Driver drowsiness is associated with altered facial thermal patterns: Machine learning insights from a thermal imaging approach.

Driver drowsiness is a significant factor in road accidents. Thermal imaging has emerged as an effective tool for detecting drowsiness by enabling the analysis of facial thermal patterns. However, it is not clear which facial areas are most affected and correlate most strongly with drowsiness. This study examines the variations and importance of various facial areas and proposes an approach for detecting driver drowsiness. Twenty participants underwent tests in a driving simulator, and temperature changes in various facial regions were measured. The random forest method was employed to evaluate the importance of each facial region. The results revealed that temperature changes in the nasal area exhibited the highest value, while the eyes had the most correlated changes with drowsiness. Furthermore, drowsiness was classified with an accuracy of 88 % utilizing thermal variations in the facial region identified as the most important regions by the random forest feature importance model. These findings provide a comprehensive overview of facial thermal imaging for detecting driver drowsiness and introduce eye temperature as a novel and effective measure for investigating cognitive activities.

A modeling method for two-dimensional two-wheeler driving behavior during severe conflict interaction at intersections.

The safety of two-wheelers is a serious public safety issue nowadays. Two-wheelers usually have severe conflict interaction with vehicles at intersections, such as running red lights, which is very likely to cause traffic accidents. Therefore, a model of two-wheeler driving behavior in conflicting interactions can provide guidance for traffic safety management on one hand, and can be used for the development and testing of autonomous vehicles on the other. However, the existing models perform poorly when interacting with vehicles. To address the problems, this paper proposes a modeling method (an improved social force model, ISFM) for two-dimensional two-wheeler driving simulation for conflict interaction at intersections. Based on analysis of naturalistic driving study data, when two-wheelers encounter with vehicles, their driving intentions and trajectories can be categorized into two groups, which are yielding and overtaking. Therefore, the vehicle-related social forces are designed to be a set of two forces rather than a repulsion force in original SFM, which is a yielding force based on the relative distance between the two-wheeler and the vehicle, and an overtaking force based on the velocity of the two-wheeler itself. This opens up the possibilities for modeling the multi-modal driving intention of two-wheelers encountering with cross traffic. Based on ISFM, a bicycle model, a powered two-wheeler (PTW) model and a model of a group of PTWs, are then constructed. Compared to the original SFM, ISFM increases the precision of driving intention prediction by 19.7 % (yielding situation) and 25.0 % (overtaking situation), and reduces the root mean square error between simulated and actual trajectories by 7.8 % and 14.8 % on the bicycle model and the PTW model, respectively. Meanwhile, the model of a group of PTWs also performs well. Finally, the results of ablation experiments also validate the effectiveness of the social force designed based on velocity.

Assessing the determinants of crash propensity using structural equation modeling: Role of distractions caused by fellow drivers.

INTRODUCTION: Aggressive behavior of drivers is a source of crashes and high injury severity. Aggressive drivers are part of the driving environment, however, excessive aggressive driving by fellow drivers may take the attention of the recipient drivers away from the road resulting in distracted driving. Such external distractions caused by the aggressive and discourteous behavior of other road users have received limited attention. These distractions caused by fellow drivers (DFDs) may agitate recipient drivers and ultimately increase crash propensity. Aggressive driving behaviors are quite common in South Asia and, thus, it is necessary to determine their contribution to distractions and crash propensity. METHOD: Our study aimed to evaluate the effects of DFDs using primary data collected through a survey conducted in Lahore, Pakistan. A total of 801 complete responses were obtained. Various hypotheses were defined to explore the associations between the latent factors such as DFDs, anxiety/stress (AS), anxiety-based performance deficits (APD), hostile behavior (HB), acceptability of vehicle-related distractions (AVRD), and crash propensity (CP). Structural Equation Modeling (SEM) was employed as a multivariate statistical technique to test these hypotheses. RESULTS: The results supported the hypothesis that DFDs lead to AS among recipient drivers. DFDs and AS were further found to have positive associations with APDs. Whereas, there was a significant negative association between DFD, AS, and AVRD. As hypothesized, DFD and AS had positive associations with CP, indicating that distractions caused by aggressive behaviors leads to stress and consequently enhances crash propensity. PRACTICAL APPLICATIONS: The results of this study provide a statistically sound foundation for further exploration of the distractions caused by the aggressive behaviors of fellow drivers. Further, the results of this study can be utilized by the relevant authorities to alter aggressive driving behaviors and reduce DFDs.

Modeling distracted driving behavior considering cognitive processes.

The modeling of distracted driving behavior has been studied for many years, however, there remain many distraction phenomena that can not be fully modeled. This study proposes a new method that establishes the model using the queuing network model human processor (QN-MHP) framework. Unlike previous models that only consider distracted-driving-related human factors from a mathematical perspective, the proposed method reflects the information processing in the human brain, and simulates the distracted driver's cognitive processes based on a model structure supported by physiological and cognitive research evidence. Firstly, a cumulative activation effect model for external stimuli is adopted to mimic the phenomenon that a driver responds only to stimuli above a certain threshold. Then, dual-task queuing and switching mechanisms are modeled to reflect the cognitive resource allocation under distraction. Finally, the driver's action is modeled by the Intelligent Driver Model (IDM). The model is developed for visual distraction auditory distraction separately. 773 distracted car-following events from the Shanghai Naturalistic Driving Study data were used to calibrate and verify the model. Results show that the model parameters are more uniform and reasonable. Meanwhile, the model accuracy has improved by 57% and 66% compared to the two baseline models respectively. Moreover, the model demonstrates its ability to generate critical pre-crash scenarios and estimate the crash rate of distracted driving. The proposed model is expected to contribute to safety research regarding new vehicle technologies and traffic safety analysis.

The effect of guardrail color on driver behavior based on driving style along mountain curves.

OBJECTIVE: Mountain highways are linearly complex, with extensive curves and high accident injury rates, how to improve driving safety is the key to traffic safety management on mountain highways, and it also meets the need for harmonious and sustainable development of the society. Therefore, this study investigates the effects of different guardrail color configurations on the driving behavior of different styles of drivers when driving on mountainous curves from the perspective of improving road aids - guardrails. METHODS: A virtual reality experiment was designed using a driving simulator and VR technology, and 64 subjects were recruited to participate and complete the experiment. RESULTS: Drivers with non-adaptive driving styles (Reckless, Angry, Anxious) traveled at significantly higher speeds than subjects with adaptive driving styles (Cautious) on mountainous roads; drivers with Cautious styles had better lane-keeping ability when passing through different radii of curves as compared to non-adaptive drivers; and the red and yellow guardrails were more effective in decreasing the speeds at which drivers passed and in increasing the stability of lane-keeping. CONCLUSIONS: The results of the study show that the effectiveness of red and yellow guardrails is better, which provides a reference for the traffic management department to propose a standardized color setting of guardrails in mountainous areas, which is conducive to the development of more precise traffic management measures to reduce the occurrence of traffic accidents.

A comparative analysis of distracted driving behavior among drivers of different income levels: A case study in huainan, China.

This research aims to investigate the differences and causes behind distracted driving behavior among drivers with varying income levels. A comparative survey of 1121 drivers in Huainan City, China, was conducted, including 562 drivers from high-end communities representing the high-income group, and 559 drivers from general communities representing the low-income group. Employing social norms, risk perception, and experience as independent variables, the study further examines the role of in-group bias as a mediating variable, with distracted driving behavior serving as the dependent variable, through the construction of two structural equation models for analysis. The study found that among the high-income driver group, in-group bias significantly mediates the impact of social norms, risk perception, and experience on distracted driving behavior; however, this mediating effect is less pronounced in the low-income driver group. This finding is crucial for understanding the potential distracted driving behaviors induced by in-group bias within the high-income driver group and for effectively promoting driving safety. In summary, this research provides new insights into reducing distracted driving behavior among the high-income driver group, thereby enhancing road safety.

Resolving uncertainty on the fly: modeling adaptive driving behavior as active inference.

Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena. While models developed in the fields of machine learning and robotics can effectively learn adaptive driving behavior from data, due to their black box nature, they offer little or no explanation of the mechanisms underlying the adaptive behavior. Thus, generalizable, interpretable, computational models of adaptive human driving behavior are still rare. This paper proposes such a model based on active inference, a behavioral modeling framework originating in computational neuroscience. The model offers a principled solution to how humans trade progress against caution through policy selection based on the single mandate to minimize expected free energy. This casts goal-seeking and information-seeking (uncertainty-resolving) behavior under a single objective function, allowing the model to seamlessly resolve uncertainty as a means to obtain its goals. We apply the model in two apparently disparate driving scenarios that require managing uncertainty, (1) driving past an occluding object and (2) visual time-sharing between driving and a secondary task, and show how human-like adaptive driving behavior emerges from the single principle of expected free energy minimization.

Data-driven evaluation of electric vehicle energy consumption for generalizing standard testing to real-world driving.

Standard energy-consumption testing, providing the only publicly available quantifiable measure of battery electric vehicle (BEV) energy consumption, is crucial for promoting transparency and accountability in the electrified automotive industry; however, significant discrepancies between standard testing and real-world driving have hindered energy and environmental assessments of BEVs and their broader adoption. In this study, we propose a data-driven evaluation method for standard testing to characterize BEV energy consumption. By decoupling the impact of the driving profile, our evaluation approach is generalizable to various driving conditions. In experiments with our approach for estimating energy consumption, we achieve a 3.84% estimation error for 13 different multiregional standardized test cycles and a 7.12% estimation error for 106 diverse real-world trips. Our results highlight the great potential of the proposed approach for promoting public awareness of BEV energy consumption through standard testing while also providing a reliable fundamental model of BEVs.

Integrated driving risk surrogate model and car-following behavior for freeway risk assessment.

Drivers' risk perception plays a crucial role in understanding vehicle interactions and car-following behavior under complex conditions and physical appearances. Therefore, it is imperative to evaluate the variability of risks involved. With advancements in communication technology and computing power, real-time risk assessment has become feasible for enhancing traffic safety. In this study, a novel approach for evaluating driving interaction risk on freeways is presented. The approach involves the integration of an interaction risk perception model with car-following behavior. The proposed model, named the driving risk surrogate (DRS), is based on the potential field theory and incorporates a virtual energy attribute that considers vehicle size and velocity. Risk factors are quantified through sub-models, including an interactive vehicle risk surrogate, a restrictions risk surrogate, and a speed risk surrogate. The DRS model is applied to assess driving risk in a typical scenario on freeways, and car-following behavior. A sensitivity analysis is conducted on the effect of different parameters in the DRS on the stability of traffic dynamics in car-following behavior. This behavior is then calibrated using a naturalistic driving dataset, and then car-following predictions are made. It was found that the DRS-simulated car-following behavior has a more accurate trajectory prediction and velocity estimation than other car-following methods. The accuracy of the DRS risk assessments was verified by comparing its performance to that of traditional risk models, including TTC, DRAC, MTTC, and DRPFM, and the results show that the DRS model can more accurately estimate risk levels in free-flow and congested traffic states. Thus the proposed risk assessment model provides a better approach for describing vehicle interactions and behavior in the digital world for both researchers and practitioners.

Analysis and regulation of driving behavior in the entrance zone of freeway tunnels: Implementation of visual guidance systems in China.

In China, visual guidance systems are commonly used in tunnels to optimize the visual reference system. However, studies focusing specifically on visual guidance systems in the tunnel entrance zone are limited. Hence, a driving simulation test is performed in this study to quantitatively evaluate the effectiveness of (i) visual guidance devices at different vertical positions (pavement and roadside) and (ii) a multilayer visual guidance system for regulating driving behavior in the tunnel entrance zone. Furthermore, the characteristics of driving behavior and their effects on traffic safety in the tunnel entrance zone are examined. Data such as the vehicle position, area of interest (AOI), throttle position, steering wheel angle, and lane center offset are obtained using a driving simulation platform and an eye-tracking device. As indicators, the first fixation position (FP), starting deceleration position (DP), average throttle position (TPav), number of deceleration stages (N|DS), gradual change degree of the vehicle trajectory (G|VT), and average steering wheel angle (SWAav) are derived. The regulatory effect of visual guidance devices on driving performance is investigated. First, high-position roadside visual guidance devices effectively reduce decision urgency and significantly enhance deceleration and lane-keeping performance. Specifically, the advanced deceleration performance (AD), smooth deceleration performance (SD), trajectory gradualness (TG), and trajectory stability (TS) in the tunnel entrance zone improve by 63%, 225%, 269%, and 244%, respectively. Additionally, the roadside low-position visual guidance devices primarily target the trajectory gradualness (TG), thus resulting in improvements by 80% and 448% in the TG and TS, respectively. Meanwhile, the pavement visual guidance devices focus solely on enhancing the TS and demonstrates a relatively lower improvement rate of 99%. Finally, the synergistic effect of these visual guidance devices facilitates the multilayer visual guidance system in enhancing the deceleration and lane-keeping performance. This aids drivers in early detection and deceleration at the tunnel entrance zone, reduces the urgency of deceleration decisions, promotes smoother deceleration, and improves the gradualness and stability of trajectories.

Behind the wheel: Probing into personality, skills, and driving behavior's role in bus rapid transit crashes.

OBJECTIVE: Personality traits and driving skills are significantly associated with driving behaviors and crashes. In the case of professional bus drivers, the relationships amongst these variables have not been sufficiently examined in terms of road crashes. Therefore, this study seeks to examine the relationship between personality traits, driving skills, driving behaviors, and crash involvement among Bus Rapid Transit (BRT) drivers. METHODS: The study employed a comprehensive data collection strategy involving self-reported questionnaires, including the driver behavior questionnaire, driver skill inventory, and Big Five inventory, alongside Global Positioning System (GPS)-extracted speeding data from a sample of 166 drivers. To explore the relationship between variables, the study utilized the Partial Least Squares Structural Equation Model (PLS-SEM) as the analytical method. RESULT: The findings reveal that self-reported violations and actual speeding performed by drivers were positively associated with crash involvement, whereas positive driving behavior negatively influences violation, errors, speeding and crash involvement. The study also found that the safety skills were negatively associated with violations, errors, and speeding, while higher perceptual-motor skills were associated with higher instances of speeding violations, resulting to a higher possibility of getting involved in a crash. Finally, the study reveals that certain personality traits (extraversion and neuroticism) were positively associated with violations, errors, and speeding, leading to a higher risk of getting involved in crashes, whereas certain personality traits (conscientiousness and agreeableness) were associated with safe driving. CONCLUSION: The study findings offer valuable insights into the predictors of crashes among professional BRT drivers, which can be used to enhance driving practices, ensuring the safety of the public. Moreover, these findings provide transportation agencies with better management and decision-making capabilities to implement effective interventions to improve road safety.

How job stressors and economic stressors impact public transport drivers' performance and well-being under the health risk of the COVID-19 pandemic.

INTRODUCTION: During the COVID-19 pandemic, public transport (e.g., bus and taxi) drivers encountered great stress because they needed to work to maintain the operation of the transportation system. This study proposes and empirically investigates the impacts of job stressors and economic stressors of public transport drivers on emotional exhaustion, and subsequent psychological well-being and performance under the health risk of COVID-19. The moderating effects of perceived threat and death anxiety on the relationships between stressors and emotional exhaustion are also examined. METHOD AND RESULTS: Using two survey samples collected from bus and taxi drivers in Taiwan, the results reveal that, except for the effect of time pressure on taxi drivers' exhaustion, job stressors (job overload and time pressure) and economic stressors (job insecurity) positively relate to emotional exhaustion for both bus and taxi drivers. Drivers' emotional exhaustion has negative effects on both job satisfaction and positive effects on risky driving behaviors. Perceived pandemic threat strengthens the positive influence of job insecurity on emotional exhaustion for bus drivers, while perceived pandemic threat and death anxiety weaken the negative influence of job insecurity on emotional exhaustion for taxi drivers. PRACTICAL APPLICATIONS: Effective intervention strategies and policies to mitigate perceived pandemic threat and death anxiety of drivers are recommended.

The effect of breathing hypoxic gas (15% FIO2 ) on physiological and behavioral outcomes during simulated driving in healthy subjects.

Hypoxia is mainly caused by cardiopulmonary disease or high-altitude exposure. We used a driving simulator to investigate whether breathing hypoxic gas influences driving behaviors in healthy subjects. Fifty-two healthy subjects were recruited in this study, approved by the Science and Engineering Ethical Committee. During simulated driving experiments, driving behaviors, breathing frequency, oxygen saturation (SpO2 ), and heart rate variability (HRV) were analyzed. Each subject had four driving sessions; a 10-min practice and three 20-min randomized interventions: normoxic room air (21% FIO2 ) and medical air (21% FIO2 ) and hypoxic air (equal to 15% FIO2 ), analyzed by repeated measures ANOVA. Driving behaviors and HRV frequency domains showed no significant change. Heart rate (HR; p < 0.0001), standard deviation of the RR interval (SDRR; p = 0.03), short-term HRV (SD1; p < 0.0001), breathing rate (p = 0.01), and SpO2 (p < 0.0001) were all significantly different over the three gas interventions. Pairwise comparisons showed HR increased during hypoxic gas exposure compared to both normoxic interventions, while SDRR, SD1, breathing rate, and SpO2 were lower. Breathing hypoxic gas (15% FiO2 , equivalent to 2710 m altitude) may not have a significant impact on driving behavior in healthy subjects. Furthermore, HRV was negatively affected by hypoxic gas exposure while driving suggesting further research to investigate the impact of breathing hypoxic gas on driving performance for patients with autonomic dysfunction.

High-risk event prone driver identification considering driving behavior temporal covariate shift.

Drivers who perform frequent high-risk events (e.g., hard braking maneuvers) pose a significant threat to traffic safety. Existing studies commonly estimated high-risk event occurrence probabilities based upon the assumption that data collected from different time periods are independent and identically distributed (referred to as i.i.d. assumption). Such approach ignored the issue of driving behavior temporal covariate shift, where the distributions of driving behavior factors vary over time. To fill the gap, this study targets at obtaining time-invariant driving behavior features and establishing their relationships with high-risk event occurrence probability. Specifically, a generalized modeling framework consisting of distribution characterization (DC) and distribution matching (DM) modules was proposed. The DC module split the whole dataset into several segments with the largest distribution gaps, while the DM module identified time-invariant driving behavior features through learning common knowledge among different segments. Then, gated recurrent unit (GRU) was employed to conduct time-invariant driving behavior feature mining for high-risk event occurrence probability estimation. Moreover, modified loss functions were introduced for imbalanced data learning caused by the rarity of high-risk events. The empirical analyses were conducted utilizing online ride-hailing services data. Experiment results showed that the proposed generalized modeling framework provided a 7.2% higher average precision compared to the traditional i.i.d. assumption based approach. The modified loss functions further improved the model performance by 3.8%. Finally, benefits for the driver management program improvement have been explored by a case study, demonstrating a 33.34% enhancement in the identification precision of high-risk event prone drivers.

Assessing the effectiveness of psychoeducational interventions on driving behavior: A systematic review and meta-analysis.

This review aimed to quantitatively summarize the evidence concerning the effectiveness of psychoeducational interventions on driving behavior. A final pool of 138 studies, totaling approximately 97,000 participants, was included in the analyses and covered all types of driving behavior targeted by the interventions. Using a random effects model, significant results were found for almost all driving outcomes, both post-intervention and long-term. The strongest effect was for reducing distracted driving at post-intervention (d = 1.87 [1.12, 2.60], Z = 4.94, p < 0.001). The only non-significant effects were for reducing errors in the long term (d = 0.50 [-0.87, 1.86], Z = 0.71, p = 0.48) and driving under the influence at post-intervention (d = 0.35 [0.00, 0.71], Z = 1.96, p = 0.05). Concerning which type of intervention was more effective, feedback, training and motivational ones appear to work best. Educational interventions show only weak effects, while awareness interventions seem mostly ineffective. Overall, our results show that most interventions can reduce different types of driving behaviors, but there are specific aspects to be considered based on the targeted behavior.

Research on driver's anger recognition method based on multimodal data fusion.

OBJECTIVES: This paper aims to address the challenge of low accuracy in single-modal driver anger recognition by introducing a multimodal driver anger recognition model. The primary objective is to develop a multimodal fusion recognition method for identifying driver anger, focusing on electrocardiographic (ECG) signals and driving behavior signals. METHODS: Emotion-inducing experiments were performed employing a driving simulator to capture both ECG signals and driving behavioral signals from drivers experiencing both angry and calm moods. An analysis of characteristic relationships and feature extraction was conducted on ECG signals and driving behavior signals related to driving anger. Seventeen effective feature indicators for recognizing driving anger were chosen to construct a dataset for driver anger. A binary classification model for recognizing driving anger was developed utilizing the Support Vector Machine (SVM) algorithm. RESULTS: Multimodal fusion demonstrated significant advantages over single-modal approaches in emotion recognition. The SVM-DS model using decision-level fusion had the highest accuracy of 84.75%. Compared with the driver anger emotion recognition model based on unimodal ECG features, unimodal driving behavior features, and multimodal feature layer fusion, the accuracy increased by 9.10%, 4.15%, and 0.8%, respectively. CONCLUSIONS: The proposed multimodal recognition model, incorporating ECG and driving behavior signals, effectively identifies driving anger. The research results provide theoretical and technical support for the establishment of a driver anger system.

Optimization of colored pavement considering driving behavior and psychological characteristics under dynamic low-visibility conditions related to fog-a driving simulator study.

OBJECTIVE: Colored pavement is commonly used to reduce the road traffic risk and promote road traffic safety, but its performance in foggy environments has not been fully assessed. The goal of this research is to explore the effectiveness and optimization of colored pavement in a dynamic low-visibility environment. METHODS: A driving simulation experiment is conducted. Three road risk sections in which collisions are common, including a long straight section, a sharp bend section, and a long downslope section, are considered, and three forms of colored pavement are used in five different visibility environments. The effectiveness of the colored pavement is explored by collecting and analyzing driving behavior and physiological characteristic data for 30 drivers in the established driving environment, and information is obtained through a subjective colored evaluation questionnaire. Eight evaluation indexes are selected from the perspectives of driving behavior and physiological characteristics, and the gray premium evaluation method is applied to evaluate the effectiveness of different forms of colored pavement considering the influence of visibility. Finally, the optimal colored pavement under various visibility and road alignment conditions is proposed. RESULTS: The results show that reasonably selecting colored pavement can effectively improve drivers' behaviors and physiological characteristics under foggy conditions. For different road alignments and visibility conditions, different forms of colored pavement should be used to ensure road traffic safety. CONCLUSIONS: The findings provide a theoretical reference for the optimization of colored pavement in foggy conditions.

Effects of insomnia on risky driving behavior among bus drivers: The mediating effect of mental health.

Crashes caused by problems with bus drivers' physical and mental health have increased in frequency in recent years. Insomnia, a common type of sleep problem, has significant positive relationships with both crash risk and mental health problems, especially anxiety and depression, which are themselves associated with driving behavior. However, few studies have conducted analysis on sleep-related problems and mental health exclusively on bus drivers, nor on how these problems influence driving performance. Thus, this study explored the effect of insomnia and mental health on bus drivers' risky driving behavior and evaluated the interaction of four variables: insomnia, anxiety, depression, and risky driving behavior. The survey-based investigation was conducted in a bus company in Suzhou, China, with 1,295 bus drivers participating. Insomnia, anxiety, and depression were self-reported based on professional mental health scales and risky driving behaviors were measured by the Driver Behavior Questionnaire. Two mediation models and a chain mediation model were developed to examine relationships among the bus drivers' insomnia, anxiety, depression, and risky driving behavior. Results revealed that (a) bus drivers less than 31 years old, drivers with more than 11 years' experience driving buses, and those with crash and violation involvement within three years demonstrated more severe degrees of insomnia, anxiety, depression, and risky driving behavior; (b) there were significant positive correlations and interactions among the four variables. Results specifically related to the interaction among variables include findings that (a) anxiety mediated between insomnia and risky driving behavior; (b) depression mediated between insomnia and risky driving behavior; and (c) anxiety affected bus drivers' risky driving behavior primarily though depression. The findings in this study indicate the importance of regular physical and mental health examination of bus drivers and suggest that interventions focused on insomnia and mental health problems may be helpful to reduce risky driving behaviors of bus drivers both directly and indirectly.

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.

The short-term impact of a referendum on motor vehicle collisions casualties.

OBJECTIVE: A referendum took place in Greece on the 5th of July 2015 to decide whether the suggested bailout would be accepted. Since this was the first one since 1974, the period between the referendum and the subsequent national elections was characterized by increased uncertainty and had spillover effects in many aspects of everyday life. We take advantage of this quasi-experiment to investigate the short-term impact of the referendum on vehicle collisions casualties. METHODS: We use data from the daily number of injuries and fatalities caused by vehicle collisions in 2015 and employ a difference-in-differences approach, comparing trends before and after the referendum. RESULTS: We reveal that the referendum had a short-term impact on road traffic casualties (4.14 more casualties per day), compared to what would have been expected in the absence of the referendum. CONCLUSIONS: The study provides evidence that negative emotions and anxiety, due to uncertainty, could promote dangerous driving behavior. Preventive and traffic control measures may need to be considered by policy makers during periods of uncertainty.