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Road traffic safety is confronted with unique challenges worldwide due to the varied road network, high population density, and the rapid increase in vehicle numbers. This necessitates an effective evaluation of traffic safety at the road segment level to improve traffic safety management. Existing safety indicators, such as crash frequency (which lacks real-time availability) and Surrogate Safety Measures (focused on micro-level interactions), fall short in quantifying the overall safety status of road segments. To overcome these limitations, we introduce the concept of entropy, typically used to measure disorder in complex systems, into road traffic safety assessment. Our research presents a novel macroscopic safety indicator named 'velocity entropy,' along with its calculation method, which involves discretization and the Parzen window technique. The efficacy of velocity entropy is validated using the UTE dataset, a high-fidelity trajectory dataset from China, showing a more significant correlation with traffic conflicts than other macroscopic traffic properties. Notably, velocity entropy can track the temporal and spatial evolution of traffic safety, providing insights into the dynamic development of traffic risks. Overall, velocity entropy offers an innovative, practical, and theoretically meaningful tool for assessing and predicting road safety. [ABSTRACT FROM AUTHOR]

• An improved two-dimensional surrogate safety measure based on fuzzy logic model (FL-iTTC). • FL-iTTC can account for neighboring vehicles' lateral kinematics and the uncertainty of their movements. • The proposed model is proven to be more accurate than traditional SSM in identifying typical risky scenarios. • FL-iTTC outperforms other two-dimensional SSM in quantifying crash risks. • FL-iTTC provides a reliable tool to evaluate various crash risks associated with lateral movements such as cut-in and sideswipe. Surrogate Safety Measures (SSM) are extensively applied in safety analysis and design of active vehicle safety systems. However, most existing SSM focus only on the one-dimensional interactions along the vehicle traveling direction and cannot handle the crash risks associated with vehicle lateral movements such as sideswipes and angle crashes. To bridge this important knowledge gap, this study proposes a two-dimensional SSM defined based on Fuzzy Logic and the Inverse Time to Collision (FL-iTTC), which accounts for neighboring vehicles' lateral kinematics and the uncertainty of their movements. The proposed FL-iTTC are proven to be more accurate than traditional SSM in identifying typical risky scenarios, including harsh decelerations, sudden lane-changes, cut-ins and pre-crashes that are extracted from the NGSIM dataset. Additionally, other naturalistic driving scenarios are extracted from the NGSIM dataset and are used to evaluate the effectiveness of different SSM in quantifying crash risks. FL-iTTC is compared with other two-dimensional SSM including Anticipated Collision Time (ACT) and Probabilistic Driving Risk Field (PDRF) based on the confusion matrix and the receiver operating characteristic (ROC) curve. The Area under the ROC Curve (AUC) is 0.923 for FL-iTTC, while only 0.891 for ACT and 0.907 for PDRF, which indicates FL-iTTC outperforms other two-dimensional SSM in risk assessment. Overall, the proposed FL-iTTC greatly complements existing SSM and provides a reliable and useful tool to evaluate various crash risks associated with vehicle lateral movements such as cut-in and sideswipe. [ABSTRACT FROM AUTHOR]

• Four types of reactions from leading vehicles when being tailgated were identified. • Changing lanes was the most prevalent reaction by leading vehicle. • Random Forests analysis showed three main factors related to lead vehicle's reactions. • For 95% of the cases, vehicles change lanes when being tailgated for over two minutes. • Male drivers were overrepresented in the tailgating events. • Middle-aged drivers tailgated more frequently than the other cohorts. Background: Tailgating is a common aggressive driving behavior that has been identified as one of the leading causes of rear-end crashes. Previous studies have explored the behavior of tailgating drivers and have reported effective solutions to decrease the amount or prevalence of tailgating. This paper tries to fill the research gap by focusing on understanding highway tailgating scenarios and examining the leading vehicles' reaction using existing naturalistic driving data. Method: A total of 1,255 tailgating events were identified by using the one-second time headway threshold criterion. Four types of reactions from the leading vehicles were identified, including changing lanes, slowing down, speeding up, and making no response. A Random Forests algorithm was employed in this study to predict the leading vehicle's reaction based on corresponding factors including driver, vehicle, and environmental variables. Results: The analysis of the tailgating scenarios and associated factors showed that male drivers were more frequently involved in tailgating events than female drivers and that tailgating was more prevalent under sunny weather and in daytime conditions. Changing lanes was the most prevalent reaction from the leading vehicle during tailgating, which accounted for more than half of the total events. The results of Random Forests showed that mean time headway, duration of tailgating, and minimum time headway were three main factors, which had the greatest impact on the leading vehicle drivers' reaction. It was found that in 95% of the events, leading vehicles would change lanes when being tailgated for two minutes or longer. Practical Applications: Results of this study can help to better understand the behavior and decision making of drivers. This understanding can be used in designing countermeasures or assistance systems to reduce tailgating behavior and related negative safety consequences. [ABSTRACT FROM AUTHOR]

The investigation of relationships between traffic crashes and relevant factors is important in traffic safety management. Various methods have been developed for modeling crash data. In real world scenarios, crash data often display the characteristics of over-dispersion. However, on occasions, some crash datasets have exhibited under-dispersion, especially in cases where the data are conditioned upon the mean. The commonly used models (such as the Poisson and the NB regression models) have associated limitations to cope with various degrees of dispersion. In light of this, a generalized event count (GEC) model, which can be generally used to handle over-, equi-, and under-dispersed data, is proposed in this study. This model was first applied to case studies using data from Toronto, characterized by over-dispersion, and then to crash data from railway-highway crossings in Korea, characterized with under-dispersion. The results from the GEC model were compared with those from the Negative binomial and the hyper-Poisson models. The cases studies show that the proposed model provides good performance for crash data characterized with over- and under-dispersion. Moreover, the proposed model simplifies the modeling process and the prediction of crash data. [ABSTRACT FROM AUTHOR]

• Scenario-specific electric vehicle behavioral model. • Higher accuracy in representing electric vehicles' movements. • Applicability to timid, aggressive and normal vehicles. • Ability to represent traffic oscillations of electric vehicles. Electric vehicles (EVs) are deemed to be a solution for reducing air pollution and greenhouse gas emissions. As a result, the market share has increased exponentially in recent years. Despite their distinct vehicle dynamics and characteristics, movement simulation models dedicated to EVs are yet to be developed. In this research, a micro-traffic flow model for EVs by considering their unique acceleration/deceleration characteristics is proposed to represent and simulate the movements of EVs in traffic flow, especially in congested traffic. Car-following pairs where second car is an EV were collected from Longpan mid road, Nanjing, China in March 2019 for model calibration and verification. The results show that the proposed EV behavior model outperforms traditional behavior models for both timid and aggressive drivers. In assessing the predictive power of the movement simulation models, we compare their performance for collected car-following pairs. The R-squared values indicate that the performance of the EV behavior model is similar to that of the asymmetric behavior model under free-flow conditions, but substantially better for congested scenarios. With this model, we can better understand and reproduce the trajectories and energy consumption of EVs in complex traffic flow scenarios, and especially in congested traffic. [ABSTRACT FROM AUTHOR]

The widespread adoption of emerging connected and automated vehicles (CAVs) highlights the need for identifying the roadway capacity of mixed traffic flow with CAVs and human-driven vehicles (HDVs) for future traffic management. Previous studies focus on analyzing the impacts of CAV technologies on the mixed traffic capacity. However, research on how different car-following behaviors of HDVs to follow CAVs affect the mixed traffic capacity is still lacking. This study proposes an analytical formulation of the mixed traffic capacity based on the probability distributions of HDVs and CAVs and three types of behavior willingness (i.e., believer, neutral, and skeptic) of HDV-following-CAV in a mixed traffic environment. The sufficient conditions are derived for mixed traffic capacity to increase or decrease with the behavior willingness and CAV penetration rate. Based on that, an analytical lane management (LM) model is further proposed to determine the optimal number of CAV dedicated lanes (CDLs) and CAV non-dedicated lane (CNL) strategies that maximize the mixed traffic throughput, considering the mixed traffic demand, behavior willingness, and CAV penetration rate. The results from numerical experiments show that the proposed LM strategies can significantly improve mixed traffic throughputs by deploying optimal CDLs and CNL strategies, compared to one without the LM strategy. The mixed traffic throughputs improve and reduce as the trust willingness and skeptic willingness increase and decrease, respectively. Moreover, the increment in CAV penetration rate does not certainly correspond to the high mixed traffic throughputs. The behavior willingness and heterogeneous headways could be jointly considered to guarantee the positive influences of CAV penetration rate on the mixed traffic throughputs when implementing the LM strategy for the mixed traffic flow. • An mixed traffic flow capacity model considering different car-following behaviors developed. • Sufficient conditions for mixed traffic capacity to increase/decrease with parameters derived. • Mixed traffic throughput models considering the lane-level CAV penetration rate formulated. • Numerical analysis to verify the effectiveness of the lane management strategy conducted. [ABSTRACT FROM AUTHOR]

Addressing the global challenge of traffic crashes necessitates transcending traditional statistical models, which often fail to fully capture the interactions between factors causing crashes. This oversight restricts the predictive accuracy and adaptability of current methodologies. Additionally, there is a notable gap in research that examines the links between behavior-cause relationships and crash injury severity. Our study deploys Natural Language Processing (NLP) and Frequent Pattern (FP) growth algorithm to mine crash narratives for behavior-cause connections, combines with the predictive strength of eXtreme Gradient Boosting (XGBoost) and the interpretative clarity offered by SHapley Additive exPlanations (SHAP), our approach not only predicts crash injury severity with satisfactory precision but also explains the influence of specific behavior-cause and environment conditions on crash outcomes. The integration of NLP and XGBoost, complemented by SHAP insights, has shown promising results with an accuracy of 0.79, outperforming traditional discrete choice models and competes closely with other machine learning approaches, including Support Vector Machines, Random Forest, Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM). Through detailed textual analysis and the establishment of a behavior-cause matrix, identifying five broad crash causes linked to 141 specific crash cause with behaviors, we uncover critical patterns such as the prominence of distracted driving in severe crashes. This comprehensive approach not only fills a critical research gap by linking behavior-cause relationships with injury severity but also sets the stage for developing targeted interventions to enhance road safety. • Merges NLP and XGBoost model for crash injury severity prediction. • Identifies crash causes and related driver behaviors using NLP and FP-growth. • Aids in developing traffic safety interventions based on SHAP interpretability. [ABSTRACT FROM AUTHOR]

• Enhanced dynamic Forward Collision Warning (FCW) model tailored for hazmat truck drivers with varying driving characteristics and risk levels. • Multi-objective optimization method employed to determine the optimal warning time considering real-time driving risk, driver acceptance, and driving comfort. • Differentiation in optimal warning times for driver categories under mid-level and low-level risks, while similar warning time for high-level risks. • Development of user profiles based on historical driving records improves driver trust and enhances driving safety with ADAS systems. The risky driving behavior of hazmat truck drivers is a crucial factor in many severe traffic accidents. In-vehicle Advanced Driving Assistance Systems (ADAS), integrating vehicle active safety and driver assistance technology, has been installed into hazmat trucks aiming to reduce driving risks during emergencies. This paper presents an enhanced dynamic Forward Collision Warning (FCW) model tailored for hazmat truck drivers with different driving characteristics and risk levels. Our objective is to determine the optimal moment to alert drivers during risky situations. The novelty of our approach lies in analyzing the driver's response mechanism to the warning by considering their characteristics and real-time driving risk levels. We employ a multi-objective optimization method that integrates real-time driving risk, driver acceptance, and driving comfort to calculate the optimal warning time. Our findings indicate that the appropriate warning time is similar for all drivers under high-level risks, while significant differentiation exists for different driver categories under mid-level and low-level risks. Additionally, aggressive drivers tend to follow leading vehicles closely and exhibit lower deceleration intentions when faced with dangers compared to normal and cautious drivers. Our research outcomes enable the development of user profiles for hazmat truck drivers based on extensive historical driving records, facilitating the analysis of driver response differences to FCWs. This enhances driving safety and improves driver trust in ADAS systems. [ABSTRACT FROM AUTHOR]

• A novel collision warning system is proposed based on the visual road environment schema. • This schema can quantify drivers' unequal attention to different road environmental elements and visual fields. • The visual motion state of vehicles is calculated by the sensitivity matrix and the vision matrix. • The effectiveness of this novel system is examined by vehicle and driver characteristics. • A grey target decision-making model is built to comprehensively evaluate the system. Drivers pay unequal attention to different road environmental elements and visual fields, which greatly influences their driving behavior. However, existing collision warning systems ignore these visual characteristics of drivers, which limits the performance of collision warning systems. Therefore, this study proposes a novel collision warning system based on the visual road environment schema, in order to enhance the support for avoiding potential dangers in objects and areas that are easily overlooked by the drivers' vision. To capture the above visual characteristics of drivers, the visual road environment schema that consists of the semantic layer, the scene depth layer, the sensitive layer, and the visual field layer is established by using several different deep neural networks, which realizes the recognition, quantization, and analysis of the road environment from the drivers' visual perspective. The effectiveness of the novel collision warning system is verified by the driving simulation experiment from six indicators, including warning distance, maximum lateral acceleration, maximum longitudinal deceleration, minimum collision time, reaction time, and heart rate. Additionally, a grey target decision-making model is built to comprehensively evaluate the system. The results show that compared with the traditional collision warning system, the novel collision warning system proposed in this study performs significantly better and can discover potential dangers earlier, give timely warnings, enhance the vehicles' lateral stability and driving comfort, shorten reaction time, and relieve the drivers' nervousness. By integrating the drivers' visual characteristics into the collision warning system, this study could help to optimize the existing collision warning system and promote the mutual understanding between intelligent vehicles and human drivers. [ABSTRACT FROM AUTHOR]