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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]

Objectives: This article focuses on the effect of road lighting on road safety at accesses to quantitatively analyze the relationship between road lighting and road safety.Methods: An artificial neural network (ANN) was applied in this study. This method is one of the most popular machine learning methods and does not require any predefined assumptions. This method was applied using field data collected from 10 road segments in Nanjing, Jiangsu Province, China.Results: The results show that the impact of road lighting on road safety at accesses is significant. In addition, road lighting has a greater influence when vehicle speeds are higher or the number of lanes is greater. A threshold illuminance was also found, and the results show that the safety level at accesses will become stable when reaching this value.Conclusions: Improved illuminance can decrease the speed variation among vehicles and improve safety levels. In addition, high-grade roads need better illuminance at accesses. A threshold value can also be obtained based on related variables and used to develop scientific guidelines for traffic management organizations. [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]

• 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]