Near-crash Events Research Articles

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 30years, 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.

Performance of basic kinematic thresholds in the identification of crash and near-crash events within naturalistic driving data

Understanding causal factors for traffic safety-critical events (e.g., crashes and near-crashes) is an important step in reducing their frequency and severity. Naturalistic driving data offers unparalleled insight into these factors, but requires identification of situations where crashes are present within large volumes of data. Sensitivity and specificity of these identification approaches are key to minimizing the resources required to validate candidate crash events. This investigation used data from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) and the Canada Naturalistic Driving Study (CNDS) to develop and validate different kinematic thresholds that can be used to detect crash events. Results indicate that the sensitivity of many of these approaches can be quite low, but can be improved by selecting particular threshold levels based on detection performance. Additional improvements in these approaches are possible, and may involve leveraging combinations of different detection approaches, including advanced statistical techniques and artificial intelligence approaches, additional parameter modifications, and automation of validation processes.

In-vehicle stereo vision system for identification of traffic conflicts between bus and pedestrian

Abstract The traffic conflict technique (TCT) was developed as “surrogate measure of road safety” to identify near-crash events by using measures of the spatial and temporal proximity of road users. Traditionally applications of TCT focus on a specific site by the way of manually or automated supervision. Nowadays the development of in-vehicle (IV) technologies provides new opportunities for monitoring driver behavior and interaction with other road users directly into the traffic stream. In the paper a stereo vision and GPS system for traffic conflict investigation is presented for detecting conflicts between vehicle and pedestrian. The system is able to acquire geo-referenced sequences of stereo frames that are used to provide real time information related to conflict occurrence and severity. As case study, an urban bus was equipped with a prototype of the system and a trial in the city of Catania (Italy) was carried out analyzing conflicts with pedestrian crossing in front of the bus. Experimental results pointed out the potentialities of the system for collection of data that can be used to get suitable traffic conflict measures. Specifically, a risk index of the conflict between pedestrians and vehicles is proposed to classify collision probability and severity using data collected by the system. This information may be used to develop in-vehicle warning systems and urban network risk assessment.

High risk of near-crash driving events following night-shift work

Night-shift workers are at high risk of drowsiness-related motor vehicle crashes as a result of circadian disruption and sleep restriction. However, the impact of actual night-shift work on measures of drowsiness and driving performance while operating a real motor vehicle remains unknown. Sixteen night-shift workers completed two 2-h daytime driving sessions on a closed driving track at the Liberty Mutual Research Institute for Safety: (i) a postsleep baseline driving session after an average of 7.6 ± 2.4 h sleep the previous night with no night-shift work, and (ii) a postnight-shift driving session following night-shift work. Physiological measures of drowsiness were collected, including infrared reflectance oculography, electroencephalography, and electrooculography. Driving performance measures included lane excursions, near-crash events, and drives terminated because of failure to maintain control of the vehicle. Eleven near-crashes occurred in 6 of 16 postnight-shift drives (37.5%), and 7 of 16 postnight-shift drives (43.8%) were terminated early for safety reasons, compared with zero near-crashes or early drive terminations during 16 postsleep drives (Fishers exact: P = 0.0088 and P = 0.0034, respectively). Participants had a significantly higher rate of lane excursions, average Johns Drowsiness Scale, blink duration, and number of slow eye movements during postnight-shift drives compared with postsleep drives (3.09/min vs. 1.49/min; 1.71 vs. 0.97; 125 ms vs. 100 ms; 35.8 vs. 19.1; respectively, P < 0.05 for all). Night-shift work increases driver drowsiness, degrading driving performance and increasing the risk of near-crash drive events. With more than 9.5 million Americans working overnight or rotating shifts and one-third of United States commutes exceeding 30 min, these results have implications for traffic and occupational safety.

Enhancing Our Understanding of Teen-Driver Crashes

Whileprogresshasbeenmade in reducing thenumberof teens killedinmotorvehiclecrashes,crashescontinuetobetheleading causeofdeathfor teens intheUnitedStates.1Thehighcrashrate for teens isowing in largepart to theirlackofdrivingexperience.2,3 Continued identificationof specific characteristicsof teens that leadtoelevatedcrashriskcan informinterventions forhigh-risk subgroups.Thismonth’sissueofJAMAPediatrics includesastudy byOuimetetal4 thatexaminedtheassociationbetweencortisol reactivity and subsequent occurrence of crash and near-crash (CNC) events among newly licensed teens. As part of theNaturalisticTeenDrivingStudy,salivarycortisolreactivity inresponse toa stress-inducing taskwasassessedatbaseline ina sampleof 42 healthy, typically developing 16-year-old participants, after which they drove instrumented vehicles for 18months. Ouimetetal4 foundthat teenswith lowercortisol reactivity in response to the stressorhadhigherCNC ratesduring the first 18monthsof licensure. Inaddition, thosewith lowercortisol reactivityhadaslowerdecreaseinCNCratesovertime.Theauthors positedthattheassociationsobservedintheirstudycouldbeowingtoindividualdifferencesinoptimalphysiologicalarousaland/ or tothemannerbywhichteensprocessemotional information. The extent to which the authors believe these are competing, complementary,or interactiveprocesses remainsunclear.Most compelling,andsupportedbyotherliterature, istheideaofanormativeemotional learningpathway,whereby individualdifferences in hypothalamic-pituitary-adrenal (HPA) axis reactivity affect thesensitivityofdrivers to stimuli in thedrivingenvironment,whichsubsequentlyaffectslearning.5Forexample,asteens gain independent driving experience, they are exposed to a varietyofemotionallyevocativestimuli—suchasnearmisseswith unexpected hazards—which enables them to better anticipate, avoid, and react more effectively in the future. In this hypothesizedpathway, theemotional response (as indicatedbygreater cortisol reactivity) to stressorsandchallengesexperiencedduring the learning-to-driveprocesspromoteseffective learningof necessaryskills.ThefindingsbyOuimetetal4wouldsuggestthat, althoughall teensgainexperienceover timeandthisexperience is associatedwithdecreasingCNCs, individual variation in cortisol reactivity—specificallyaweakeremotional reactivity in responsetohazards—mightattenuate theassociationbetweenexperienceandlowercrashrisk.Furthersupport for theemotional learninghypothesis among teendrivers isprovidedbyprior experimental research. In a small convenience sample of experienced and novice drivers, Kinnear et al6 found that themagnitude of skin conductance response, a physiologicalmeasure of autonomic arousal, to simulateddrivinghazards increased as a function of real-world driving experience. Implications for Researchers Themost immediate implicationof the findingsof theOuimet et al4 study is for continued research tobetter characterize the relationship between cortisol reactivity in response to stressors and crash risk in the general population of healthy teens and among those teenswhomight be at higher crash risk owing to preexisting conditions or history of risky behaviors. For example, variation in function of the HPA axis has been implicated in clinical samples of youthas apotential factor in the development of antisocial disorders, aswell as a variety of internalizing and externalizing disorders.7-9 Although thecurrent studybenefited fromstrongdata collection methods, generalizations are difficult to make owing to the small and likely nonrepresentative sample of healthy, typically developing teens; very few actual crash events; and limited characterization of the near crashes. It remains unclear what proportion of novice teen-driver crashesmight be explained by individual differences in HPA regulation. Previous research has suggested that most crashes involving teen drivers aredue to inexperienceandnotovert risk takingon the part of teens,2,3 lending additional support to an emotional learning pathway. Further research is needed to explicate the mechanisms by which individual differences in the HPA systemcovarybydevelopmental factors, driving experience, and other individual difference variables (eg, attention-deficit/ hyperactivity disorder and conduct disorder) to affect driving behaviors and outcomes.

Near-Crash Identification in a Connected Vehicle Environment

The main objective of this study was to identify near crashes in vehicle trajectory data with interdriver heterogeneity and situation dependency considered. Several efforts have been made to evaluate the effects of near-crash events on safety with the use of naturalistic driving data, driving simulators, and test tracks. However, these efforts have faced some challenges because the observations reflected only the equipped vehicles. The development of connected vehicle technology provided the essential data to study high-risk maneuvers in the entire traffic stream. In this study, two near-crash detection algorithms were proposed. One algorithm had its basis in fixed thresholds, while the other considered interdriver heterogeneity and estimates driver-specific thresholds. The models were tested against two NGSIM trajectory data sets. Initial results showed that consideration of driver preferences resulted in more realistic identification of near crashes than otherwise.

Studying the Effects of Driver Distraction and Traffic Density on the Probability of Crash and Near-Crash Events in Naturalistic Driving Environment

Driver distraction detection and intervention are important for designing modern driver-assistance systems and for improving safety. The main research question of this paper is to investigate how the cumulative driver off-road glance duration can be controlled to reduce the probability of occurrences of crash and near-crash events. Based on the available data sets from the Virginia Tech Transportation Institute (VTTI) 100-car study, the conditional probability is calculated to study the chance of crash and near-crash events when the given cumulative off-road glance duration in 6 s has been reached. Different off-road eye-glance locations and traffic density levels are also evaluated. The results show that one linear relationship can be obtained between the cumulative off-road eye-glance duration in 6 s and the risk of occurrences of crash and near-crash events, which varies for different off-road eye-glance locations. In addition, the traffic density level is found to be one significant moderator to this linear relationship. Detailed comparisons are made for different traffic density levels, and one nonlinear equation is obtained to predict the probability of occurrences of crash and near-crash events by considering both cumulative off-road glance duration and traffic density levels.

Do Elevated Gravitational-Force Events While Driving Predict Crashes and Near Crashes?

The purpose of this research was to determine the extent to which elevated gravitational-force event rates predict crashes and near crashes. Accelerometers, global positioning systems, cameras, and other technology were installed in vehicles driven by 42 newly licensed Virginia teenage drivers for a period of 18 months between 2006 and 2009. Elevated gravitational force and crash and near-crash events were identified, and rates per miles driven were calculated. (One mile = 1.6 km.) The correlation between crashes and near crashes and elevated gravitational-force event rates was 0.60. Analyses were done by using generalized estimating equations with logistic regression. Higher elevated gravitational-force event rates in the past month substantially increased the risk of a crash in the subsequent month (odds ratio = 1.07, 95% confidence interval: 1.02, 1.12). Although the difference in this relation did not vary significantly by time, it was highest in the first 6 months compared with the second and third 6-month periods. With a receiver operating characteristic curve, the risk models showed relatively high predictive accuracy with an area under the curve of 0.76. The authors conclude that elevated gravitational-force event rates can be used to assess risk and to show high predictive accuracy of a near-future crash.

Analysis of Naturalistic Driving Data

Recently completed naturalistic (i.e., unobtrusive) driving studies provide safety researchers with an unprecedented opportunity to study and analyze the occurrence of crashes and a range of near-crash events. Rather than focus on the details of the events immediately before the crash, this study seeks to identify methodological paradigms that can be used to answer questions long of interest to safety researchers. In particular, an attempt is made to shed some light on the four important components of methodological paradigms for naturalistic driving analysis: surrogates, evaluative aspects related to model structures, interpretation of driving context, and assessment of risk and associated sampling issues. The methodological paradigms are founded on a formal definition of the attributes of a valid crash surrogate that can be used in model formulation and testing. After a brief summary of the type of data collected in the studies, an overall framework for the analysis and a range of specific models to test hypotheses of interest are presented. A summary is given of how the systematic analyses with statistical models can extend safety knowledge beyond an assessment of “causes” of individual crashes.