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• A novel algorithm was developed to analyze driving route uniqueness and driver familiarity using GPS data from a smartphone app, encompassing young novice and older drivers. • The algorithm compares trips of identified users, creating same route trip arrays with statistically determined thresholds for GPS proximity and trip overlap. Optimal thresholds were found using a General Linear Model. • The algorithm outputs driving diversity (number of unique routes) and route-based familiarity using the Rescorla-Wagner model. • Gaussian mixture model clustering revealed two groups in young drivers: high and low frequency drivers with opposite patterns of route diversity and familiarity. • In older drivers, a significant negative correlation was found between the number of unique routes and Geriatric Depression Score, and a positive correlation with walking gait speed, suggesting route diversity and familiarity may complement existing measures of driving safety and behavior-related outcomes. Introduction: This study addresses the lack of methods to quantify driver familiarity with roadways, which poses a higher risk of crashes. Method: We present a new approach to assessing driving route diversity and familiarity using data from the DrivingApp , a smartphone-based research tool that collects trip-level information, including driving exposure and global positioning system (GPS) data, from young novice drivers (15–19 years old) to older drivers (67–78 years old). Using these data, we developed a GPS data-based algorithm to analyze the uniqueness of driving routes. The algorithm creates same route trip (SRT) arrays by comparing each trip of an identified user, employing statistically determined thresholds for GPS coordinate proximity and trip overlap. The optimal thresholds were established using a General Linear Model (GLM) to examine distance, and repeated observations. The Adjusted Breadth-First Search method is applied to the SRT arrays to prevent double counting or trip omission. The resulting list is classified as geographically distinct routes, or unique routes (URs). Results: Manual comparison of algorithm output with geographical maps yielded an overall precision of 0.93 and accuracy of 0.91. The algorithm produces two main outputs: a measure of driving diversity (number of URs) and a measure of route-based familiarity derived from the Rescorla–Wagner model. To evaluate the utility of these measures, a Gaussian mixture model clustering algorithm was used on the young novice driver dataset, revealing two distinct groups: the low-frequency driving group with lower route familiarity when having higher route diversity, whereas the high-frequency driving group with the opposite pattern. In the older driver group, there was a significant correlation found between the number of URs and Geriatric Depression Score, or walking gait speed. Practical Applications: These findings suggest that route diversity and familiarity could complement existing measures to understand driving safety and how driving behavior is related to physical and psychological outcomes. [ABSTRACT FROM AUTHOR]

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. A virtual reality experiment was designed using a driving simulator and VR technology, and 64 subjects were recruited to participate and complete the experiment. 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. 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. [ABSTRACT FROM AUTHOR]

To investigate drivers' gaze behavior and the characteristics of their gaze positions while driving, a natural driving behavior test method was employed alongside a non-contact eye-tracking device to conduct an in-vehicle experiment for collecting gaze data. Initially, we utilized the traditional approach to delineate the area of interest, analyzing variations in pupil diameter, gaze positions, and the duration spent in each area throughout the driving task, thereby compiling statistics on drivers' gaze patterns. Subsequently, harnessing the You Only Look Once version 5 architecture, we can precisely identify the position of vehicles and obstacles from the captured images. Enhancements to the network model—including streamlining and integrating an attention mechanism—have significantly refined target detection accuracy. In the final analysis, by correlating drivers' gaze data with the positional information of upcoming obstacles, we can accurately assess where drivers are looking. This fusion of data allows for a more nuanced observation of gaze dispersion and position within a one-second timeframe, providing valuable insights into drivers' attention distribution and driving behaviors. [ABSTRACT FROM AUTHOR]

The study aimed to determine the sociodemographic and psychological profiles of drivers with a history of motor vehicle crashes (MVCs), following the contextual-mediated model of crash involvement, and trying to define similarities and differences with drivers without MVCs. Although road trauma prevention has become a central public health issue, the study of psychological determinants of MVCs does not have consistent results due to methodological and theoretical weaknesses. Three-hundred and forty-five active drivers (20% females) completed an extensive office-based fitness-to-drive evaluation including measures of cognition, personality, self-reported driving-related behaviors, attitudes, as well as computerized measures of driving performance. The Classification and Regression Tree method (CART) was used to identify discriminant predictors. The classification identified several relevant predictors; the personality trait of Discostraint (as a distal context variable; cut-point: 50 T points) and motor speed (as a proximal context variable; cut-point: 64 percentile ranks). The global classification model increased approximately 3 times the probability of identifying people with a history of MVC involvement, starting from an estimated prevalence of being involved in an MVC in a period of five years in the population of active drivers. Consistent with the 'contextual-mediated model of traffic accident involvement', the results of the present study suggest that road trauma analysis should focus on both distal and proximal driver-related factors by paying attention to their association in determining MVCs. These results represent a valuable source of knowledge for researchers and practitioners for preventing road trauma. [ABSTRACT FROM AUTHOR]

Novice drivers often face challenges such as misjudgment, inappropriate steering control, distraction, and insufficient speed control when making left turns at intersections, leading to safety hazards. Installing intersection guide lines offers a solution by providing clear path directions, mitigating safety concerns associated with novice drivers' left-turn actions. This study explored the impact of intersection guide line configurations on the driving behavior of novice drivers during left turns, utilizing large, medium, and small typical intersections to create six categories of left-turn simulation scenarios in a driving simulator. Data on vehicle trajectory, steering angle, steering speed, and eye-tracking were collected and analyzed. The study revealed that guide line arrangement significantly influences novice drivers' left-turning behavior, enhancing path guidance while reducing trajectory and steering angle fluctuations, speed variations, and driver attention dispersion. This improvement in stability is particularly notable as intersection size and the number of left-turn lanes increase. The study's findings offer robust theoretical support and guidance for the development and widespread adoption of intersection guide lines. [ABSTRACT FROM AUTHOR]

• Investigated the combined effect of visibility and accumulated sand on drivers' avoidance performance during sand and dust storms. • Analysed drivers' lateral avoidance process from three stages: perception, decision-making, and maneuver. • Found a significant decrease in lateral avoidance percentage only when visibility dropped below 100 m. • Identified that reduced visibility delays drivers' perception but insignificantly affects maximum lateral acceleration. Sand and dust storms (SDS) are characterized by their instantaneous and abrupt characteristics, and the accompanying, often quick, accumulation of aeolian sand on road surfaces contributes to increased driving risks on expressways. We investigated the combined effect of visibility conditions and accumulated sand on drivers' avoidance performance. We utilized a driving simulator to simulate SDS events and conducted tests across six different visibility conditions using 45 drivers. We analyzed the drivers' control of the vehicle's speed based on longitudinal speed and acceleration in test road segments defined as Clear, Transition, and SDS. We divided the drivers' risk avoidance maneuvers on sand-covered road segments into two strategies, lane-keeping and lateral avoidance, with a focus on the latter. We divided the drivers' lateral avoidance process into three stages: perception, decision-making, and maneuver. Key performance indicators were selected to analyze the drivers' performance in terms of their lateral and longitudinal control of the vehicle. The findings suggest that drivers reduce their speed to compensate for the limited field of view during SDS events. Lower visibility corresponds to a greater distance required for drivers to reach a relatively stable driving state and leads to higher standard deviation values for the speeds. The coupled effect is reflected mainly in the percentage of lateral avoidance maneuvers. When the visibility was 150 m or above, more than 60 % of drivers in this study chose lateral avoidance over lane-keeping, with no significant difference between visibility conditions. Only when the visibility dropped to 100 m or below did the percentage of lateral avoidance instances significantly decrease. In SDS environments, light and visibility conditions are poor and a thin layer of sand is spread across the road surface. Even under relatively good visibility conditions, a thin layer of accumulated sand is difficult to discern until the driver is relatively close to sand-covered road segments. This paper provides an in-depth exploration of driving avoidance behavior under SDS conditions. The findings can contribute to proactive intervention strategies for driving during SDS and offer theoretical insights for developing driving safety assistance systems. [ABSTRACT FROM AUTHOR]

• Eye movement and driving behavior were used to evaluate situation awareness (SA). • The variable message sign gives drivers a higher SA level. • The variable message signs (VMS) have a speed-limiting effect on vehicles. • Experienced drivers have better SA than inexperienced drivers. • Eye movement is a sensitive SA indicator. The driving environment in the freeway exit areas is complex, and the installation of relevant signs can enhance the creation of a safe and smooth exit area environment. This study investigated the situation awareness (SA) scores of two types of drivers using different signs in the freeway exit areas and the correlation between drivers' eye movement, driving behavior, and SA scores. The driving tasks were divided into two situations: continuing on the mainline and entering the exit ramp. The independent variables included scene type (static speed limit signs, static speed limit signs plus variable message signs, variable speed limit signs, variable speed limit signs plus variable message signs), and participant type (experienced and inexperienced participants). The dependent variables were SA, eye movement, and driving behavior. Each participant completed a driving simulation experiment with the two driving tasks, totaling eight scenes. The SA scores were measured using the Situation Awareness Global Assessment Technique (SAGAT). The driving behavior and eye movement were collected during the experimental data analysis segment, and the sensitivity indicators for evaluating drivers' SA were determined by calculating the correlation with the SAGAT scores. This paper focuses on applying variable message signs (VMS) to speed limits in freeway exit areas from the driver's perspective to enhance driving safety. The findings showed that drivers have better SA in scenes with VMS, and experienced drivers have better SA than inexperienced drivers in the same scene. The VMS affects speed control, and eye movement is a sensitive SA indicator. [ABSTRACT FROM AUTHOR]

The delivery market in Republic of Korea has experienced significant growth, leading to a surge in motorcycle-related accidents. However, there is a lack of comprehensive data collection systems for motorcycle safety management. This study focused on designing and implementing a foundational data collection system to monitor and evaluate motorcycle driving behavior. To achieve this, eleven risky behaviors were defined, identified using image-based, GIS-based, and inertial-sensor-based methods. A motorcycle-mounted sensing device was installed to assess driving, with drivers reviewing their patterns through an app and all data monitored via a web interface. The system was applied and tested using a testbed. This study is significant as it successfully conducted foundational data collection for motorcycle safety management and designed and implemented a system for monitoring and evaluation. [ABSTRACT FROM AUTHOR]

This study aims to investigate left- and right-side merging sections on urban underground roads based on virtual reality driving simulator experiments. The behaviors investigated were changed by acceleration lane in the merging section, including 100, 120, and 140 m, considering current design guidelines. Typically, lane changing behavior was studied based on experiments using speed and lateral placement on driving. The behavior of more speed reduction in merging sections occurred in left-side merging than in right-side merging sections. In the left-side merging sections, speed reduction and acceleration rate decreased with the length of the acceleration lane. In the cases with relatively long acceleration lanes, lane changing locations for left-side merging sections were more sensitive than those of right-side merging sections. Some results from the driving simulator experiments show that road geometric design based on left-side merging sections might have more risk situations due to driver expectation and behaviors. This article provides technical knowledge to be applied to the acceleration lanes of left-side merging sections that extend 1.4 times longer than the usual road designs. [ABSTRACT FROM AUTHOR]

Human behavior significantly contributes to severe road injuries, underscoring a critical road safety challenge. This study addresses the complex task of predicting dangerous driving behaviors through a comprehensive analysis of over 356,000 trips, enhancing existing knowledge in the field and promoting sustainability and road safety. The research uses advanced machine learning algorithms (e.g., Random Forest, Gradient Boosting, Extreme Gradient Boosting, Multilayer Perceptron, and K-Nearest Neighbors) to categorize driving behaviors into 'Dangerous' and 'Non-Dangerous'. Feature selection techniques are applied to enhance the understanding of influential driving behaviors, while k-means clustering establishes reliable safety thresholds. Findings indicate that Gradient Boosting and Multilayer Perceptron excel, achieving recall rates of approximately 67% to 68% for both harsh acceleration and braking events. This study identifies critical thresholds for harsh events: (a) 48.82 harsh accelerations and (b) 45.40 harsh brakings per 100 km, providing new benchmarks for assessing driving risks. The application of machine learning algorithms, feature selection, and k-means clustering offers a promising approach for improving road safety and reducing socio-economic costs through sustainable practices. By adopting these techniques and the identified thresholds for harsh events, authorities and organizations can develop effective strategies to detect and mitigate dangerous driving behaviors. [ABSTRACT FROM AUTHOR]

In this study, the effect of five geometric design features, including radius, superelevation, longitudinal grade, lane and shoulder width, on the average speed in the horizontal curve on a two-lane undivided rural road was investigated. The standardized regression coefficients showed that the most important factor affecting the speed was the radius (10.47) followed by the longitudinal grade (4.46). Superelevation and lane width had little effect. Shoulder width had no significant effect. This would be due to the wide width of the lanes. It was found that the relationships between speed and radius, longitudinal grade, superelevation and lane width were radical, quadratic, linear and linear, respectively. Increasing the longitudinal grade has increased the speed of the drivers. Increasing the superelevation was effective when its value changed from negative to positive. [ABSTRACT FROM AUTHOR]

The i-DREAMS project focuses on establishing a framework known as the 'Safety Tolerance Zone (STZ)' to ensure drivers operate within safe boundaries. This study compares Long-Short-Term-Memory Networks and shallow Neural Networks to assess participants' safety levels during i-DREAMS on-road trials. Thirty German drivers' trips and Forty-Three Belgian drivers were analyzed using these methods, revealing factors contributing to risky behavior. Results indicate i-DREAMS interventions significantly enhance driving behavior, with Neural Networks displaying superior performance among the algorithms considered. [ABSTRACT FROM AUTHOR]

Classification of driving events is a crucial stage in driving behavior monitoring using smartphone sensory data. It has not been previously explored that to what extent classification performance depends on the classifier type and input data characteristics. To fill this gap, a real-world experiment is designed for supervised data collection. Then the effects of different machine learning (ML) classifiers, data sampling rates, and sensor combinations on the final classification accuracy are demonstrated. A considerable number of labeled events (4114) containing 11 types of driving maneuvers are collected using base sensors (accelerometer and gyroscope) and composite sensors (linear accelerometer and rotation vector) available in smartphones. Several models using 23 ML algorithms are trained. The sensitivity of these models is analyzed by changing the characteristics of the input data concerning the type of ML classifier, data sampling rate, and the bundle of mobile sensors. It is demonstrated that: (1) F1 scores vary from 70 to 96% for different ML classifiers, (2) F1 scores drop 30–40% depending on the classifier type when reducing the data sampling rate, and (3) using all four sensors as a bundle for classifying driving events is not reasonable since an approximate equal F1 score is achievable by a three-sensor bundle which includes an accelerometer and a linear accelerometer. [ABSTRACT FROM AUTHOR]

Motorcycles are a highly unstable element that impacts road traffic safety due to insufficient safety protection structures and poor maneuverability. The rapid increase in their use has exacerbated the severity of crash-related fatalities and injuries. While some countries have implemented motorcycle restrictions to address this phenomenon, the causal effect of such policies on reducing the severity of injuries from accidents requires further research. This study employs a difference-in-differences approach to estimate the causal effect of motorcycle restrictions in China on the severity of road crash injuries. The results indicate a significant reduction in the likelihood of fatal or severe injuries occurring in crashes. Specifically, in regions with motorcycle restrictions, the probability of crashes resulting in fatal or severe injuries decreased by 2.9% on average. The results remain robust following placebo and falsification tests and excluding confounding effects. Heterogeneity analysis reveals that motorcycle restrictions primarily affect male drivers and drivers with urban hukou, with no significant differences in effects across drivers of different age groups. Overall, the findings indicate that motorcycle restrictions reduce the number of motorcycles on the road and decrease the probability of motorcycle drivers being at fault in crashes, signifying improved driver behavior and effectively lowering crash severity. • Difference-in-differences approach is employed to estimate the road safety effect of motorcycle restrictions. • The probability of crashes resulting in fatal or severe injuries decreased in regions with motorcycle restrictions. • Motorcycle restrictions primarily affect male drivers and drivers with urban hukou. • Motorcycle restrictions reduce the number of motorcycles on the road and improve driver behavior. • Motorcycle restrictions may potentially threaten the travel safety of vulnerable groups. [ABSTRACT FROM AUTHOR]

• The effects of interchange spacing and driving zone on driver visual behavior were assessed. • The insufficient intersection spacing resulted in an increase in mean fixation duration and concentration of horizontal fixation points. • Drivers had lower horizontal sweep amplitude and speed on SSI than on NSI and exhibited higher visual loads. • Pupil change was found to be most relevant to driver visual load. • The visual load on drivers is highest in the ramp zone and the difference in visual load caused by the spacing is most significant in the diversion zone. The aim of this study was to evaluate the effects of interchange spacing on the visual characteristics and visual load of drivers in the exit zone of the urban expressway. A naturalistic driving experiment was conducted in a high-density interchange group on the North Ring Expressway in Chongqing, China. Eye-movement data from 47 participants were collected to assess the pattern of visual performance, seven indicators were selected to characterize drivers' visual characteristics, and the entropy weighting method was used to assess the drivers' visual loads under different interchange spacing and driving zones. The results show that insufficient spacing of the interchange affects drivers' visual behavior and increases their visual load level. The fixation duration for drivers is significantly affected by interchange spacing, with longer fixation duration in the diversion and ramp zones of small-spacing interchanging(SSI). The mean horizontal visual angle of drivers was mainly distributed in the middle of the field of view, and there was a tendency for the overall left side to be shifted under the conditions of SSI, and the mean vertical visual angle was mainly distributed in the nearer part in front of the lanes. In the ramp zone of SSI, the driver's attention to the vehicle dashboard had a significant increase. In normal-spacing interchange(NSI), drivers' horizontal saccade amplitude and speed were higher than those in SSI, and the difference was most significant in the diversion zone. Vertical saccade amplitude and speed of the driver were found to be significantly affected by interchange spacing only in the ramp zone. Drivers' visual load had the highest correlation with pupil variation, and the visual load scores of drivers at SSI were 39.65% and 17.7% higher than at NSI in the diversion and ramp zones, respectively. This study provides valuable insights into the effects of the high-density interchange group on the visual performance of drivers on urban expressways, and the results provide a theoretical basis for the spacing control of interchanges. [ABSTRACT FROM AUTHOR]

Lane-change maneuvers are a critical aspect of autonomous vehicles operation, but executing them efficiently and safely in the presence of other vehicles with varying driving behaviors, influenced by drivers' emotions, poses a significant challenge. This paper presents a novel decision-making framework with trajectory generation and control algorithm, which considers the emotion-induced driving behavior of other vehicles' drivers to perform safe and efficient lane-change maneuvers. The algorithm generates smooth trajectory candidates based on the position and velocity of other vehicles, selecting the most efficient and safest option. The control system tracks the generated lane-change trajectory, allowing the autonomous vehicle to pass the other vehicle if the driver is in a "happy," "calm," or "neutral" emotional state, exhibiting cautious behavior such as maintaining or reducing speed. Conversely, if the other vehicle's driver is in an "angry" or "unpleasant" emotional state, causing aggressive behavior like accelerating and not allowing the autonomous vehicle to pass, the control system ensures the autonomous vehicle stays on its previous lane. Simulation and experimental results demonstrate that the proposed algorithm enables autonomous vehicles to perform lane-change maneuvers safely and efficiently in the presence of the other vehicle's driver's emotions, mitigating collisions. This proposed algorithm represents a significant step toward enabling autonomous vehicles to navigate complex traffic scenarios involving other vehicles with varying driving emotions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of the Society of Plant Engineers Japan is the property of Society of Plant Engineers Japan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Traffic Simulating and Evaluating Traffic Patterns at heterogeneous traffic Situations in the Indian Context is extremely increasing. Many researchers from all over the world are trying to explore driving behavior and simulate traffic flow conditions under heterogeneous traffic environments. Simulating Traffic at a Microscopic Level with VISSIM is used in this study identifying every element that contributes to traffic at various traffic stages. Further, the VISSIM model was calibrated based on the desired safety distance and car-following theory. Traffic metrics including flow volume, speed, acceleration, and deceleration were all input parameters in the VISSIM model for simulation purposes. Furthermore, the analysis part of different cases of driving behavior models, such as the default case, and calibration values based on measurement were compared to find out if there was a significant improvement. Moreover, the linear regression model was proposed to understand the calibrated model versus the default case parameters, and it was identified that it is significant when plotting the data. The proposed study results highlighted driving behavior under heterogeneous traffic conditions using VISSIM at Toll Plaza.

Abstract The i-DREAMS project focuses on establishing a framework known as the ‘Safety Tolerance Zone (STZ)’ to ensure drivers operate within safe boundaries. This study compares Long-Short-Term-Memory Networks and shallow Neural Networks to assess participants’ safety levels during i-DREAMS on-road trials. Thirty German drivers’ trips and Forty-Three Belgian drivers were analyzed using these methods, revealing factors contributing to risky behavior. Results indicate i-DREAMS interventions significantly enhance driving behavior, with Neural Networks displaying superior performance among the algorithms considered.

To analyze the car-following behavior accurately, this paper takes the drivers' psychological factors into the consideration based on the psychological field theory. The vehicle dynamics indexes are extracted through vehicle history trajectories and the driving styles are clustered by k-means methods. After that the perception coefficient, reaction coefficient, and driving style correction coefficient are obtained and integrated into the psychological field for characterizing the drivers' driving styles. The psychological car-following model which considers the driving styles is built based on the Full Velocity Difference (FVD) model. Finally, the model is validated under the MATLAB/Simulink environment and the result reveals that the psychological field car-following model achieves higher accuracy of characterizing car-following behavior compared with the FVD model and the interaction potential model. [ABSTRACT FROM AUTHOR]

The aim of this paper is to understand the factors that influence unsafe driving practices by examining published studies that utilized the theory of planned behavior (TPB) to predict driving behavior. To this end, 42 studies published up to the end of 2021 are reviewed to evaluate the predictive utility of TPB by employing a meta-analysis and structural equation model. The results indicate that these studies sought to predict 20 distinct driving behaviors (e.g., drink-driving, use of cellphone while driving, aggressive driving) using the original TPB constructs and 43 additional variables. The TPB model with the three original constructs is found to account for 32% intentional variance and 34% behavioral variance. Among the 43 variables researchers have examined in TPB studies related to driving behavior, this study identified the six that are commonly used to enhance the TPB model’s predictive power. These variables are past behavior, self-identity, descriptive norm, anticipated regret, risk perception, and moral norm. When past behavior is added to the original TPB model, it increases the explained variance in intention to 52%. When all six factors are added to the original TPB model, the best model has only four variables (perceived risk, self-identity, descriptive norm, and moral norm); and increases the explained variance to 48%. The influence of the TPB constructs on intention is modified by behavior category and traffic category. The findings of this paper validate the application of TPB to predicting driving behavior. It is the first study to do this through the use of meta-analysis and structural equation modeling.

• Distractions caused by fellow drivers (DFDs) have received little attention. • Associations between DFDs and other latent factors were evaluated using SEM. • DFDs are positively associated with stress among recipient drivers. • Stress is positively associated with anxiety-based performance deficits. • DFDs and stress are positively associated with crash propensity. 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. [ABSTRACT FROM AUTHOR]

Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems. [ABSTRACT FROM AUTHOR]

This study aims to investigate the structural relationships among adult Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms, Sluggish Cognitive Tempo (SCT), and driving behavior with the mediation of procrastination. This study employed a descriptive-correlational design and recruited a convenience sample of 250 licensed drivers in Tabriz, Iran. The data were collected using four instruments, namely, Adult ADHD Self-Report Scale (ASRS), Adult Concentration Inventory (ACI), Tuckman Procrastination Scale (TPS), and Manchester Driving Behavior Questionnaire (MBDQ). Correlation and structural equation modeling techniques which were performed by SPSS 20 and AMOS 24 software, were used to analyze the data. The study conducted a statistical analysis that revealed several significant relationships. Firstly, there was a significant association between ADHD symptoms ( β = 0.25 , p < 0.05 ) and SCT symptoms (β = 0.18 , p < 0.05) with driving behavior. Secondly, the study found a significant correlation between ADHD (β = 0.52 , p < 0.05) and SCT symptoms (β = 0.33 , p < 0.05) with procrastination. Procrastination was also significantly related to driving behavior (β = 0.37 , p < 0.05) . Moreover, the study identified that ADHD (β = 0.19 , p < 0.05) and SCT symptoms (β = 0.12 , p < 0.05) had a significant effect on driving behavior, with procrastination serving as a mediator. Based on the results, this study has identified additional SCT dimensions associated with adult hyperactivity and procrastination, which contribute to the current understanding of the topic. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

The framing effect is a phenomenon in which a change in the way the conditions are presented can significantly change the decision-making process, even if the presented conditions are objectively equivalent. It has been reported that negative framing tends to lead to stronger motivation. The purpose of this study is to examine the alerts that promotes safe driving. We investigated the effects of pre-driving and in-driving alerts on driving behavior. As a result, it was confirmed that alerts using negative frames, rather than positive frames, before driving and in the pause-situation could promote more cautious driving behavior. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Driver behavior is one of the principal factors associated with road accidents. Much research to date focusing on Machine learning technology has been successfully applied to identifying driving styles and recognizing unsafe behaviors. In this paper, the development of an android mobile application (Driver Alert) is described, with the aim of collecting data from mobile phone sensors data to identify certain patterns and understand drivers' behaviors. Additional information was recorded regarding weather and traffic information, using public API's to complement the data directly collected from the vehicle. Four machine learning models (K-Means, Algorithm Agglomerative Hierarchical, Random Forest and Support Vector Machines) were tested and compared to identify different driver profiles. A native mobile application named DriverAlert was developed to support collect data and make it available, through an online dashboard, to drivers and researchers. Due to the available tools and libraries, it possesses, Python language was used, as it is a powerful programming language for workloads in data science, machine learning, and scientific computing. [ABSTRACT FROM AUTHOR]

The increase in population is accompanied by an increase in the number of vehicles. It is inevitable that the number of vehicle accidents will also increase, which can be caused by various factors. Driver factors reviewed in this study include socioeconomic characteristics, movement characteristics, accident characteristics, and driver behavior characteristics. the purpose of this study is to study the vehicle accident model using interviews and Driving Behavior questionnaires with a total of 307 motorist respondents who have experienced accidents. Driver factors reviewed in this study include socioeconomic characteristics, movement characteristics, accident characteristics, and driver behavior characteristics using interviews and Driving Behavior questionnaires with a total of 307 motorist respondents who have experienced accidents. This investigate used SEM (Structural Equation Modeling) with SmartPLS computer software. Two-wheeled vehicle accident modeling results Y = –0.234X1+0.153X3+ +ei2; R² = 0.102. The greatest influence occurs in the characteristics of driver behavior (X3), namely Ordinary Violation, and for four-wheeled vehicle accident modeling results, Y = –0.343X1+0.284X3+ei2; R² = 0.217. The greatest influence occurs in driver behavior characteristics (X3), namely Ordinary Violation. Ordinary Violation is defined as a deliberate deviation from the rule of law. Thus, from the research results, the most influential variable was the behavior of drivers who committed ordinary violations such as ignoring speed limits, breaking through intersections, and driving under the influence of alcohol. So, there needs to be collaboration between the police and related parties in tackling accidents and reducing the risk of traffic accidents, such as long as socialization or information through newspapers or electronic media to the public in Jayapura City regarding the importance of collective awareness of driving safety [ABSTRACT FROM AUTHOR]

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 pastan 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. [ABSTRACT FROM AUTHOR]

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. 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. 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. The findings provide a theoretical reference for the optimization of colored pavement in foggy conditions. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

Lane width is crucial for traffic safety management. Previous studies have shown that inappropriate width will lead to an increase in accident probability and unsafe driving behaviors. However, there have been limited studies on determination method of special lane width for different models, especially for passenger cars. To tackle this problem, the lateral driving behavior characteristics of the expressway need to be clarified. This work aims to obtain the appropriate lane width according to the vehicles' width and lateral position characteristics. In this study, the lane lateral residual width (the distance between the vehicle body's contour and the lane marking on the same side) and the lateral safety margin are extracted as characterization indices of vehicle lateral characteristics from natural driving trajectory data of expressways. The effect of vehicle type, driving speed, and lane position on the expressway's trajectory behavior is investigated. The results show that the utilization rate of lane width is higher in the outer lane compared to the inner lane. As driving speed increases, vehicles in the inner and middle lanes exhibit shy away effect, moving away from obstacles. Substantial variations exist in the lateral width of lanes among different vehicle types. When driving in the same lane, passenger cars' lane lateral residual width that is 0.4–0.5 m wider than that of heavy vehicles. The recommended lane width for the safe operation of trucks on expressways is 3.75 m. After a comprehensive analysis of vehicle width, trajectory oscillation, and lateral safety margin, this study proposes a minimum lane width of 3.25 m and a recommended general width of 3.5 m for a passenger car‐only expressway. This study holds great significance in enhancing road safety and provides a valuable theoretical foundation for the design of lane width in a passenger car‐only expressway. [ABSTRACT FROM AUTHOR]

In this study, the effect of five geometric design features, including radius, superelevation, longitudinal grade, lane and shoulder width, on the average speed in the horizontal curve on a two-lane undivided rural road was investigated. The standardized regression coefficients showed that the most important factor affecting the speed was the radius (10.47) followed by the longitudinal grade (4.46). Superelevation and lane width had little effect. Shoulder width had no significant effect. This would be due to the wide width of the lanes. It was found that the relationships between speed and radius, longitudinal grade, superelevation and lane width were radical, quadratic, linear and linear, respectively. Increasing the longitudinal grade has increased the speed of the drivers. Increasing the superelevation was effective when its value changed from negative to positive.

This paper investigates the ability of autonomous driving systems to predict outcomes by considering human factors like gender, age, and driving experience, particularly in the context of safety-critical events. The primary objective is to equip autonomous vehicles with the capacity to make plausible deductions, handle conflicting data, and adjust their responses in real-time during safety-critical situations. A foundational dataset, which encompasses various driving scenarios such as lane changes, merging, and navigating complex intersections, is employed to enable vehicles to exhibit appropriate behavior and make sound decisions in critical safety events. The deep learning model incorporates personalized cognitive agents for each driver, considering their distinct preferences, characteristics, and requirements. This personalized approach aims to enhance the safety and efficiency of autonomous driving, contributing to the ongoing development of intelligent transportation systems. The efforts made contribute to advancements in safety, efficiency, and overall performance within autonomous driving systems. To describe the causal relationship between external factors like weather conditions and human factors, and safety-critical driver behaviors, various data mining techniques can be applied. One commonly used method is regression analysis. Additionally, correlation analysis is employed to reveal relationships between different factors, helping to identify the strength and direction of their impact on safety-critical driver behavior.

Abstract In the context of automated driving, the connected and automated vehicles (CAVs) technology unlock the energy saving potential. This paper develops an LSTM‐based deep learning framework for eco‐driving adaptive identification on Intelligent vehicle multivariate time series data. The framework can be adapted for Driver Assistance Systems (DAS) to reduce fuel consumption. Specifically, considering overtaking on rural road is a critical maneuver for operation and has potential to reduce consumption, a simulated driving experiment with 30 participants was conducted to collect the multivariate time series data of the overtaking operation behaviors in conditional automation driving. Driving behaviors were classified into eco‐driving operation behaviors and high fuel consumption operation behaviors based on fuel consumption calculated by using vehicle specific power (VSP). Significance analysis based on linear regression was adopted to identify operation behaviors, and an eco‐driving behavior identification model was established with the use of long short‐term memory (LSTM) for multivariate classification theory. Meanwhile, the other four classification algorithms were used to establish identification models for comparison. The results indicated that the gear position, lane position, the acceleration pedal depth, the clutch pedal depth, and the brake pedal depth had a significant influence on fuel consumption. The eco‐driving behavior identification model of overtaking demonstrated a high classification power and robustness with a classification accuracy of 89.16%. According to the simulation results, the developed adaptive identification model is with promising performance. The conclusions provide theoretical support for developing an adaptive strategy for connected eco‐driving.

A connected vehicle (CV) enables vehicles to communicate not only with other vehicles but also the road infrastructure based on wireless communication technologies. A road system with CVs, which is often referred to as a cooperative intelligent transportation system (C-ITS), provides drivers with road and traffic condition information using an in-vehicle warning system. Road environments with CVs induce drivers to reduce their speed while increasing the spacing or changing lanes to avoid potential risks downstream. Such avoidance maneuvers can be considered to improve driving behavior from a traffic safety point of view. This study seeks to quantitatively evaluate the effect of in-vehicle warning information using per-vehicle data (PVD) collected from freeway C-ITSs. The PVD are reproduced to extract the speed–spacing relationship and are evaluated to determine whether the warning information induces drivers to drive in a conservative way. This study reveals that the in-vehicle warning prompts drivers to increase the spacing while decreasing their speed in the majority of samples. The rate of conservative driving behavior tends to increase during the initial operation period, but no significant changes were observed after this period; that is, the reliability of in-vehicle warning information is not constant in the CV environment. [ABSTRACT FROM AUTHOR]

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), shortterm 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. [ABSTRACT FROM AUTHOR]

This study focuses on the tire–road friction estimation for the predictive control strategy of electronically limited slip differential (ELSD) to improve the handling and acceleration performance of front-wheel drive cars, which typically suffer from excessive understeer and inner drive wheel spin during acceleration while turning due to reduced vertical load on the wheel. To mitigate this, we propose a control logic for ELSD that enhances course followability and acceleration by pre-transferring the driving torque from the inside to the outside wheel, considering the estimated traction potential for rapid response. It is essential to improve the control accuracy of wheel spin prediction by predicting the friction coefficient of the road surface. Furthermore, this study extends to the analysis of vehicle dynamics during lane-change maneuvers on low-friction surfaces, emphasizing the role of accurate tire–road friction estimation in vehicle safety. A CarSim 2023-based simulation study was conducted to investigate the vehicle response on snowy roads with low friction coefficients (μ = 0.2) and low temperatures (−5 °C). The results demonstrated that even minimal steering input could result in significant side-slip angles, highlighting the nonlinear vehicle behavior and the critical need for robust traction estimation in such challenging conditions of general racing tracks. The proposed friction-estimation method was evaluated through vehicle testing and has been substantiated by patents for its originality in control and friction-estimation approaches. The outcomes of these combined methodologies underline the critical importance of tire–road friction coefficient estimation in both the effectiveness of the ELSD system and the broader context of active safety systems. [ABSTRACT FROM AUTHOR]

To improve the simulation accuracy and efficiency of microscopic urban traffic, a unified modeling method considering the behavioral characteristics of vehicle drivers is proposed by considering the lane-changing vehicles on the inlet lanes of signalized intersections and their approach following vehicles on the target lanes as research objects. Based on the driver's multidirectional, multi-vehicle anticipation ability and introducing lateral vehicle influence coefficients, the full velocity difference car-following model was extended to microscopic traffic models that consider the driver's capacity for multi-directional, multi-vehicle anticipation. The extended model can describe longitudinal movements of lane changing and car followers using lateral vehicle influential parameters. The influences of traffic control signals and the type of lane change on drivers' decisions were integrated into the model by reformulating the optimal velocity function of the basic car following the model. Similar modeling methods and components were applied to formulate four groups of experimental models and one group of test models. Vehicle trajectory data and manual observations were collected on urban arteries to calibrate and evaluate the research models, experimental models, and test models. The results show that the car-following behavior is more sensitive to the variation in the status of the lateral moving vehicle and change of lane-changing type compared to lane-changing behavior during the lane-changing process. In addition, when lane changing gradually encroaches on the target lane, the vehicle observes the driving conditions and adjusts its driving behaviors differently. This research helps to analyze travel characteristics and influence mechanisms of vehicles on urban roads, which is a guide for the future development of sustainable transportation and self-driving vehicles and promoting the efficient operation of urban transportation systems. [ABSTRACT FROM AUTHOR]

The issue of driving behavior at night poses significant challenges due to reduced visibility and increased risk of accidents. Recent works have leveraged deep learning techniques to enhance night-time driving safety. However, the limited availability of high-quality training data and the lack of robustness in existing models present significant problems. In this work, we propose a novel approach to improve driving behavior recognition at night using ResNet50 with contrast limited adapted histogram equalization (CLAHE). We collected a new dataset and developed a more effective and robust model that can accurately recognize driving behaviors under lowillumination conditions, thereby reducing the likelihood of collisions and improving overall road safety. The experimental results demonstrate significant improvements in the deep learning model's performance compared to conventional methods. Notably, the ResNet50 model delivers the best performance with accuracy rates of 90.73% using NIGHT-VIS-CLAHE data, demonstrating a 16% improvement in accuracy. For benchmark purposes, the InceptionV3, GoogleNet, and MobileNetV2 models also show enhanced accuracy through CLAHE implementation. Furthermore, NIGHT-VISCLAHE implementation in ResNet50 achieved 90.29% accuracy, surpassing the best NIGHT-IR InceptionV3 at 89.27%, highlighting the advantage of ResNet50 with CLAHE in low-light conditions even against infra-red sensor. [ABSTRACT FROM AUTHOR]

The advent of autonomous vehicles (AVs) in the traffic stream is expected to innovatively prevent crashes resulting from human errors in manually driven vehicles (MVs). However, substantial safety benefits due to AVs are not achievable quickly because the mixed-traffic conditions in which AVs and MVs coexist in the current road infrastructure will continue for a considerably long period of time. The purpose of this study is to develop a methodology to evaluate the driving safety of mixed car-following situations between AVs and MVs on freeways based on a multi-agent driving-simulation (MADS) technique. Evaluation results were used to answer the question 'What road condition would make the mixed car-following situations hazardous?' Three safety indicators, including the acceleration noise, the standard deviation of the lane position, and the headway, were used to characterize the maneuvering behavior of the mixed car-following pairs in terms of driving safety. It was found that the inter-vehicle safety of mixed pairs was poor when they drove on a road section with a horizontal curve length of 1000 m and downhill slope of 1% or 3%. A set of road sections were identified, using the proposed evaluation method, as hazardous conditions for mixed car-following pairs consisting of AVs and MVs. The outcome of this study will be useful for supporting the establishment of safer road environments and developing novel V2X-based trafficsafetyinformation content that enables the enhancement of mixed-traffic safety. [ABSTRACT FROM AUTHOR]