalent types of secondary tasks were interaction with a passenger, talking/singing (no passenger), external distraction, and texting/dialing the cell phone. Secondary task engagement was more prevalent among those with primary vehicle access and when driving alone. Social norms, friends' risky driving behaviors, and parental limitations were significantly associated with secondary task prevalence. In contrast, environmental attributes, including lighting and road surface conditions, were not associated with teens' engagement in secondary tasks.ConclusionsOur findings indicated that teens engaged in secondary tasks frequently and poorly regulate their driving behavior relative to environmental conditions. Practical applications: Peer and parent influences on secondary task engagement provide valuable objectives for countermeasures to reduce distracted driving among teenage drivers.     

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.     

Tracking progress in teenage driver crash risk in the United States since the advent of graduated driver licensing programs

IntroductionThis study examined U.S. teenagers' crash rates since 1996, when the first graduated driver licensing (GDL) program in the United State was implemented.MethodsPassenger vehicle driver crash involvement rates for 16–19 and 30–59 (middle-aged) year-olds were examined, using data from the Fatality Analysis Reporting System, National Automotive Sampling System General Estimates System, Census Bureau, and National Household Travel Surveys.ResultsPer capita fatal and police-reported crash rates in 2012 were lower for 16 year-olds than for middle-aged drivers but older teenagers' rates were higher. Mileage-based fatal and police-reported crash rates in 2008 were higher for teenagers than for middle-aged drivers and higher for 16–17 year-olds than for older teenagers. In 1996–2012, teenagers' per capita fatal and police-reported crash rates declined sharply, especially for 16–17 year-olds, and more so than for middle-aged drivers. Substantial declines also occurred in teenagers' mileage-based fatal and police-reported crash rates from 1995–96 to 2008, generally more so than for middle-aged drivers. Regarding factors in fatal crashes in 1996 and 2012, proportions of young teenagers' crashes occurring at night and with multiple teenage passengers declined, more so than among older teenagers and middle-aged drivers. The proportion of fatally injured drivers who had been drinking declined for teenagers but changed little for middle-aged drivers. Improvements were not apparent in rates of driver errors or speeding among teenage drivers in fatal crashes.ConclusionsTeenage drivers' crash risk dropped during the period of implementation of GDL laws, especially fatal crash types targeted by GDL. However, teenagers' crash risk remains high, and important crash factors remain unaddressed by GDL.Practical applicationsAlthough this study was not designed to examine the role of GDL, the results are consistent with the increased presence of such laws. More gains are achievable if states strengthen their laws.     

Passenger effects on teenage driving and opportunities for reducing the risks of such travel

OBJECTIVES: To review the research evidence concerning the effects of passengers on teenage driving and crash involvement, and to explore ways to reduce negative effects. METHODS: Review of the international literature on these topics. RESULTS: Passenger presence increases crash risk for teenage drivers, especially when the passengers are other teenagers and especially when they are male. Female passengers do not have the same effects. Teenagers are more susceptible to peer influences than adults. The combination of passenger-induced distraction and driving inexperience can disrupt driving behavior, and there is evidence that teenage driver risk taking increases in vehicles with multiple teenagers. Possible ways to reduce the resulting crash problem include altering the in-vehicle behavior of teenagers or influencing their selection of travel partners. Legal restrictions on passengers with teenage drivers have been found effective in reducing the crash problem. Parental monitoring of teenage driving behavior, and programs aimed at teenagers themselves, could be other options but their efficacy is unproven. It currently is unknown why female passengers have a different effect than males or if that might offer clues about future interventions. CONCLUSIONS: Despite passenger restrictions in the majority of states, 42% of 16- and 17-year-old drivers in fatal crashes in 2005 were transporting teenagers with no adults in the vehicle; 61% of teenage passenger deaths (1,222 in 2005) occurred in vehicles driven by other teenagers. Wider application of passenger restrictions is indicated. IMPACT ON INDUSTRY: Ways to make passenger restrictions more effective are needed, and other techniques for reducing this major problem need development and testing.     

Characteristics of teens-with-teens fatal crashes in the United States, 2005–2010

BackgroundMore than 40% of fatal crashes of 16- and 17-year-old drivers occur when transporting teenagers. Characteristics of this predominant crash type and prevention possibilities are described, based on data from fatal crashes in the United States during 2005–2010.ResultsFifty-seven percent of 16- and 17-year old drivers in fatal crashes had at least one passenger. Most commonly, all passengers were ages 13–19 (42% of all drivers and 73% of those with passengers). Of fatal crashinvolved drivers with teenage passengers and no passengers of other ages, 56% had one passenger, 24% had two, and 20% had three or more. Most frequently, passengers were the same sex and within one year of the driver. Risk factors involving speeding, alcohol use, late-night driving, lack of a valid license, seat belt non-use, and crash responsibility were more prevalent with teenage passengers than when driving alone, and the prevalence of these factors increased with the number of teenage passengers. Many risk factors were most prevalent with passengers ages 20–29, although few crashes had this occupant configuration. Risk factors were least prevalent with a passenger 30 or older.DiscussionFatal crashes of 16- and 17-year-old drivers with teen passengers are a common crash scenario, despite passenger restrictions in 42 states and the District of Columbia during some or all of the study period. The proportion of these fatal crashes decreased slightly from 46% in 1995 (pre-GDL) to 43% in 2010 and showed no signs of decreasing during the six-year study period (range 41% to 43%).Practical applicationsExisting passenger restrictions are relatively weak and could be strengthened. Fatal crashes involving teen passengers, especially multiple passengers, are more likely to involve alcohol, late-night driving, driver error, and invalid licensure, so stepped-up enforcement of existing laws involving these behaviors might reduce the prevalence of such crashes.