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Driving, as a multifaceted daily task, is intricately intertwined with our emotional states. However, the integration of affective states into driving research is conspicuously lacking in the existing literature. The primary goal of this study is to systematically examine and elucidate the relationship between these affective states and driving actions, with a particular focus on driving behavior. The review synthesizes findings from 21 articles that specifically address the interplay between emotional states and driving behaviors. Despite the complexity and variability of the relationship between emotional states and driving, the review highlights that both positive and negative affect are associated with risky and reckless driving behaviors. The synthesis of findings presented in this review serves as a comprehensive and holistic overview of affect-integrated driving studies. Based on the findings, future intervention techniques, such as in-vehicle assistance systems, could be developed to assist drivers in managing their emotional states while driving. [ABSTRACT FROM AUTHOR]

Abstract Background Teen drivers with a traffic violation are at increased risk for crashes and crash-related injuries; however, most parent-focused interventions target teen drivers with supervised learner’s permits. Very few interventions are implemented at the probationary driver's license stage or target high-risk teen drivers, such as those with traffic violations. This paper describes the protocol of ProjectDRIVE, A Randomized Controlled Trial to Improve Driving Practices of High-Risk Teen Drivers with a Traffic Violation, which targets improving parent-teen communication about safe driving practices to reduce unsafe driving behaviors and traffic violation recidivism of teen drivers cited for traffic violation. Methods Teen drivers (ages 16 or 17) cited for a moving violation and the parent/legal guardian most involved with the teen’s driving are recruited from juvenile traffic courts following their required court hearing. After completing informed consent/assent, enrolled dyads are randomized into one of three groups using stratified block randomization: control, device feedback only, or device feedback plus parent communication training. Participating dyads are followed for 6 months with 3 months of active intervention. Using in-vehicle device and smartphone application technology, the study provides real-time and cumulative driving feedback to intervention teens and collects continually recorded, objectively measured driving outcome data throughout the teen’s study participation. Primary outcomes include rates of risky driving events and unsafe driving behaviors per 1000 miles driven. Secondary outcomes include traffic violation recidivism up to 12 months following study completion and frequency and quality of parent-teen communication about safe driving practices. Discussion Through partnership with the local juvenile traffic courts, this study integrates recruitment and randomization into existing court practices. Successfully completing this study will significantly impact juvenile traffic court’s practices and policies by informing judges’ decisions regarding the driving safety programs they refer to teens to prevent motor vehicle crashes and crash-related injuries and deaths. Trial registration The study was registered on ClinicalTrials.gov Registry (NCT04317664) on March 19, 2020, https://clinicaltrials.gov/study/NCT04317664 and updated on April 27, 2021. This protocol was developed per the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) Checklist.

Background: Teen drivers with a traffic violation are at increased risk for crashes and crash-related injuries; however, most parent-focused interventions target teen drivers with supervised learner's permits. Very few interventions are implemented at the probationary driver's license stage or target high-risk teen drivers, such as those with traffic violations. This paper describes the protocol of ProjectDRIVE, A Randomized Controlled Trial to Improve Driving Practices of High-Risk Teen Drivers with a Traffic Violation, which targets improving parent-teen communication about safe driving practices to reduce unsafe driving behaviors and traffic violation recidivism of teen drivers cited for traffic violation. Methods: Teen drivers (ages 16 or 17) cited for a moving violation and the parent/legal guardian most involved with the teen's driving are recruited from juvenile traffic courts following their required court hearing. After completing informed consent/assent, enrolled dyads are randomized into one of three groups using stratified block randomization: control, device feedback only, or device feedback plus parent communication training. Participating dyads are followed for 6 months with 3 months of active intervention. Using in-vehicle device and smartphone application technology, the study provides real-time and cumulative driving feedback to intervention teens and collects continually recorded, objectively measured driving outcome data throughout the teen's study participation. Primary outcomes include rates of risky driving events and unsafe driving behaviors per 1000 miles driven. Secondary outcomes include traffic violation recidivism up to 12 months following study completion and frequency and quality of parent-teen communication about safe driving practices. Discussion: Through partnership with the local juvenile traffic courts, this study integrates recruitment and randomization into existing court practices. Successfully completing this study will significantly impact juvenile traffic court's practices and policies by informing judges' decisions regarding the driving safety programs they refer to teens to prevent motor vehicle crashes and crash-related injuries and deaths. Trial registration The study was registered on ClinicalTrials.gov Registry (NCT04317664) on March 19, 2020, https://clinicaltrials.gov/study/NCT04317664 and updated on April 27, 2021. This protocol was developed per the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) Checklist. [ABSTRACT FROM AUTHOR]

A railway level crossing is a risk area despite installations of the warning signal and arm gate that indicate a train approach. Accidents may nevertheless occur if vehicle drivers violate or fail to conform to the rules. Such action is considered a risk behavior that may cause severe collision, injury, and death. Thus, the driver behaviors are the key factors causing accidents. The objective of this study was thus to investigate the risk behaviors and the factors influencing above behaviors. These behaviors were recorded by a high-resolution video camera and the initial data analysis was based on descriptive statistics. An in-depth analysis of the factors influencing the risk behaviors was performed by using Pearson Chi-square technique and Binary Logistic Regression Analysis. The results showed that the railway crossing behaviors of more than half of the drivers under the study were the risk-taking, followed by opportunistic violation. Most violations occurred during the beginning of the warning signal. Overall, the motorcycle riders were found to be the group with greater chance of violating across the railway during the warning signal than the 4-wheeled vehicle drivers. Especially, during the period between the beginning of the warning signal and the complete arm gate closure, as well as the greater width of the crossing road. This could be due to the higher agility and evasive capability of motorcycle riders than 4-wheeled drivers. Additionally, this study recommends approaches to initially prevent or reduce this risky behavior and can serve as a guideline for enhancing areas with physical characteristics similar to the study area. [ABSTRACT FROM AUTHOR]

Distracted driving is one of the most significant factors leading to fatal car crashes. Using a cell phone while driving is one of the riskiest behaviors while driving and is the cause of death for hundreds of drivers in the United States. Distraction prevention technologies, such as cell phone blocking apps that limit the functioning of cell phones while the car is moving, are one strategy for combating distracted driving. The main goal of this study is to investigate the effect of cell phone blocking apps on driving behaviors and crashes caused by distracted driving using a machine learning algorithm. Some 158 participants were recruited from the state of Maryland to investigate their driving behavior using a state-specific survey. The results of the survey revealed that most people have cell phone blocking apps (62.6%); however, they do not use them on a daily basis (86.7%). A Bayesian network model was then deployed, and the results showed that if all drivers use cell phone blocking apps, crashes occurring due to distraction from cell phone use will decrease by 5 %, and self-reported distraction will decrease by 9 %. The results of this study can be used to detect distracted driving and find the best strategies to overcome this problem. The results also suggest that there should be a greater degree of awareness of distraction prevention technologies and education on the use of these technologies among different groups to reduce the number of fatalities, injuries, and crashes due to distraction.

The use of autonomous vehicles (AVs) in transportation at signalized intersections is proposed to improve safety, efficiency, and sustainability. The discussion shall thus focus more on the impacts of different AV driving behaviors to some key transportation metrics solely at signalized intersections. Among the parameters considered are travel time, queue length, delay, and speed. This is clear from one such holistic study that Cautious AV behavior would reduce accidents, but on the other way around, it might increase traffic delays and lead to congestion at signalized junctions. Aggressive AVs will improve the flow of traffic but face problems in safety in this kind of set-up. Normal AV operation A balanced approach that offers intermediary levels of travel time and safety. The review further investigates the environmental effects of various driving patterns within signalized intersections, where it notes that there are noticeable differences in emissions and fuel consumed if the driving behavior is taken into consideration. It goes further into the implications for traffic management and control systems, noting challenges and opportunities while integrating AVs into existing infrastructure and spotlighting at signalized intersections. Paramount to the review are the considerations of safety, regulatory frameworks, and mitigating strategies relevant to AV behavior at signalized intersections. In so doing, this review seeks to inform future research and policy decisions with a nuanced understanding of how various AV driving behaviors affect signalized intersection performance, seeking to optimize AV benefits while mitigating possible risks at critical traffic junctures. [ABSTRACT FROM AUTHOR]

Introduction: Maladaptive behavior often results from poor decision-making and by extension poor control over decisions. Since maladaptive behavior in driving, such as excessive speed, can lead to dramatic consequences, identifying its causes is of particular concern. The present study investigated how risk-taking and executive functioning are related to driving performance and habits among the general population. Method: Five hundred and eighty-nine participants completed an on-road driving session with a professional driving instructor and a self-reported checklist of difficult driving situations typically avoided. Additionally, participants completed a set of experimental tasks assessing risk-taking tendencies, reactive adaptive mechanisms, and two distinct forms of inhibition: interference control and response inhibition. Results: The results of the present study revealed several significant findings. Firstly, poor driving performance was associated with a high avoidance of challenging driving situations. Secondly, neither form of inhibition studied (interference control or response inhibition) predicted driving performance. Thirdly, while greater involvement in reactive adaptive mechanisms did not predict better on-road performance, it was associated with a reduced tendency to avoid difficult situations. Surprisingly, a higher propensity for risk-taking predicted better on-road performance. Discussion: Overall, these results underline limited links between executive functioning and driving performance while highlighting a potentially more complex relationship between risk-taking tendencies and driving. Executive functioning, however, appears to be linked to driving habits. [ABSTRACT FROM AUTHOR]

Distracted driving is one of the most significant factors leading to fatal car crashes. Using a cell phone while driving is one of the riskiest behaviors while driving and is the cause of death for hundreds of drivers in the United States. Distraction prevention technologies, such as cell phone blocking apps that limit the functioning of cell phones while the car is moving, are one strategy for combating distracted driving. The main goal of this study is to investigate the effect of cell phone blocking apps on driving behaviors and crashes caused by distracted driving using a machine learning algorithm. Some 158 participants were recruited from the state of Maryland to investigate their driving behavior using a state-specific survey. The results of the survey revealed that most people have cell phone blocking apps (62.6%); however, they do not use them on a daily basis (86.7%). A Bayesian network model was then deployed, and the results showed that if all drivers use cell phone blocking apps, crashes occurring due to distraction from cell phone use will decrease by 5 %, and self-reported distraction will decrease by 9 %. The results of this study can be used to detect distracted driving and find the best strategies to overcome this problem. The results also suggest that there should be a greater degree of awareness of distraction prevention technologies and education on the use of these technologies among different groups to reduce the number of fatalities, injuries, and crashes due to distraction. • The results revealed that most people have cell phone blocking apps (62.6%); however, they do not use them (86.7%). • The results showed that if all drivers use cell phone blocking apps, crashes occurring due to distraction will decrease. • With using cell phone blocking apps, self-reported distraction will decrease by 9%. • The results can be used to detect distracted driving and find the best strategies to overcome this problem. [ABSTRACT FROM AUTHOR]

This manuscript discusses how familiarization with an experimental driving course in a driving simulator would influence driving behaviors in terms of manipulation of the gas/brake pedal, ability to stay in one's lane, and driving speed. Generally, when conducting an experiment using a driving simulator to gain new findings, an experimental driving course is required and the influence of familiarization with the course affecting the results is inevitable. Traditionally, to remove this influence of familiarization with driving courses from a targeted evaluation criterion, predictable driving courses such as straight-based roads like city roads and expressways, roads with gentle curves like country roads and mountain ones, are employed. However, it is still questionable whether findings from those driving courses would hold for variation of driving courses which play an important role in understanding the findings in reality. This manuscript focuses on the progress of familiarization with a driving course and clarifies what properties of driving behaviors are influenced from it. The findings show that people control speed of a car with fewer presses of the acceleration pedal (the number of counts of pushing the gas pedal decreased by -11.6%) with less pressure applied (degree of pushing decreased by -12.8%), leading to driving more slowly as they drive around 7 ∼ 9 driving laps. In addition, they manipulate the steering wheel with a smaller operating range (standard deviation of angular position of the steering wheel decreased by -5.7%), so that lane keeping becomes more precise (lateral deviation decreased by -28.7%) as they drive around 4 ∼ 6 driving laps. Finally, user familiarization with driving courses is dealt with in this manuscript and our ultimate goal is to quantify user familiarization, predict its level, and give drivers feedback on it in real time so that they are warned to be drive carefully. The outcome of this manuscript is a first step and quantify the user familiarization. [ABSTRACT FROM AUTHOR]

IntroductionMaladaptive behavior often results from poor decision-making and by extension poor control over decisions. Since maladaptive behavior in driving, such as excessive speed, can lead to dramatic consequences, identifying its causes is of particular concern. The present study investigated how risk-taking and executive functioning are related to driving performance and habits among the general population.MethodFive hundred and eighty-nine participants completed an on-road driving session with a professional driving instructor and a self-reported checklist of difficult driving situations typically avoided. Additionally, participants completed a set of experimental tasks assessing risk-taking tendencies, reactive adaptive mechanisms, and two distinct forms of inhibition: interference control and response inhibition.ResultsThe results of the present study revealed several significant findings. Firstly, poor driving performance was associated with a high avoidance of challenging driving situations. Secondly, neither form of inhibition studied (interference control or response inhibition) predicted driving performance. Thirdly, while greater involvement in reactive adaptive mechanisms did not predict better on-road performance, it was associated with a reduced tendency to avoid difficult situations. Surprisingly, a higher propensity for risk-taking predicted better on-road performance.DiscussionOverall, these results underline limited links between executive functioning and driving performance while highlighting a potentially more complex relationship between risk-taking tendencies and driving. Executive functioning, however, appears to be linked to driving habits.

• Dutch drivers behave less aggressively and committed fewer violations. • Iranian drivers were more inclined to commit risky behaviors such as speeding or illegal overtaking. • Iranian drivers were more prone to breaking traffic laws across a range of situations. • The SEM results indicated a higher probability of crashes for those committing more violations. • Drivers who made more driving errors were more likely to be involved in crashes. Introduction : The rates of road traffic injuries and fatalities in developing countries are significantly higher than in developed countries. This study examines the differences in driving behavior, road safety attitudes, and driving habits between a developed country (the Netherlands) and a developing country (Iran), which bear major differences in terms of crash involvement per population. Method : In this context, this study assesses the statistical association of crash involvement with errors, lapses, aggressive driving incidents, and non-compliance with traffic rules, attitudes, and habits. Structural equation modeling was used to evaluate data obtained from 1,440 questionnaires (720 samples for each group). Results : The results revealed that more insecure attitudes toward traffic-regulation observance, negative driving habits, and risky behaviors, such as traffic rule violations act as influential factors of crash involvement. Iranian participants showed a greater likelihood to get involved in violations and driving habits with a higher level of risk. In addition, lower levels of safety attitudes toward traffic-regulation observance were observed. On the other hand, Dutch drivers were more likely to report lapses and errors. Dutch drivers also reported safer behavior in terms of unwillingness to engage in risky behaviors such as violations (speeding and no-overtaking). The structural equation models for crash involvement based on behaviors, attitudes, and driving habits were also evaluated for their accuracy and statistical fit using relevant indicators. Practical Applications : Finally, the findings of the present study point out the need for extensive research in some areas to foster policies that can effectively enhance safer driving. [ABSTRACT FROM AUTHOR]

The effectiveness of regenerative braking strategies plays an important role in extending the driving range of electric vehicles. Since the driver is still an essential factor in levels 3 and 4 of intelligent electric vehicles, improving user acceptance and adoption of the braking control strategy is crucial. This paper puts forward a new regenerative braking strategy to find a compromise between optimal braking control performance and naturalistic regeneration performance while satisfying the maximum speed preference when driving between two-stop events. Unlike other similar works that only maximize regenerative braking energy while satisfying the physical limits of an electrified powertrain, this paper considers naturalistic regeneration performance. To achieve this, firstly, the power regenerated by three drivers is predicted with a long-horizon (30 seconds), using long-short-term memory networks (LSTM) and non-linear autoregressive exogenous model (NARX). Subsequently, an estimation of the energy recovery maximization rate is performed to give a perception of the naturalistic regeneration performance. As this performance varies, the deceleration planning employs three horizon scales of long, medium, and short, determined by the energy recovery maximization rate. Finally, dynamic programming (DP) is utilized to optimize a deceleration profile. The study utilizes real data of inverter efficiency, transmission efficiency, and motor-to-battery efficiency map. The outcome of this study shows that the proposed regeneration braking strategy is adaptive, improving regeneration efficiency by 39,6% for driver 1, 16% for driver 2, and 26% for driver 3, and forecasting the optimality of some deceleration behaviors.

• Self-reported (SR) driving errors significantly worsened with the number of positive items from the STOP-BA(N)G. • Reaction times were worse for participants at higher risk of OSA when compared to participants at lower risk of OSA. • SR driving errors and SR driving safety are particularly worse for individuals with longer reaction times. Sleep disorders have been implicated as a cause of motor vehicle crashes in select populations through reduced cognitive function. We investigated the impact of insomnia and obstructive sleep apnea (OSA) on reaction times and driving performance in the population-based cohort of older adults in the Canadian Longitudinal Study on Aging (CLSA). Risk of OSA was classified by the positive responses to the STOP-BA(N)G questionnaire. Time-dependent cognitive function was formally evaluated in all participants with choice reaction times (CRT). Of 30,097 participants, 2657 (8.8 %) met criteria for insomnia and 2092 (7.0 %) were classified as high risk of OSA. Insomnia was equally distributed in all risk categories for OSA. Both self-reported lane position errors and driving safety were more often described as worse in moderate and higher risk groups for OSA although there was also a marginal association with improved driving safety. This is in spite of the fact that daily driving frequency was reported more often with higher risk categories of OSA. Choice reaction times were on average longer in both moderate (845.7 ± 245 msec) and higher (860.2 ± 263 msec) risk groups for OSA compared to participants with low risk of OSA (860.2 ± 266 msec). Insomnia was not associated with driving performance, lane position errors and CRT. Subgroups with categories based upon OSA risk groups with and without insomnia showed no distinct relationship beyond the association with OSA risk categories alone. Too few responses were recorded to assess near miss and motor vehicle crashes. We conclude that objective slower reaction times and poorer self-reported driving performance were associated with the risk of OSA but not insomnia. [ABSTRACT FROM AUTHOR]

A novel computational model for the volatile state was developed to improve eco-driving in intelligent transportation systems (ITS). First, the volatile state was divided into eight types using vehicle acceleration and jerk as delineating criteria. Data analysis showed that each jerk type had a different proportion and contribution level to fuel consumption. Next, the model was created by considering eight instantaneous driving decisions as represented by vehicle speed, acceleration, and jerk. The model input included vehicle speed multiplied by acceleration, with jerk as a classifier. The model was calibrated using quadratic polynomial fitting, and validated using another portion of the data. Finally, predictions were compared with the widely used Vehicle Specific Power (VSP) model and the Virginia Tech Microscopic (VT-Micro) model to evaluate model performance. The new model thoroughly captured the measured fuel consumption and provided more accurate predictions in new routes than the above-mentioned models. The mean absolute percentage error value of the new model was ∼4.9% and 3.2% lower than those of the VSP and VT-Micro models, respectively. The determinant coefficient value was up to 95.8%, which was ∼4.6% and 8.5% higher than those of the VSP and VT-Micro models, respectively. [ABSTRACT FROM AUTHOR]

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Based on the present situation of usage based insurance (UBI) research and application, this paper puts forward the UBI rating model based on driving behavior classification, and applies the technology of data mining to the evaluation of driving behavior. The actual driving behavior data and the risk data of 400 drivers are used as experimental data. Finally, an example shows that the driving behavior classification model is superior to the driving behavior score model for the identification of accident risk, which can make UBI rate more scientific and reasonable.

Autonomous vehicles (AVs) have been a rapidly emerging phenomenon in recent years, with some automated features already available in vehicles. AVs are expected to potentially revolutionize the existing inefficient state of urban transportation and be a step closer to environmental sustainability. This study focuses on simulation modeling in assessing the potential effects of autonomous vehicles (AVs) and on mobility and safety by developing a framework model based on traffic microsimulation for a real network located in Al-Madinah, Saudi Arabia. The market penetration rates (MPRs) will not reach 100% in the near future; instead, penetration will progressively increase. As a result, in our study, we investigated the potential effect of AV technology in five different AV market penetration rates: 0% (baseline), 25%, 50%, 75%, and 100%. The results suggest that Avs significantly improve the network's safety and operational performance at high penetration rates. Specifically, estimated vehicle delays decreased by 26%, 34.4%, 63.7%, and 74.2% for 25%, 50%, 75%, and 100% AV penetration rates, respectively. Finally, we think this study will help decisionmakers over in the long-term in their attempts to achieve sustainable development through the optimal integration of innovative and novel technologies. [ABSTRACT FROM AUTHOR]

• Emotional intelligence predicted dangerous driving behaviors. • Lower emotionality increased dangerous driving behaviors. • State and federal administrations should promote emotional intelligence. • Emotional intelligence should be included in driving education and traffic safety programs. Dangerous driving behaviors have been found to be a leading contributor to vehicle crashes and fatalities, with more than 2.7 million people injured and 36,560 people killed in the United States in 2018 (NHTSA, 2020). Drivers' emotions have been found to be among the leading contributors to dangerous driving behaviors. Emotions can be measured and understood through one's emotional intelligence (EI). Previous research has confirmed the relationship between EI and dangerous driving behaviors among general driving populations in limited scope. This study analyzed dangerous driving behaviors (e.g., aggressive driving) among non-commercial US drivers. 615 US drivers ages 18 to 65 (M = 31.14, SD = 11.15) with valid US driver's licenses (non-commercial) participated in this study. Participants completed an online survey through Qualtrics that included the Trait Emotional Intelligence Questionnaire (TEIQue-SF) to measure different dimensions of EI and the Dula Dangerous Driving Index (DDDI) and the Driving Behavior Questionnaire (DBQ) to measure dangerous driving behaviors. Furthermore, participants reported their demographic information, including age, sex, and location. Correlation analysis revealed that significant associations exist between dangerous driving behaviors and EI. The emotionality component of EI was found to be the strongest predictor of dangerous driving behaviors. The findings concluded that participants with higher EI scores engaged in less dangerous driving behavior, resulting in fewer crashes and fatalities. Thus, promoting and improving EI may be useful in preventing risky driving among non-commercial drivers. Incorporating emotional intelligence education in driver's education, workplace training, and licensing procedures can be helpful to develop safer drivers. Further research is needed to investigate commercial drivers' behaviors in relation to EI. [ABSTRACT FROM AUTHOR]

Energy consumption is the key to restrict the development of electric vehicles (EV), which is heavily affected by complex driving behaviors. In this paper, we propose a classified driving behavior based energy consumption prediction model, as well as recommended mechanisms for energy-efficient driving. Firstly, utilizing six EVs, we collect real data related to driving behaviors and energy consumption of vehicle in one year. After clustering behaviors of drivers, we present an energy consumption predication model, which accurately forecast the energy consumption caused by different driving behaviors. Motivated by the model, energy-saving strategies are proposed to recommend suitable driving behaviors. The simulation results further indicate that the accuracy of the proposed model is up to 98%. Specifically, the proposed model is less dependence on data volume, which guarantees the precision of more than 96% when the data volume is very small.

Road traffic signs are part of the road infrastructure components meant to ensure the safe use of roads by motorists and pedestrians alike. A good knowledge and comprehension of road signs helps ensure smooth flow of traffic, improves safety for other motorists and provides drivers with good reaction time to comply with the message dictated by the signs. Abu Dhabi, being the capital of the UAE, brings together people from all over the world for work, business or tourism. Abu Dhabi has quality roads and traffic signage together with an efficient police force that enforces adherence to traffic rules. Despite all these and the reduced fatalities in absolute terms, traffic violations have been increasing exponentially, resulting in five million traffic fines in 2019 for a population of about 2.9 million inhabitants. This study sought to assess motorists' comprehension of the various traffic signs used across the UAE. The results of the survey (N = 200) revealed that the drivers were able to correctly identify 77% of the road signs in the survey. It was also found that the respondents failed to correctly identify traffic signs within the specific category referred to as "advance warning signs". Respondents' education, nationality, gender, and marital status did not have any statistically significant effects on the results compared to earlier studies. It is recommended that more attention be focused on driver education and training. [ABSTRACT FROM AUTHOR]

The quantification and estimation of the driving style are crucial to improve the safety on the road and the acceptance of drivers with level2–level3(L2–L3) intelligent vehicles. Previous studies have focused on identifying the difference in driving style between categories, without further consideration of the driving behavior frequency, duration proportion properties, and the transition properties between driving style and behaviors. In this paper, a novel methodology to characterize the driving style is proposed by using the State–Action semantic plane based on the Bayesian nonparametric approach, i.e., hierarchical Dirichlet process–hidden semi–Markov model (HDP–HSMM). This method segments the time series driving data into fragment clusters with similar characteristics and construct the State–Action semantic plane based on the statistical characteristics of the state and action layer to label and interpret the fragment clusters. This intuitively and simply visualizes the driving performance of individual drivers, while the risk index of the individual drivers can also be obtained through semantic plane. In addition, according to the joint mutual information maximization (JIMI) approach, seven transition probabilities of driving behaviors are extracted from the semantic plane and applied to identify driving styles of drivers. We found that the aggressive drivers prefer high–risk driving behaviors, and the total duration and frequency of high–risk behaviors are greater than those of cautious and normal drivers. The transition probabilities among high–risk driving behaviors are also greater compared with low–risk behaviors. Moreover, the transition probabilities can provide rich information about driving styles and can improve the classification accuracy of driving styles effectively. Our study has practical significance for the regulation of driving behavior and improvement of road safety and the development of advanced driver assistance systems (ADAS). [ABSTRACT FROM AUTHOR]

The advent of Connected Vehicles (CVs) is creating new opportunities within the transportation sector. It is, indeed, expected to improve road traffic safety, enhance mobility, reduce fuel consumption and gas emissions, as well as foster economic growth via investments and jobs. However, to motivate the deployment of CVs and maximize their related benefits, policymakers must create appropriate neutral legal frameworks. These frameworks should promote the innovation of current road infrastructures, support cooperation and interoperability between transportation systems, and encourage fair competition between companies while upholding consumer privacy as well as data protection. We argue that policymakers should also support innovative mobility services toward a better accommodation of individual drivers and vehicles. Within this scope, we are proposing in this paper an intelligent approach that promotes the implementation of personalized road policies based on driving behaviors, driving performance, and the ongoing road traffic situation. These policies, which are dynamic in space and time, ultimately aim to increase drivers’ awareness by encouraging behavioral self-regulation. To meet our goals, we are using software agents that autonomously manage the driving behaviors according to well-defined transitions between driving states while enabling appropriate message exchanges between CVs. We run software simulations as well as field tests and obtained promising results that would reflect the relevance of implementing our vision of personalized policies.

This research was an investigation of changes in driving behavior that occurs in response to rainfall intensity, especially focusing on risky behaviors. This was done using driving records of 620 taxis in Seoul (South Korea). We utilized driving volatility as a quantitative measure of driving behavior. This parameter indicates the variability of vehicle movement as indicated by vehicular acceleration and jerk. The result verified that, as the rainfall intensity increases, driving patterns deviate more from those without rainfall. From these changes, a measure of aggressiveness was derived considering these behavioral differences under different rainfall conditions. In particular, volatile and risky driving decisions with respect to jerk occur more frequently as rainfall intensity increases. This implies that changes in acceleration (e.g., acceleration after deceleration, deceleration after acceleration) are prevalent in rainy days. Furthermore, using crashes and law violation information about taxis, this research verified that higher volatility is related to a higher likelihood of crashes and law violations. The contributions of this study are that it quantifies the aggressiveness of drivers as a reflection of changing driving behavior under different rainfall conditions, and verifies the volatility index by relating to crashes and traffic law violations of individual drivers. [ABSTRACT FROM AUTHOR]

Data exchange in Vehicle-to-vehicle (V2V) communications systems is a field that requires automated solutions, tools and methods and the capability to facilitate early detection and even a prediction. Many studies have focused on V2V system and its classification to improve road safety, reduce traffic congestion and help streamline the vehicle flow on the road. This study aims to review and analyse literature related to data exchange in V2V communications systems. The factors considered to improve the understanding of the field's various contextual aspects were derived from published studies. We systematically searched all articles about the classification and detection of data exchange in vehicles, as well as their evaluation. Three main databases, namely, ScienceDirect, Web of Science and IEEE Xplore from 2008 to 2018, were used. These indices were considered sufficiently extensive to encompass our literature. On the basis of our inclusion and exclusion criteria, 140 articles were selected. Most articles (53/140) are studies conducted in a V2V communication system; a number of papers (51/140) covered the actual attempts to develop V2V communications; and few papers (18/140) comprised framework proposals and architectures. The last portion (18/140) of articles presented review and survey articles. V2V collision avoidance system, which is a field requiring automated solutions, tools and methods, entails the capability to facilitate early detection. Several studies have been performed on the automatic detection of V2V and their subtypes to promote accurate detection. The basic characteristics of this emerging field are identified from the aspects of motivations, open challenges that impede the technology's utility, authors' recommendations and substantial analysis of the previous studies are discussed based on seven aspect (devices, number of scenario, test location, types of sensors, number of vehicle, evaluation techniques used and types of software). A propose research methodology as new direction is provided to solve the gaps identified in the analysis. This methodology consists of four phases; investigation, develop a hardware system, study and analysis, and evaluation phases. However, research areas on V2V communication with the scope of data exchange are varied. This systematic review is expected to open opportunities for researchers and encourage them to work on the identified gaps.

With more commercialized automated vehicles (AVs) shortly entering the market, evaluating their fuel economy has become an important topic. The traditional fixed-profile test methods only indicate the effect of powertrain efficiency on fuel economy and cannot reflect the influences of control algorithms or driving behaviors on fuel consumption under real-world traffic conditions. Therefore, a data-driven simulation method for evaluating the real driving fuel economy of automated vehicles is developed. It utilizes naturalistic driving data to reconstruct test cycles. This method can inspect the performance of automated vehicle control algorithms under realistic traffic conditions in terms of fuel economy. The naturalistic driving data collected on urban expressways and freeways were used to model the longitudinal driving scenarios. Then, the fuel consumption of automated and human-driven vehicle was evaluated under the simulated scenarios via the Monte Carlo integration approach. It was found that the fuel consumption rate of the automated vehicle was 11.944 L/100 km under the car-following scenarios. The human-driven vehicle had a fuel consumption rate of 10.124 L/100 km under the same traffic conditions. The tested automated vehicle control algorithm is tuned to achieve better performance in terms of safety and travel efficiency and hence it tends to maintain a relatively steady time headway to the leading vehicle. It applies more frequent accelerating/braking cycles and higher average speed compared to typical human drivers. These characteristics lead to a higher fuel consumption rate of the automated vehicle. The presented method provides an accurate and efficient way to analyze the fuel economy performance of automated vehicles under practical conditions. This method can easily be scaled for large-scale traffic flow analyses. It can also be used to study the effects of human driving styles on fuel economy.

The acceleration profile of leading vehicles at intersections is critical for emission estimation and microlevel queue simulation. Data obtained from experiments using a high-resolution driving simulator can deliver useful insights into microscale acceleration behaviors at signalized intersections. Acceleration data of the leading vehicles in queues are collected by the simulator. The observed accelerations are found to be stochastic. The acceleration characteristics are also significantly diversified among participants. Hence, a Markov chain is implemented to simulate the acceleration behaviors. The acceleration data are classified into varied operation states. And the Markov chain reconstructs the acceleration profiles of leading vehicles and reproduces the randomness of acceleration behaviors. Among numerous candidate profiles, a speed profile is selected by a proposed criterion that represents the typical acceleration behaviors at signalized intersections. [ABSTRACT FROM AUTHOR]

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The acceleration profile of leading vehicles at intersections is critical for emission estimation and microlevel queue simulation. Data obtained from experiments using a high-resolution driving simulator can deliver useful insights into microscale acceleration behaviors at signalized intersections. Acceleration data of the leading vehicles in queues are collected by the simulator. The observed accelerations are found to be stochastic. The acceleration characteristics are also significantly diversified among participants. Hence, a Markov chain is implemented to simulate the acceleration behaviors. The acceleration data are classified into varied operation states. And the Markov chain reconstructs the acceleration profiles of leading vehicles and reproduces the randomness of acceleration behaviors. Among numerous candidate profiles, a speed profile is selected by a proposed criterion that represents the typical acceleration behaviors at signalized intersections. [ABSTRACT FROM AUTHOR]

Introduction Varenicline is an effective smoking cessation medicine. However, the possible adverse neuropsychiatric events reported by Food and Drug Administration for varenicline may cause safety problems for professional drivers. We aimed to investigate its safety and impacts on driving behaviors among taxi drivers in Beijing, China. Methods An observational cohort study was conducted in a smoking cessation clinic in Beijing, China, between September 2017 and April 2018. Smokers with varenicline for smoking cessation were included and categorized into taxi-driver smokers (n=103) and non-taxi-driver smokers (n=119). All participants received varenicline up to 12 weeks and five standardized counseling sessions. Treatmentrelated adverse events (AEs) were collected in all participants and their impacts on driving behaviors were assessed in taxi-driver smokers. Multiple logistic regression analysis was used to examine potential risk factors for vareniclinerelated somnolence/fatigue. Results The incidence of most treatment-related AEs was similar between taxidriver smokers and non-taxi-driver smokers, but treatment-related somnolence/ fatigue was more frequently reported in taxi-driver smokers (18.4% vs 6.7%; p=0.008). Most taxi-driver smokers (87.4%) reported that treatment-related AEs did not affect their driving behaviors, and no traffic accident was reported during treatment. Conclusions Varenicline appears to be a well-tolerated smoking cessation aid for Beijing taxi drivers and has less impact on driving behaviors. However, taxidriver smokers were more likely to report somnolence/fatigue during varenicline treatment and physicians should pay more attention to this occupational population.

• Visual and driving behaviors can be improved by linear visual guiding facilities. • Linear visual guiding facilities can enhance traffic safety. • Linear visual guiding facilities can be used in conjunction with tunnel lighting. This study endeavors to assess the efficacy of retroreflective linear visual guiding facilities in enhancing traffic safety within freeway tunnels. While simulation tests have traditionally served as the primary means for investigating traffic engineering solutions aimed at improving driving behaviors within tunnels, empirical studies validating these methods in real tunnel environments have been notably lacking. To bridge this research gap, our study developed two cost-effective linear visual guiding facilities equipped with retroreflective features and evaluated their effectiveness in authentic freeway tunnel settings. We employed various indicators, including visual behaviors such as the speed of pupil area change and the distribution time of gaze points, as well as driving behaviors encompassing speed and lateral deviation, to assess the impact of these facilities on drivers. Our findings unequivocally demonstrate that the integration of linear visual guiding facilities significantly enhances driving safety within tunnels compared to conventional facilities. Specifically, the speed of pupil area change decreases, while the distribution time of gaze points increases following the implementation of linear visual guiding facilities. Moreover, we observe mitigations in the speed differentials among vehicles, both within and between tunnel zones, alongside reductions in vehicular lateral deviations. The synergistic deployment of linear visual guiding facilities in conjunction with conventional delineators and raised pavement markers can expedite visual perception, promote accurate driving decisions, and facilitate the safe passage of vehicles through tunnels. In conclusion, our study has proved that retroreflective linear visual guiding facilities effectively can enhance driving safety in freeway tunnels. [ABSTRACT FROM AUTHOR]

There have been significant research and developments in recent years for intersection-safety solutions that are intended to alert drivers of hazardous situations by utilizing sensing, computing, and communication technologies. Since the effectiveness of intersection-safety systems depends strongly on driver perception and acceptance of the provided warning signal, the understanding of driver actions under the targeted scenario is a central research topic. One significant safety concern at intersections is the left-turn crossing-path scenarios, where a left-turning vehicle is confronted by oncoming traffic. This paper describes the analysis and synthesis of real-world data for such scenarios observed in field observations. Specifically, traffic interactions in left-turn across-path situations are evaluated to compare data from various intersections with different operation and traffic attributes. The analyzed data were characterized to gain insight into a time gap acceptance exhibited by a population of drivers. The knowledge of driving behaviors can provide the guidelines for future investigation as well as a knowledge basis for the selection of warning criteria to allow timely alerts to drivers in the intended safety applications.

The quantification and estimation of the driving style are crucial to improve the safety on the road and the acceptance of drivers with level2–level3(L2–L3) intelligent vehicles. Previous studies have focused on identifying the difference in driving style between categories, without further consideration of the driving behavior frequency, duration proportion properties, and the transition properties between driving style and behaviors. In this paper, a novel methodology to characterize the driving style is proposed by using the State–Action semantic plane based on the Bayesian nonparametric approach, i.e., hierarchical Dirichlet process–hidden semi–Markov model (HDP–HSMM). This method segments the time series driving data into fragment clusters with similar characteristics and construct the State–Action semantic plane based on the statistical characteristics of the state and action layer to label and interpret the fragment clusters. This intuitively and simply visualizes the driving performance of individual drivers, while the risk index of the individual drivers can also be obtained through semantic plane. In addition, according to the joint mutual information maximization (JIMI) approach, seven transition probabilities of driving behaviors are extracted from the semantic plane and applied to identify driving styles of drivers. We found that the aggressive drivers prefer high–risk driving behaviors, and the total duration and frequency of high–risk behaviors are greater than those of cautious and normal drivers. The transition probabilities among high–risk driving behaviors are also greater compared with low–risk behaviors. Moreover, the transition probabilities can provide rich information about driving styles and can improve the classification accuracy of driving styles effectively. Our study has practical significance for the regulation of driving behavior and improvement of road safety and the development of advanced driver assistance systems (ADAS).

Road traffic signs are part of the road infrastructure components meant to ensure the safe use of roads by motorists and pedestrians alike. A good knowledge and comprehension of road signs helps ensure smooth flow of traffic, improves safety for other motorists and provides drivers with good reaction time to comply with the message dictated by the signs. Abu Dhabi, being the capital of the UAE, brings together people from all over the world for work, business or tourism. Abu Dhabi has quality roads and traffic signage together with an efficient police force that enforces adherence to traffic rules. Despite all these and the reduced fatalities in absolute terms, traffic violations have been increasing exponentially, resulting in five million traffic fines in 2019 for a population of about 2.9 million inhabitants. This study sought to assess motorists’ comprehension of the various traffic signs used across the UAE. The results of the survey (N = 200) revealed that the drivers were able to correctly identify 77% of the road signs in the survey. It was also found that the respondents failed to correctly identify traffic signs within the specific category referred to as “advance warning signs”. Respondents’ education, nationality, gender, and marital status did not have any statistically significant effects on the results compared to earlier studies. It is recommended that more attention be focused on driver education and training.

In this study, we explore the problem of traffic congestion, its effects and related issues for Qasimabad, Hyderabad, Pakistan. The study is based on survey conducted at different sites of Qasimabad. The data was collected using a Questionnaire comprising of questions associated with traffic congestion and its causes. The data was analyzed with SPSS software. A main objective of this work is to present extent to which different driving behaviors are associated with each other. This is achieved using correlation analysis. Finally, recommendations to avoid transportation issues and traffic congestion in Qasimabad, Hyderabad based on present analysis are provided

Introduction: Driving an automobile is an important activity for the social participation of individuals with traumatic brain injury (TBI). Return to safe driving is usually addressed during rehabilitation, but we know little about driving behaviors in the years following TBI rehabilitation.Objective: To explore self-reported and objective (official driving records) post-rehabilitation driving behaviors and offenses in individuals with TBI: (a) having passed a driving evaluation, (b) who did not undergo a driving evaluation, and (c) non-injured controls.Methods: Cross-sectional design with 162 adults: (a) 48 participants with mild, moderate, or severe TBI whose drivers' license was suspended and reinstated following a driving evaluation during rehabilitation (TBI-DE; M = 42.2 years of age, SD = 11.5); (b) 24 participants with TBI who maintained their driving privileges without undergoing a driving evaluation (TBI-NE; M = 36.5 years of age, SD = 9.9); (c) 90 non-injured controls (M = 43.8 years of age, SD = 11.4). Participants with TBI were recruited from seven rehabilitation centers, 2–3 years after the end of rehabilitation in the province of Quebec, Canada. During a telephone interview, data were obtained regarding self-reported driving: (a) habits; (b) self-efficacy; (c) anger expression; (d) sensation-seeking; (e) violations/errors; (f) accidents, driving offenses, and demerit points for the two-year interval predating the study. Objective data for driving offenses, accidents, and demerit points were obtained from the automobile regulatory body for the same period and for the two-year interval before the injury for the TBI groups.Results: Compared to non-injured controls, the TBI-DE group reported significantly lower scores for self-reported verbal aggressive expression of anger and driving violations/errors. Conversely, their official driving records showed significantly more demerit points for the last 2 years, and a significantly higher frequency of serious post-rehabilitation accidents (10), compared to the TBI-NE group (one) and the control group (none). Compared to pre-injury levels, individuals with TBI had significantly more demerit points post-rehabilitation.Conclusions: Individuals with TBI may underestimate risky driving behaviors even if they have been deemed fit to drive. Reduced self-awareness, memory, and dysexecutive problems following TBI could influence self-report of driving behaviors and explain discrepancies between self-reported and objective driving-related behaviors. Recommendations for research and practice are provided.

Introduction : In Brazil, driver aggressiveness in road traffic is a critical issue and could be an important contributing factor to the high number of traffic accidents. Because no instruments are available in Portuguese to register driving aggressiveness or driving anger in Brazil, we adapted English instruments into the Brazilian context. The aims of this study were to provide a Brazilian adaptation of the Driving Anger Expression Inventory (DAX) and to try to validate it by testing its psychometric properties and investigating its relationships with risky driving behaviors (DBQ), road accidents, driving sensation seeking, and hostility. Method : The Brazilian adaptations of the DAX, DBQ, the Driving Sensation Seeking Scale (DSSS) and the hostility Scale were administered to a sample of 512 undergraduate students (with a mean age of 23.7 years, 52.1% men). Results : Confirmatory factor analysis of the Brazilian DAX (DAX-BR) items yielded a four-factor solution with 43 items, which obtained the best goodness-of-fit to the data. Cronbach's alpha for the DAX-BR factors ranged from 0.69 to 0.88. Other results on validity were a positive correlation (range 0.39–0.59) between the factors of the DAX-BR, DSSS, and DBQ. Conclusion : DAX-BR as the same structure as the original and is a reliable instrument for use with young drivers. Other studies should be conducted to further validate the DAX-BR in different types of populations such as older and more experienced drivers, professional drivers, and traffic regulation offenders whose driver's license has been taken away. Practical applications: This Brazilian version can be recommended for the assessment of driving anger expression in Brazil among young drivers in view of helping them driver more safely, and in particular to reduce traffic violations. • The study provides evidence of the validity of the Brazilian adaptation of the Driving Anger Expression Inventory - (DAX-BR). • The structure of the factors for the DAX-BR was similar to the original one. • DAX-BR has good psychometric properties. • Results showed that the DAX-BR is a good predictor of the DBQ scales. • DAX-BR can be recommended for the assessment of driving anger expression in Brazil among young drivers. [ABSTRACT FROM AUTHOR]

Due to the prevalence of IoV technology, big data has increasingly been promoted as a revolutionary development in a variety of applications. Indeed, the received big data from IoV is valuable particularly for those involved in analyzing driver's behaviors. For instance, in the fleet management domain, fleet administrators are interested in fine-grained information about fleet usage, which is influenced by different driver usage patterns. In the vehicle insurance market, usage-based insurance or pay-as-you-drive schemes aim to adapt the insurance premium to individual driver behavior or even to provide various value-added services to policy holders. These applications can be expected to improve and to make safer the driving style of various individuals. Nowadays, big data analysis is becoming indispensable for automatic discovering of intelligence that is involved in the frequently occurring patterns and hidden rules. It is essential and necessary to study how to utilize these large-scale data. Regarding driving behaviors analysis, this paper presents a preliminary study based on feature selection and statistical approach. Feature selection is one of the important and frequently used techniques in data preprocessing for big data mining. Feature selection, as a dimensionality reduction technique, focuses on choosing a small subset of the significant features from the original data by removing irrelevant or redundant features. According to selection process, the most significant feature is vehicle speed for the collected vehicular data. Afterward, the statistical approach calculates skewness and dispersion in speed distribution as the statistical features for driving behaviors analysis. Finally, the established classification rules not only provide data-driven services and big data analytics but also offer training data samples for supervised machine learning algorithms. To validate the feasibility of the proposed method, over 150 drivers and more than 200,000 trips are verified in the simulation. As expected, experimental results are well matched with our observations. [ABSTRACT FROM AUTHOR]

• Driving profiles at baseline did not predict adherence to treatment. • Sleepiness and AHI are associated with official driving record violations. • Fatigue is associated with self-reported driving violations. • Fatigue and sleepiness should be assessed and analyzed separately in studies of driving risk. The present investigation examines the role of daytime sleepiness and fatigue and how these relate to driving behaviors and risk assessment in people newly diagnosed with obstructive sleep apnea (OSA). We recruited 47 individuals, (24 female, 23 male), between the ages of 25 and 71 (mean age = 51, SD = 11.28). Of those, 24 individuals were newly diagnosed with OSA and 23 individuals were in a comparison sample with similar proportions of biological sex and ages, who tested negative for OSA. All participants completed questionnaire measures related to sleep, psychological adjustment, driving behavior, sleepiness and fatigue, immediately after their follow-up appointment. We collected data on driving violations from registered driving records for the 5 years preceding their enrolment in the study, as well as sleep-related data for all participants. Results show that individuals with OSA (M = 1.08, SD = 1.38) do not commit more driving violations than control participants (M = 0.64, SD = 1.26). Although drivers with OSA indicate significantly worse scores for fatigue (M = 7.73, SD = 3.71) compared with controls (M = 4.26, SD = 3.66), there was no significant difference for sleepiness between drivers with OSA (M = 10, SD = 3.57) and Controls (M = 8, SD = 3.69). An association between driving violations and sleepiness was found for drivers with OSA – r (24) = −0.45, p <.05 – but not for Controls – r (23) = −0.22, p >.05. Fatigue, and sleepiness should be assessed as distinct constructs, and each should be taken into account separately in studies of driving risk. [ABSTRACT FROM AUTHOR]

Although Connected Vehicle technology is developing rapidly, connected vehicles (CV) are going to mix with the traditional vehicles (i.e., non-connected vehicles) for a long time. The effects of deploying CV on urban traffic systems are actually not clear. The main objective of this study is to evaluate the potential effects of route guidance under connected vehicle environment on an urban traffic network in terms of traffic mobility and safety. Microscopic simulation approach is used to conduct CV environment simulation and the rolling horizon approach is used for information updating among the connected vehicles. Meanwhile, driving behavior is modeled through aggressiveness and awareness of drivers. Traffic mobility for the road network was measured by average trip time and average vehicle trip speed. A surrogate measure, i.e., the time-to-collision involved incident rate for one kilometer driven, was used to assess the safety of the road network. Based on a real urban traffic network, the impacts of market penetration levels of connected vehicles and information updating intervals were studied. Simulation results showed that market penetration level of connected vehicles has little impact on the mobility and safety of road network. In addition, according to the simulation conducted in this paper, shorter updating interval is shown to be likely to lead to better mobility, while the safety of road network is likely to decline, under the assumptions embraced in the simulation. By contrast, the simulation also showed that longer updating interval is likely to lead to better safety and decreased mobility. [ABSTRACT FROM AUTHOR]

Introduction: Driving an automobile is an important activity for the social participation of individuals with traumatic brain injury (TBI). Return to safe driving is usually addressed during rehabilitation, but we know little about driving behaviors in the years following TBI rehabilitation. Objective: To explore self-reported and objective (official driving records) post-rehabilitation driving behaviors and offenses in individuals with TBI: (a) having passed a driving evaluation, (b) who did not undergo a driving evaluation, and (c) non-injured controls. Methods: Cross-sectional design with 162 adults: (a) 48 participants with mild, moderate, or severe TBI whose drivers' license was suspended and reinstated following a driving evaluation during rehabilitation (TBI-DE; M = 42.2 years of age, SD = 11.5); (b) 24 participants with TBI who maintained their driving privileges without undergoing a driving evaluation (TBI-NE; M = 36.5 years of age, SD = 9.9); (c) 90 non-injured controls (M = 43.8 years of age, SD = 11.4). Participants with TBI were recruited from seven rehabilitation centers, 2–3 years after the end of rehabilitation in the province of Quebec, Canada. During a telephone interview, data were obtained regarding self-reported driving: (a) habits; (b) self-efficacy; (c) anger expression; (d) sensation-seeking; (e) violations/errors; (f) accidents, driving offenses, and demerit points for the two-year interval predating the study. Objective data for driving offenses, accidents, and demerit points were obtained from the automobile regulatory body for the same period and for the two-year interval before the injury for the TBI groups. Results: Compared to non-injured controls, the TBI-DE group reported significantly lower scores for self-reported verbal aggressive expression of anger and driving violations/errors. Conversely, their official driving records showed significantly more demerit points for the last 2 years, and a significantly higher frequency of serious post-rehabilitation accidents (10), compared to the TBI-NE group (one) and the control group (none). Compared to pre-injury levels, individuals with TBI had significantly more demerit points post-rehabilitation. Conclusions: Individuals with TBI may underestimate risky driving behaviors even if they have been deemed fit to drive. Reduced self-awareness, memory, and dysexecutive problems following TBI could influence self-report of driving behaviors and explain discrepancies between self-reported and objective driving-related behaviors. Recommendations for research and practice are provided. [ABSTRACT FROM AUTHOR]

Highlights • Association between sensation seeking (SS) and driving outcomes was systematically reviewed and meta-analyzed. • SS had positive correlations with risky driving, aggressive driving and errors. • SS was also positively correlated with accident involvement and tickets received. • The correlations could be moderated by a number of study and sample characteristics. Abstract The purpose of this study was to evaluate the association between sensation seeking (SS) and driving outcomes (including four aberrant driving behaviors, accident involvement and tickets received) through a systematic review and meta-analysis. Forty-four eligible studies, representing 48 individual trials, were identified from a systematic literature search of four electronic databases, and included in the meta-analysis. Overall, the meta-analysis results showed that SS yielded significant positive correlations with risky driving (pooled r = 0.24, p < 0.001), aggressive driving (pooled r = 0.23, p = 0.019), and errors (pooled r = 0.22, p = 0.016). SS was also positively correlated with accident involvement (pooled r = 0.08, p < 0.001) and tickets received (pooled r = 0.19, p < 0.001), though at weaker levels. The correlations could also be moderated by a number of study and sample characteristics, such as country of origin, publication year, age, gender, driving experience and type of SS measure. The findings help facilitate our understanding of the role of SS in aberrant driving behaviors and accident risk, and provide new insight into the design of evidence-based driving education and accident prevention interventions. [ABSTRACT FROM AUTHOR]

Objective: Emergency service vehicle incidents are a leading cause of firefighter fatalities and are also hazardous to civilian road users. Modifiable driving behaviors may be associated with emergency service vehicle incidents. The goal of this study was to use telematics to identify driving behaviors associated with crashes in the fire service.Methods: Forty-three emergency service vehicles in 2 fire departments were equipped with telematics devices (12 in Department A and 31 in Department B). The devices collected vehicle coordinates, speed, and g forces, which were monitored for exceptions to driving rules established by the fire departments regarding speeding, harsh braking, and hard cornering. Fire department administrative reports were used to identify vehicles involved in crashes and merged with daily telematics data. Penalized logistic regression was used to identify driving rules associated with crashes. Least absolute shrinkage and selection operator (LASSO) regression was used to generate a telematics-based risk index for emergency service vehicle incidents.Results: Nearly 1.1 million km of driving data and 44 crashes were recorded among the 2 departments during the study. Harsh braking was associated with increased odds of crash in Department A (odds ratio [OR] = 2.22; 95% confidence interval [CI], 1.09-4.51) and Department B (OR = 1.55; 95% CI, 1.12-2.15). For every kilometer of nonemergency speeding, the odds of crash increased by 35% in Department A (OR = 1.35; 95% CI, 1.03-1.77) and by over 2-fold in Department B (OR = 2.09; 95% CI, 1.19-3.66). In Department B, hard cornering (OR = 1.14; 95% CI, 1.03-1.26) and emergency speeding (OR = 1.65; 95% CI, 1.06-2.57) were also associated with increased odds of crash. The final LASSO risk index model had a sensitivity of 73% and specificity of 57%.Conclusions: Harsh braking and excessive speeding were driving behaviors most associated with crash in the fire service. Telematics may be a useful tool for monitoring driver safety in the fire service. [ABSTRACT FROM AUTHOR]

Driving accidents are a leading cause of death for late adolescents and young adults, particularly males. Little research has been conducted to determine whether cultural context moderates specific risk factors of dangerous driving. The present study tested the links between sensation seeking, Big Five personality traits, and four risky driving behaviors (unintentional violations [without knowledge], mistakes [underestimating speed], and slips [misreading signs], and traffic violations) in samples of N = 280 college-age American and Czech late adolescents. A few mean level differences across countries were found. Both extraversion (3 of 4) and sensation seeking (2 of 4) were positive correlates of risky driving behaviors, while agreeableness and openness were negative ones (3 of 4). Importantly, a country main effect was found for slips, mistakes, and unintentional violations, not for traffic violations, perhaps the most salient risky driving behavior; none of the tested moderation effects by culture were statistically significant. N = 149. • Cross-cultural evidence supports salience of sensation seeking and Big Five. • Agreeableness and openness negative correlates of risky driving • Extraversion and sensation seeking positive correlates of risky driving • Culture did not explain unique variance in traffic violations. • None of the links were moderated across four risky driving measures. [ABSTRACT FROM AUTHOR]

Texting while driving is prevalent among college-aged students, despite distracted driving laws. Social Norms Theory suggests that individuals are influenced by perceptions of how their social groups act. Proximal sources of social influence, such as significant others (S.O.) may be more likely to effect college-aged students than distal sources (e.g. people they know in their friend or peer group, but don’t identify as people with whom they have a particularly important or meaningful relationship). We investigated whether perceived S.O. texting behaviors mediated the relationship between perceived risk of texting while driving and reported texting while driving among college-aged students. A sample of 835 undergraduate licensed drivers were surveyed about the influence of perceived risk of texting while driving on texting while driving within the past month. We examined whether seeing one’s S.O. (versus their friends) texting while driving mediated this effect. Two groups of drivers were considered: low-risk drivers, who never received a traffic citation nor got into a crash, and high-risk drivers, who had ever received traffic citation and been involved in a crash. We also examined gender differences within low-risk and high-risk groups. A series of logistic regressions, adjusting for gender and ethnicity, were conducted along with Sobel Tests to evaluate the significance of the mediation effect. Results showed that perceptions of the S.O. partially mediated the relationship between perceived risk of texting while driving and texting while driving. This effect was also observed for low risk (female only) and high risk drivers. There was no mediation effect for distal sources. These findings bolster the limited research on the importance of proximal sources of social influence and suggest prevention efforts should focus on proximal social networks of college-aged students as opposed to distal social influences when targeting young adults distracted driving. [ABSTRACT FROM AUTHOR]

Intelligent vehicles use advanced driver assistance systems (ADASs) to mitigate driving risks. There is increasing demand for an ADAS framework that can increase driving safety by detecting dangerous driving behavior from driver, vehicle, and lane attributes. However, because dangerous driving behavior in real-world driving scenarios can be caused by any or a combination of driver, vehicle, and lane attributes, the detection of dangerous driving behavior using conventional approaches that focus on only one type of attribute may not be sufficient to improve driving safety in realistic situations. To facilitate driving safety improvements, the concept of dangerous driving intensity (DDI) is introduced in this paper, and the objective of dangerous driving behavior detection is converted into DDI estimation based on the three attribute types. To this end, we propose a framework, wherein fuzzy sets are optimized using particle swarm optimization for modeling driver, vehicle, and lane attributes and then used to accurately estimate the DDI. The mean opinion scores of experienced drivers are employed to label DDI for a fair comparison with the results of our framework. The experimental results demonstrate that the driver, vehicle, and lane attributes defined in this paper provide useful cues for DDI analysis; furthermore, the results obtained using the framework are in favorable agreement with those obtained in the perception study. The proposed framework can greatly increase driving safety in intelligent vehicles, where most of the driving risk is within the control of the driver. [ABSTRACT FROM PUBLISHER]