The role of pre-crash driving instability in contributing to crash intensity using naturalistic driving data

The role of driver head pose dynamics and instantaneous driving in safety critical events: Application of computer vision in naturalistic driving

This paper investigates the role of driver behavior especially head pose dynamics in safety-critical events (SCEs). Using a large dataset collected in a naturalistic driving study, this paper analyzes the head pose dynamics and driving behavior in moments leading up to crashes or near-crashes. The study uses advanced computer vision and mixed logit modeling techniques to identify patterns and relationships between drivers' head pose dynamics and crash involvement. The results suggest that driver-head pose dynamics, especially poses that indicate distraction and movement volatility, are important factors that can contribute to undesirable safety outcomes. Marginal effects show that angular deviation for head pose dynamics indicated by yaw, pitch and roll increase the likelihood of crash intensity by 4.56%, 4.92% and 8.26% respectively. Furthermore, traffic flow and lane changing also contribute to increase in likelihood of crash intensity. These findings provide new insights into pre-crash factors, especially human factors and safety-critical events. The study highlights the importance of considering human factors in designing driver assistance systems and developing safer vehicles. This research contributes by examining naturalistic driving data at the microscopic level with early detection of behaviors that lead to SCEs and provides a basis for future research on automation.

Unveiling pre-crash driving behavior common features based upon behavior entropy

Driving behavior is considered as the primary crash influencing factor, whereas studies claimed that over 90% crashes were attributed by behavior features. Therefore, unveil pre-crash driving behavior features is of great importance for crash prevention. Previous studies have established the correlations between features such as vehicle speed, speed variability, and the probability of crash occurrences, but these analyses have concluded inconsistent results. This is due to the varying operating characteristics among roadway facilities, where given the same driving behavior statistical features, the corresponding traffic states are not identical. In this study, a behavioral entropy index was proposed to address the abovementioned issue. First, through comparing the individual driving behavior with the group distribution, behavioral entropy index was calculated to quantify the abnormality of driving behavior. Then, crash classification models were established by comparing the behavioral entropy prior to crash events and normal driving conditions. The empirical analyses have been conducted based on 1,634,770 naturalistic driving trajectories and 1027 crash events. And models have been carried out for urban roadway sections, urban intersections, and highway sections separately. The results showed that utilizing the behavior entropy instead of the statistical features could enhance the crash classification accuracy by 11.3%. And common pre-crash features of increased behavioral entropy were identified. Moreover, the speed coefficient of variation (QCV) entropy was concluded as the most influencing factor, which can be used for real-time driving risk monitoring and enables individual-level hazard mitigation.

Existence of connected and autonomous vehicles in mixed traffic: Impacts on safety and environment

OBJECTIVES

With the growing market penetration of connected and autonomous vehicles (CAVs), the interaction between conventional human-driven vehicles (HDVs) and CAVs will be inevitable. However, the effects of CAVs in mixed traffic streams have not been extensively studied in China. This study aims to quantify the changes in driving characteristics of an HDV while following a CAV compared to following another HDV and investigate the corresponding impact on traffic safety and the environment caused by these changes.

METHODS

Firstly, two scenarios were built on a driving simulation platform. In scenario 1, the driver follows a vehicle programmed to execute the speed profile of the HDV obtained from the Shanghai Naturalistic Driving Study (SH-NDS) project. In scenario 2, the driver follows a vehicle whose speed profile is calibrated according to the Cooperative Adaptive Cruise Control (CACC) follow-along theory. Secondly, the speed, acceleration, and headway of 30 individuals in each following scenario were analyzed. Speed and acceleration volatility (standard deviation, deviation rate) and time-to-collision (TTC) were selected as indexes to assess the safety impact. The emission and fuel consumption models were used to determine the environmental impact after being localized by the parameters.

RESULTS

HDVs following CAVs exhibit less driving volatility in speed and acceleration, show remarkable improvements in TTC, consume less fuel, and produce fewer emissions on average.

CONCLUSIONS

By introducing CAVs into the road traffic system, traffic operation safety and environmental quality will be improved, with a more stable flow status, lower collision risk, and less air pollution.

Applying the heteroskedastic ordered probit model on injury severity for improved age and gender estimation

OBJECTIVE

Driver characteristics have been linked to the frequency and severity of car crashes. Among these, age and gender have been shown to impact both the possibility and severity of a crash. Previous studies have used standard ordered probit (OP) models to analyze crash data, and some research has suggested heteroskedastic ordered probit (HETOP) could provide improved model fit. The objective of this paper is to evaluate potential improvements of the heteroskedastic ordered probit (HETOP) model compared to the standard ordered probit (OP) model in crash analysis, by examining the effect of gender across age on injury severity among drivers. This paper hypothesizes that the HETOP model can provide a better fit to crash data, by allowing heteroskedasticity in the distribution of injury severity across driver age and gender.

METHODS

Data for 20,222 crashes were analyzed for North Carolina from 2016 to 2018, which represents the state with the highest number of fatalities per 100 million vehicle miles traveled amongst available crash data from the Highway Safety Information System.

RESULTS

Darker lighting conditions, severe road surface conditions, and less severe weather were associated with increased injury severity. For driver demographics, the probability of severe injuries increased with age and for male drivers. Moreover, the variance of severity increased with age disproportionately within and across genders, and the HETOP was able to account for this.

CONCLUSIONS

The results of the two applied approaches revealed that HETOP model outperformed the standard OP model when measuring the effects of age and gender together in injury severity analysis, due to the heteroskedasticity in injury severity within gender and age. The HETOP statistical method presented in this paper can be more broadly applied across other contexts and combinations of independent variables for improved model prediction and accuracy of causal variables in traffic safety.

Application of naturalistic driving data: A systematic review and bibliometric analysis

The application of naturalistic driving data (NDD) has the potential to answer critical research questions in the area of driving behavior assessment, as well as the impact of exogenous and endogenous factors on driver safety. However, the presence of a large number of research domains and analysis foci makes a systematic review of NDD applications challenging in terms of information density and complexity. While previous research has focused on the execution of naturalistic driving studies and on specific analysis techniques, a multifaceted aggregation of NDD applications in Intelligent Transportation System (ITS) research is still unavailable. In spite of the current body of work being regularly updated with new findings, evolutionary nuances in this field remain relatively unknown. To address these deficits, the evolutionary trend of NDD applications was assessed using research performance analysis and science mapping. Subsequently, a systematic review was conducted using the keywords "naturalistic driving data" and "naturalistic driving study data". As a result, a set of 393 papers, Published between January 2002-March 2022, was thematically clustered based on the most common application areas utilizing NDD. the results highlighted the relationship between the most crucial research domains in ITS, where NDD had been incorporated, and application areas, modeling objectives, and analysis techniques involving naturalistic databases.

A proactive crash risk prediction framework for lane-changing behavior incorporating individual driving styles

Driving style may have an important effect on traffic safety. Proactive crash risk prediction for lane-changing behaviors incorporating individual driving styles can help drivers make safe lane-changing decisions. However, the interaction between driving styles and lane-changing risk is still not fully understood, making it difficult for advanced driver-assistance systems (ADASs) to provide personalized lane-changing risk information services. This paper proposes a personalized risk lane-changing prediction framework that considers driving style. Several driving volatility indices based on vehicle interactive features have been proposed, and a dynamic clustering method is developed to determine the best identification time window and methods of driving style. The Light Gradient Boosting Machine (LightGBM) based on Shapley additive explanation is used to predict lane-changing risk for cautious, normal, and aggressive drivers and to analyze their risk factors. The highD trajectory dataset is used to evaluate the proposed framework. The obtained results show that i) spectral clustering and a time window of 3 s can accurately identify driving styles during the lane-changing intention process; ii) the LightGBM algorithm outperforms other machine learning methods in personalized lane-changing risk prediction; iii) aggressive drivers seek more individual driving freedom than cautious and normal drivers and tend to ignore the state of the car behind them in the target lane, with a greater lane-changing risk. The research conclusion can provide basic support for the development and application of personalized lane-changing warning systems in ADASs.

Driving behavior classification at signalized intersections using vehicle kinematics: Application of unsupervised machine learning

Driving behavior is considered as a unique driving habit of each driver and has a significant impact on road safety. This study proposed a novel data-driven Machine Learning framework that can classify driving behavior at signalized intersections considering two different signal conditions. To the best of our knowledge, this is the first study that investigates driving behavior at signalized intersections with two different conditions that are mostly used in practice, i.e., the control setting with the signal order of green-yellow-red and a flashing green setting with the signal order of green-flashing green-yellow-red. A driving simulator dataset collected from participants at Qatar University's Qatar Transportation and Traffic Safety Center, driving through multiple signalized intersections, was used. The proposed framework extracts volatility measures from vehicle kinematic parameters including longitudinal speed and acceleration. K-means clustering algorithm with elbow method was used as an unsupervised machine learning to cluster driving behavior into three classes (i.e., conservative, normal, and aggressive) and investigate the impact of signal conditions. The framework confirmed that in general driving behavior at a signalized intersection reflects drivers' habits and personality rather than the signal condition, still, it manifests the intersection nature that usually requires drivers to be more vigilant and cautious. Nonetheless, the results suggested that flashing green condition could make drivers more conservative, which could be due to the limited capabilities of human to estimate the remaining distance and the prolonged duration of the additional flashing green interval. The proposed framework and findings of the study were promising that can be used for clustering drivers into different styles for different conditions and might be beneficial for policymakers, researchers, and engineers.

Automated vehicle data pipeline for accident reconstruction: New insights from LiDAR, camera, and radar data

As automated vehicles are deployed across the world, it has become critically important to understand how these vehicles interact with each other, as well as with other conventional vehicles on the road. One such method to achieve a deeper understanding of the safety implications for Automated Vehicles (AVs) is to analyze instances where AVs were involved in crashes. Unfortunately, this poses a steep challenge to crash-scene investigators. It is virtually impossible to fully understand the factors that contributed to an AV involved crash without taking into account the vehicle's perception and decision making. Furthermore, there is a tremendous amount of data that could provide insight into these crashes that is currently unused, as it also requires a deep understanding of the sensors and data management of the vehicle. To alleviate these problems, we propose a data pipeline that takes raw data from all on-board AV sensors such as LiDAR, radar, cameras, IMU's, and GPS's. We process this data into visual results that can be analyzed by crash scene investigators with no underlying knowledge of the vehicle's perception system. To demonstrate the utility of this pipeline, we first analyze the latest information on AV crashes that have occurred in California and then select two crash scenarios that are analyzed in-depth using high-fidelity synthetic data generated from the automated vehicle simulator CARLA. The data visualization procedure is demonstrated on the real-world Kitti dataset by using the YOLO object detector and a monocular depth estimator called AdaBins. Depth from LIDAR is used as ground truth to calibrate and assess the effect of noise and errors in depth estimation. The visualization and data analysis from these scenarios clearly demonstrate the vast improvement in crash investigations that can be obtained from utilizing state-of-the-art sensing and perception systems used on AVs.

Exploring pathways from driving errors and violations to crashes: The role of instability in driving

This study explores how different driving errors, violations, and roadway environments contribute to safety-critical events through instability in driving speed. We harness a subsample (N = 9239) of the naturalistic driving study (NDS) data collected through the Second Strategic Highway Research Program (SHRP2). From a methodological standpoint, we use the safe systems approach relying on path analysis to jointly model outcomes. This accounts for the potential correlation between unobserved factors associated with both instability in driving speed and epoch (video stream) outcomes, i.e., baseline or event-free driving, near-crashes, and crashes. Tobit and ordered Probit regressions are estimated to model the coefficient of variation (COV) of speed and epoch outcomes, respectively. Results from the Tobit model indicate that driving errors and violations are associated with instability in the driving speed of the subject driver (COV of speed). The Probit model reveals that driving errors, violations, and instability in driving speed are associated with higher chances of crashes and near-crashes. Our key finding is that driving errors and violations not only induce event risk directly but also indirectly through instability in driving speed. For instance, recognition errors associate with higher crash risk by 6.78 % but this error is accompanied by instability in driving speed, which further increases event risk by 4.73 %, bringing the total increase in risk to 11.51 %. Moreover, significant correlations were found between unobserved factors reflected in the error terms of the two models. Ignoring such correlations can lead to inefficient parameter estimates. Based on the findings, practical implications are discussed, which can lead to effective countermeasures that effectively reduce crash risk.

Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest

Accurate crash frequency prediction is critical for proactive safety management. The emerging connected vehicles technology provides us with a wealth of vehicular motion data, which enables a better connection between crash frequency and driving behaviors. However, appropriately dealing with the spatial dependence of crash frequency and multitudinous driving features has been a difficult but critical challenge in the prediction process. To this end, this study aims to investigate a new Artificial Intelligence technique called Geographical Random Forest (GRF) that can address spatial heterogeneity and retain all potential predictors. By harnessing more than 2.2 billion high-resolution connected vehicle Basic Safety Message (BSM) observations from the Safety Pilot Model Deployment in Ann Arbor, MI, 30 indicators of driving volatility are extracted, including speed, longitudinal and lateral acceleration, and yaw rate. The developed GRF was implemented to predict rear-end crash frequency at intersections. The results show that: 1) rear-end crashes are more likely to happen at intersections connecting minor roads compared to major roads; 2) a higher number of hard acceleration and deceleration events beyond two standard deviations in the longitudinal direction is a leading indicator of rear-end crashes; 3) the optimal GRF significantly outperforms Global Random Forest, with a 9% lower test error and a substantially better fit; and 4) geographical visualization of variable importance highlights the presence of spatial non-stationarity. The proposed framework can proactively identify at-risk intersections and alert drivers when leading indicators of driving volatility tend to worsen.

Incorporating driving volatility measures in safety performance functions: Improving safety at signalized intersections

About 40 percent of motor vehicle crashes in the US are related to intersections. To deal with such crashes, Safety Performance Functions (SPFs) are vital elements of the predictive methods used in the Highway Safety Manual. The predictions of crash frequencies and potential reductions due to countermeasures are based on exposure and geometric variables. However, the role of driving behavior factors, e.g., hard accelerations and declarations at intersections, which can lead to crashes, are not explicitly treated in SPFs. One way to capture driving behavior is to harness connected vehicle data and quantify performance at intersections in terms of driving volatility measures, i.e., rapid changes in speed and acceleration. According to recent studies, driving volatility is typically associated with higher risk and safety-critical events and can serve as a surrogate for driving behavior. This study incorporates driving volatility measures in the development of SPFs for four-leg signalized intersections. The Safety Pilot Model Deployment (SPMD) data containing over 125 million Basic Safety Messages generated by over 2,800 connected vehicles are harnessed and linked with the crash, traffic, and geometric data belonging to 102 signalized intersections in Ann Arbor, Michigan. The results show that including driving volatility measures in SPFs can reduce model bias and significantly enhances the models' goodness-of-fit and predictive performance. Technically, the best results were obtained by applying Bayesian hierarchical Negative Binomial Models, which account for spatial correlation between signalized intersections. The results of this study have implications for practitioners and transportation agencies about incorporating driving behavior factors in the development of SPFs for greater accuracy and measures that can potentially reduce volatile driving.

Causal mediation analysis of the impacts of distracted driving on crash injury risks

Distracted driving can pose great risks to road traffic safety. Although there is a rich body of literature devoted to identifying the statistical association between distracted driving and crash risks, few are available to examine the causal effect mechanism of distracted driving. Thus, the study attempts to conduct the causal mediation analysis to reveal the impact mechanism of distracted driving on crash injury risks, in which various hazardous driving actions are used as the mediators between driver distraction and crash injuries. Sensitivity analysis is also carried out to validate the underlying assumption of causal mediation analysis. The analytic results indicate that 1) distracted driving can lead to a higher likelihood of hazardous driving actions such as failing to yield, disobeying traffic control devices, driving left of lane center, and failing to stop in assured clear distance, 2) both the driver distraction and hazardous actions are the contributory factors to the severe crash injuries, and 3) distracted driving is identified to have significant mediation effects on crash injury risks. The study confirms the causal mediation effects of distracted driving on crash injury risks, which can serve to propose specific safety countermeasures to mitigate the crash injury risks.

Causation analysis of crashes and near crashes using naturalistic driving data

Understanding crash causation to the extent needed for applying countermeasures has always been a focus as well as a difficulty in the field of traffic safety. Previous research has been limited by insufficient crash data and analysis methods more suitable to single crashes. The use of crashes and near crashes (CNCs) and naturalistic driving studies can help solve the data problem, and use of pre-crash scenarios can identify the high-prevalence causes across multiple crashes of a given scenario. This study therefore proposes a two-stage crash causation analysis method based on pre-crash scenarios and a crash causation derivation framework that systematically categorizes and analyzes contributing factors. From the Shanghai Naturalistic Driving Study (SH-NDS), 536 CNCs were extracted, and were grouped into 23 different pre-crash scenarios based on the National Highway Traffic Safety Administration (NHTSA) pre-crash scenario typology. In-depth investigations were conducted, and CNCs sharing the same scenario were analyzed using the proposed framework, which identifies causation patterns based on the interaction of the framework's road user, vehicle, roadway infrastructure, and roadway environment subsystems. Through statistical analysis, the causation patterns and their contributing factors were compared for three common pre-crash scenarios of highest incidence: rear-end, lane change, and vehicle-pedalcyclist. Braking error in low-speed car following, following too closely, and non-driving-related distraction were important causes of rear-end scenarios. In lane change scenarios, the main causation patterns included illegal use of turn signals and dangerous lane changes as critical factors. Pedalcyclist scenarios were particularly impacted by visual obstructions, inadequate lanes for non-motorized vehicles, and pedalcyclists violating traffic regulations. Based on the identified causation patterns and their contributing factors, countermeasures for the three common scenarios are suggested, which provide support for safety improvement projects and the development of advanced driver assistance systems.

Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach

Influencing factors on crash severity involved with autonomous vehicles (AVs) have been paid increasing attention. However, there is a lack of comparative analyses of those factors between AVs and human-driven vehicles. To fill this research gap, the study aims to explore the divergent effects of factors on crash severity under autonomous and conventional (i.e., human-driven) driving modes. This study obtained 180 publicly available autonomous vehicle crash data, and 39 explanatory variables were extracted from three categories, including environment, roads, and vehicles. Then, a hierarchical Bayesian approach was applied to analyze the impacting factors on crash severity (i.e., injury or no injury) under both driving modes with considering unobserved heterogeneities. The results showed that some influencing factors affected both driving modes, but their degrees were different. For example, daily visitors' flowrate had a greater impact on the crash severity under the conventional driving mode. More influencing factors only had significant impacts on one of the driving modes. For example, in the autonomous driving mode, mixed land use increased the severity of crashes, while daytime had the opposite effects. This study could contribute to specifying more appropriate policies to reduce the crash severity of both autonomous and human-driven vehicles especially in mixed traffic conditions.

The Analysis of Classification and Spatiotemporal Distribution Characteristics of Ride-Hailing Driver's Driving Style: A Case Study in China

Monitoring the driving styles of ride-hailing drivers is helpful for providing targeted training for drivers and improving the safety of the service. However, previous studies have lacked analyses of the temporal variation as well as spatial variation characteristics of driving styles. Understanding the variations can also help authorities formulate driver management policies. In this study, trajectory data are used to analyze driving styles in various temporal and spatial scenarios involving 34,167 drivers. The k-means method is used to cluster sample drivers. In terms of driving style time-varying, we found that only 31.79% of drivers could maintain a stable driving style throughout the day. Spatially, we divided the research area into two parts, namely, road segments and intersections, to analyze the spatial driving characteristics of drivers with different styles. The speed distribution, the acceleration and deceleration distributions are analyzed, results indicated that aggressive drivers display more aggressive driving styles in road segments, and conservative drivers exhibit more conservative driving styles at intersections. The findings of this study provide an understanding of temporal and spatial driving behavior factors for ride-hailing drivers and offer valuable contributions to ride-hailing driver training and road safety management.

Application of Extremely Randomised Trees for exploring influential factors on variant crash severity data

Crash severity models play a crucial role in evaluating the influencing factors in the severity of traffic crashes. In this study, Extremely Randomised Tree (ERT) is used as a machine learning technique to analyse the severity of crashes. The crash data in the province of Khorasan Razavi, Iran, for a period of 5 years from 2013 to 2017, is used for crash severity model development. The dataset includes traffic-related variables, vehicle specifications, vehicle movement, land use characteristics, temporal characteristics, and environmental variables. In this paper, Feature Importance Analysis (FIA), Partial Dependence Plots (PDP), and Individual Conditional Expectation (ICE) plots are utilised to analyse and interpret the results. According to the results, the involvement of vulnerable road users such as motorcyclists and pedestrians alongside traffic-related variables are among the most significant variables in crash severity. Results show that the presence of motorcycles can increase the probability of injury crashes by around 30% and almost double the probability of fatal crashes. Analysing the interaction of PDPs shows that driving speeds above 60 km/h in residential areas raises the probability of injury crashes by about 10%. In addition, at speeds higher than 70 km/h, the presence of pedestrians approximately increases the probability of fatal crashes by 6%.

A Robust Adaptive Traffic Signal Control Algorithm Using Q-Learning under Mixed Traffic Flow

The operational and safety performance of intersections is the key to ensuring the efficient operation of urban traffic. With the development of automated driving technologies, the ability of adaptive traffic signal control has been improved according to data detected by connected and automated vehicles (CAVs). In this paper, an adaptive traffic signal control was proposed to optimize the operational and safety performance of the intersection. The proposed algorithm based on Q-learning considers the data detected by loop detectors and CAVs. Furthermore, a comprehensive analysis was conducted to verify the performance of the proposed algorithm. The results show that the average delay and conflict rate have been significantly optimized compared with fixed timing and traffic actuated control. In addition, the performance of the proposed algorithm is good in the test of the irregular intersection. The algorithm provides a new idea for the intelligent management of isolated intersections under the condition of mixed traffic flow. It provides a research basis for the collaborative control of multiple intersections.

Integrating machine learning into path analysis for quantifying behavioral pathways in bicycle-motor vehicle crashes

The behavioral pathways in traffic crashes describe the chained linkages among contributing factors, pre-crash road user behaviors, and crash outcomes. Bicyclists are more vulnerable than motorists on road and their pre-crash behaviors play an essential role in the pathways leading to injuries. The objective of this study is to develop a methodological framework that integrates machine learning with path analysis to quantify behavioral pathways in bicycle-motor vehicle crashes. Specifically, two sets of models are developed for predicting: 1) pre-crash behaviors given contributing factors and 2) bicyclist injury severity given contributing factors including pre-crash behaviors. The path analysis chains machine learning models to establish the indirect linkages between contributing factors and injury severities through correlates of pre-crash behaviors. This study explored five machine learning methods, including Random Forest (RF), Categorical Naive Bayes (CNB), Support vector machine (SVM), AdaBoost (Boost), and Neural network (NN). To reduce the bias of any single model, this study proposes a technique to combine model estimates by averaging marginal effects. This study used a dataset containing 9,296 bicycle-motor vehicle crashes to demonstrate the application of the framework. Across five machine learning models, the signs of marginal effects generally agree but their magnitudes vary substantially. The pre-crash behavior of "bicyclist failed to yield" increases bicyclist injury severity by 1.11%. The path analysis results highlighted contributing factors related to risky pre-crash behaviors that lead to severe injuries, such as bicyclist intoxication. The framework is expected to support agencies' decision-making to improve cycling safety by reducing unsafe behaviors on roads.

Exploring the associations between driving volatility and autonomous vehicle hazardous scenarios: Insights from field operational test data

With the promising development and deployment trends of autonomous vehicles (AVs), AVs' operation safety has become a key issue worldwide. Studies have been conducted to reveal the risk factors of AV operation safety based upon AV-involved crash reports. However, the crash data sample size was limited and the crash reports only recorded static information, thus it failed to identify crash contributing factors and further provide feedbacks to AV algorithm development. In this study, the risk factors were investigated based upon hazardous scenarios, which were claimed to possess consistent causal mechanisms with crash events. First, contributing factors were extracted from both vehicle kinematics and traffic environment aspects, and their volatility features were obtained. Then, path analysis models were developed to reveal the concurrent relationships between scenario volatility and hazardous scenario occurrence probability. Besides, to understand the varying risk factors for hazardous scenarios caused by human drivers and AVs, a logit regression model was further established. The modeling results showed that large volatility of space headway held direct impacts on increasing the AV driving risks. And the volatility of the drivable road area had no significant impacts on AV driving risks while it indirectly influenced human driving risks. Finally, result implications for AV driving behavior improvements have been discussed.

Effect of daily car-following behaviors on urban roadway rear-end crashes and near-crashes: A naturalistic driving study

The rear-end crash is one of the most common types of crashes, and key risk factors have been broadly identified in the car-following behaviors preceding a crash. However, the relationships between rear-end crash risk and daily car-following behaviors, or habits, have not been well examined. This study aims to identify the daily car-following behaviors on urban surface roads and urban expressways that have the most influence on rear-end crashes and near-crashes (CNC). Two months of naturalistic driving study data were used to investigate the daily car-following behavior of 54 drivers. A paired t-test and a Wilcoxon matched-pairs signed rank test were conducted to find the differences in behaviors on the two road types, and basic Poisson regression and Poisson hurdle regression models were used to explore significant risk factors. Results revealed that (1) drivers' longitudinal vehicle control, time control, and emergency behaviors are significantly different on urban surface roads and urban expressways; (2) for surface roads, three key influencing factors were ranked, in descending order, as the standard deviation of relative speed, percentage of time gap less than 1 s, and maximum acceleration; (3) for expressways, four key factors were ranked: minimum time gap, maximum deceleration, percentage of TTC less than 5 s, and the percentage of large positive jerk. The knowledge achieved on risky daily driving behaviors can be applied to training drivers to improve safe practices, assist insurance companies in creating usage-based insurance strategies, and support driver assistant systems design.

Examining the relationship between poor sleep health and risky driving behaviors among college students

Objective: Risky driving behaviors, such as texting while driving, are common among young adults and increase risk of traffic accidents and injuries. We examine the relationship between poor sleep and risky driving behaviors among college students as potential targets for traffic injury prevention.Methods: Data for this study were obtained from a cross-sectional survey administered to a college student sample in the United States Midwest (n = 1,305). Sleep was measured using the Pittsburgh Sleep Quality Index (PSQI). Risky driving behaviors were measured, including sending texts/emails; reading texts/emails while driving; talking on the phone while driving; falling asleep while driving; and driving under the influence. Risky driving behavior was defined as a response of "just once," "rarely," "sometimes," "fairly often" or "regularly" (reference = "never"). Logistic regression was used to examine the relationship between sleep and risky driving, after adjusting for confounders.Results: Among participants, 75% reported sending texts/emails while driving, 82% reported reading texts/emails while driving, and 84% reported phone talking while driving; 20% reported falling asleep while driving; 8% reported driving under the influence; and 62% reported 3 or more risky behaviors. Compared to those reporting no sleep disturbance, those with sleep disturbance "once or twice a week" were more likely to report sending a text/email while driving (aOR: 2.9, 95%CI:1.7-4.9), reading a text/email while driving (aOR:3.1,95%CI:1.5-5.5), talking on the phone while driving (aOR:1.9, 95%CI:1.0-3.4), and falling asleep while driving (aOR:3.4,95%CI:1.5-7.4). Compared to those reporting no daytime dysfunction, those reporting issues "once or twice a week" were more likely to report talking on the phone while driving (aOR:1.7, 95%CI:1.1-2.7) and falling asleep while driving (aOR:3.6,95%CI:2.3-5.6).Conclusions: Future research may consider designing behavioral interventions that aim to improve sleep, reduce drowsy driving among young adults.

Analyzing drivers' hazard recognition: Precursors to single-vehicle collisions

Extensive driver behavior and performance information provided by real-world video surveillance and sensor data in the SHRP2 Naturalistic Driving Study has enabled the examination of new layers and pathways leading to crash outcomes. We note that the prominence of hazards and the importance of recognizing them vary systematically across single vs. multi-vehicle crashes, and address a fundamental question about safety: why do around three-quarters of drivers involved in single-vehicle crashes not recognize, perceive, or react to the precipitating event (PE)? Using a path-analytic framework through marginal effects, this study investigates factors correlated to recognition of the PE in single-vehicle events, and how these correlations may act as crash precursors. Logit models, accounting for heterogeneity among events and drivers by estimating both fixed and random parameters, quantified correlations among key variables, given a crash or near-crash event (N = 543). The type of PE, roadway environment factors, and driving maneuvers heavily influenced recognition chances. Drivers had a harder time recognizing less conspicuous hazards (e.g. departing the travel way, decreased recognition chances by 48.29%), but seemed better at recognizing prominent hazards (e.g. vehicle losing control, increased recognition chances by 46.71%). In addition, drivers are less likely to recognize PEs when executing less involved driving maneuvers in more relaxed environments, such as daylight (decreased recognition chances by 16.00%), but are more adept in environments that already demand more attention. Recognition reduced the chances of a crash by 12.23%, so we found similar correlations with crash outcome. Future intelligent transportation systems may focus on increasing driver recognition of potential hazards by bringing attention to less conspicuous hazards and less involved driving environments and actions.

Study on the Impact of Traffic Accidents in Key Areas of Rural Roads

In recent years, the proportion of rural road fatalities in the country is gradually increasing, among which the traffic safety problems are particularly prominent in the town-rural area and the town-center area. Based on the relevant accident data in Hunan Province in recent years, the chi-square test was conducted to obtain the correlation degree between each risk factor and accident severity based on gender stratification. Then, a binary logistic model was established to obtain the significant factors that affect the accident severity in the town-rural area and the town-center area, respectively. Based on the significant factors, relevant safety improvement measures were proposed for the key areas. The results show that severe accidents were significantly related to single-vehicle factors, motorcycle factors, and intersections factors in the town-rural area. In the town-center area, severe accidents were significantly related to elderly age, single-vehicle factors, and nighttime factors. The study obtained the risk factors in key areas, which can provide a reference for the improvement of traffic safety in key areas of rural roads, to ensure the safety and sustainability of rural traffic.

Built environment, driving errors and violations, and crashes in naturalistic driving environment

Driving errors and violations are highly relevant to the safe systems approach as human errors tend to be a predominant cause of crash occurrence. In this study, we harness highly detailed pre-crash Naturalistic Driving Study (NDS) data 1) to understand errors and violations in crash, near-crash, and baseline (no event) driving situations, and 2) to explore pathways that lead to crashes in diverse built environments by applying rigorous modeling techniques. The "locality" factor in the NDS data provides information on various types of roadway and environmental surroundings that could influence traffic flow when a precipitating event is observed. Coded by the data reductionists, this variable is used to quantify the associations of diverse environments with crash outcomes both directly and indirectly through mediating driving errors and violations. While the most prevalent errors in crashes were recognition errors such as failing to recognize a situation (39 %) and decision errors such as not braking to avoid a hazard (34 %), performance errors such as poor lateral or longitudinal control or weak judgement (8 %) were most strongly correlated with crash occurrence. Path analysis uncovered direct and indirect relationships between key built-environment factors, errors and violations, and crash propensity. Possibly due to their complexity for drivers, urban environments are associated with higher chances of crashes (by 6.44 %). They can also induce more recognition errors which correlate with an even higher chances of crashes (by 2.16 % with the "total effect" amounting to 8.60 %). Similar statistically significant mediating contributions of recognition errors and decision errors near school zones, business or industrial areas, and moderate residential areas were also observed. From practical applications standpoint, multiple vehicle technologies (e.g., collision warning systems, cruise control, and lane tracking system) and built-environment (roadway) changes have the potential to reduce driving errors and violations which are discussed in the paper.

A taxonomy of driving errors and violations: Evidence from the naturalistic driving study

Driving errors and violations are identified as contributing factors in most crash events. To examine the role of human factors and improve crash investigations, a systematic taxonomy of driver errors and violations (TDEV) is developed. The TDEV classifies driver errors and violations based on their occurrence during the theoretically based perception-reaction process and analyzes their contributions in safety critical events. To empirically explore errors and violations, made by drivers of instrumented vehicles, in diverse built environments, this study harnesses unique and highly detailed pre-crash sensor data collected in the Naturalistic Driving Study (NDS), containing 673 crashes, 1,331 near-crashes and 7,589 baselines (no-event). Human factors are categorized into recognition errors, decision errors, performance errors, and errors due to the drivers' physical condition or their lack of contextual experience/familiarity, and intentional violations. In the NDS data, built environments (measured by roadway localities) are classified based on roadway functional classification and land uses, e.g., residential areas, school zones, and church zones. Based on the crash percentage to baseline percentage in a specific locality, interstates and open country/open residential (rural and semi-rural settings) may pose lower risks, while urban, business/industrial, and school zone locations showed higher crash risk. Human errors and violations by instrumented vehicle drivers contributed to 93% of the observed crashes, while roadway factors contributed to 17%, vehicle factors contributed in 1%, and 4% of crashes contained unknown factors. The most common human errors were recognition and decision errors, which occurred in 39% and 34% of crashes, respectively. These two error types occurred more frequently (each contributing to nearly 39% of crashes) in business or industrial land use environments (but not in dense urban localities). The findings of this study reveal continued prevalence of human factors in crashes. The distribution of driving errors and violations across different roadway environments can aid in the implementation of driver assistance systems and place-based interventions that can potentially reduce these driving errors and violations.

Percentile charts for speeding based on telematics information

Reference charts are widely used as a graphical tool for assessing and monitoring children's growth given gender and age. Here, we propose a similar approach to the assessment of driving risk. Based on telematics data, and using quantile regression models, our methodology estimates the percentiles of the distance driven at speeds above the legal limit depending on drivers' characteristics and the journeys made. We refer to the resulting graphs as percentile charts for speeding and illustrate their use for a sample of drivers with Pay-How-You-Drive insurance policies. We find that percentiles of distance driven at excessive speeds depend mainly on total distance driven, the percentage of driving in urban areas and the driver's gender. However, the impact on the estimated percentile for these covariates is not constant. We conclude that the heterogeneity in the risk of driving long distances above the speed limit can be easily represented using reference charts and that, conversely, individual drivers can be scored by calculating an estimated percentile for their specific case. The dynamics of this risk score can be assessed by recording drivers as they accumulate driving experience and cover more kilometres. Our methodology should be useful for accident prevention and, in the context of Manage-How-You-Drive insurance, reference charts can provide real-time alerts and enhance recommendations for ensuring safety.

Injury severity analysis of pedestrian and bicyclist trespassing crashes at non-crossings: A hybrid predictive text analytics and heterogeneity-based statistical modeling approach

Non-motorists involved in rail-trespassing crashes are usually more vulnerable to receiving major or fatal injuries. Previous research has used traditional quantitative crash data for understanding factors contributing to injury outcomes of non-motorists in train involved collisions. However, usually overlooked crash narratives can provide useful and unique contextual crash-specific information regarding factors associated with injury outcomes. The main objective of this study is to harness the rapid advancements in more sophisticated qualitative analysis procedures for identifying thematic concepts in unstructured crash narrative data. A two-staged hybrid approach is proposed where text mining is applied first to extract valuable information from crash narratives followed by inclusion of the new variables derived from text mining in formulation of advanced statistical models for injury outcomes. By using ten-year (2006-2015) non-motorist non-crossing trespassing injury data obtained from the Federal Railroad Administration, statistical procedures and advanced machine learning text analytics are applied to extract unique information on contributory factors of trespassers' injury outcomes. The key concepts are systematically categorized into trespasser, injury, train, medical, and location related factors. A total of 13 unique variables are extracted from the thematic concepts that are not present in traditional tabular crash data. The analysis reveals a positive statistically significant association between presence of crash narrative and trespasser's injury outcome (coded as minor, major, and fatal injury). Compared to crashes with minor injuries, crashes involving major and fatal injuries are more likely to be reported with crash narratives. A crosstabulation of new variables derived from text mining with injury outcomes revealed that trespassers with confirmed suicide attempts, trespassers wearing headphones, or talking on cell phones are more likely to receive fatal injuries. Among other factors identified, trespassers under alcohol influence, trespasser hit by commuter train, and advance warnings by engineer are associated with more severe (major and fatal) trespasser injury outcomes. Accounting for unobserved heterogeneity and controlling for other factors, fixed and random parameter discrete outcome models are developed to understand the heterogeneous correlations between trespasser injury outcomes and the new crash specific explanatory variables derived from text mining - providing deeper insights. Practical implications and future research directions are discussed.

Exploring the who, what, when, where, and why of automated vehicle disengagements

Automated vehicles are emerging on the transportation networks as manufacturers test their automated driving system (ADS) capabilities in complex real-world environments in testing operations like California's Autonomous Vehicle Tester Program. A more comprehensive understanding of the ADS safety performances can be established through the California Department of Motor Vehicle disengagement and crash reports. This study comprehensively examines the safety performances (159,840 disengagements, 124 crashes, and 3,669,472 automated vehicle miles traveled by the manufacturers) documented since the inauguration of the testing program. The reported disengagements were categorized as control discrepancy, environmental conditions and other road users, hardware and software discrepancy, perception discrepancy, planning discrepancy, and operator takeover. An applicable subset of disengagements was then used to identify and quantify the 5 W's of these safety-critical events: who (disengagement initiator), when (the maturity of the ADS), where (location of disengagement), and what/why (the facts causing the disengagement). The disengagement initiator, whether the ADS or human operator, is linked with contributing factors, such as the location, disengagement cause, and ADS testing maturity through a random parameter binary logit model that captured unobserved heterogeneity. Results reveal that compared to freeways and interstates, the ADS has a lower likelihood of initiating the disengagement on streets and roads compared to the human operator. Likewise, software and hardware, and planning discrepancies are associated with the ADS initiating the disengagement. As the ADS testing maturity advances in months, the probability of the disengagement being initiated by the ADS marginally increases when compared to human-initiated. Overall, the study contributes by understanding the factors associated with disengagements and exploring their implications for automated systems.

Investigating the injury severity of single-vehicle truck crashes in a developing country

Trucking plays a vital role in economic development in every country, especially countries where it serves as the backbone of the economy. The fast growth of economy in Iran as a developing country has also been accompanied by an alarming situation in terms of fatalities in truck-involved crashes, among the drivers and passengers of the trucks as well as the other vehicles involved. Despite the sizable efforts to investigate the truck-involved crashes, very little is known about the safety of truck movements in developing countries, and about the single-truck crashes worldwide. Thus, this study aims to uncover significant factors associated with injury severities sustained by truck drivers in single-vehicle truck crashes in Iran. The explanatory factors tested in the models include the characteristics of drivers, vehicles, and roadways. A random threshold random parameters hierarchical ordered probit model is utilized to consider heterogeneity across observations. Several variables turned out to be significant in the model, including driver's education, advanced braking system deployment, presence of curves on roadways, and high speed-limit. Using those results, we propose safety countermeasures in three categories of 1) educational, 2) technological, and 3) road engineering to mitigate the severity of single-vehicle truck crashes.

Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis

Detecting traffic accidents as rapidly as possible is essential for traffic safety. In this study, we use eXtreme Gradient Boosting (XGBoost)-a Machine Learning (ML) technique-to detect the occurrence of accidents using a set of real time data comprised of traffic, network, demographic, land use, and weather features. The data used from the Chicago metropolitan expressways was collected between December 2016 and December 2017, and it includes 244 traffic accidents and 6073 non-accident cases. In addition, SHAP (SHapley Additive exPlanation) is employed to interpret the results and analyze the importance of individual features. The results show that XGBoost can detect accidents robustly with an accuracy, detection rate, and a false alarm rate of 99 %, 79 %, and 0.16 %, respectively. Several traffic related features, especially difference of speed between 5 min before and 5 min after an accident, are found to have relatively more impact on the occurrence of accidents. Furthermore, a feature dependency analysis is conducted for three pairs of features. First, average daily traffic and speed after accidents/non-accidents time at the upstream location are interpreted jointly. Then, distance to Central Business District and residential density are analyzed. Finally, speed after accidents/non-accidents time at upstream location and speed after accidents/non-accidents time at downstream location are evaluated with respect to the model's output.

Severity analysis for large truck rollover crashes using a random parameter ordered logit model

Large truck rollover crashes present significant financial, industrial, and social impacts. This paper presents an effort to investigate the contributing factors to large truck rollover crashes. Specific focus was placed on exploring the role of heterogeneity and the potential sources of heterogeneity regarding their impacts on injury-severity outcomes. The data used in this study contained large truck rollover crashes that occurred between 2007 and 2016 in the state of Florida. A random parameter ordered logit (RPOL) model was applied. Various driver, vehicle, roadway, and crash attributes were explored as potential predictors in the model. Their impacts were examined for the presence of heterogeneity. Interaction effects were then added to the random variables in order to detect potential sources of heterogeneity. Model results showed that the impacts of lighting conditions and driving speed had significant variation across observations, and this variation could be attributed to driver actions and driver conditions at the time of the crash, as well as driver vision obstruction. Findings from this study shed light on the direction, magnitude, and randomness of the factors that contribute to large truck rollover crashes. Findings associated with heterogeneity could help develop more effective and targeted countermeasures to improve freight safety. Driver education programs could be planned more efficiently, and advisory and warning signs could be designed in a more insightful manner by taking into account specific roadway attributes, such as sandy surfaces, downhill, curved alignment, unpaved shoulders, and lighting conditions.