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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]

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]