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Understanding the sideslip risks of various trajectory patterns, as well as the impact of rainfall on them, is critical for improving road safety. However, the lack of precise classification indicators hampers systematic analysis of the variations in vehicle trajectory patterns. To address this, this study proposes a parameterized classification method for trajectories on curved segments, employing the radius and offset of the trajectory as the primary classification features and dividing the trajectories into nine patterns. These patterns represent variations from smaller to larger radii and inside to outside lane offsets, reflecting different driving behaviors and vehicle stability during vehicle cornering. Concurrently, the friction coefficient utilization rate is used to effectively compare vehicles' sideslip risk under different weather conditions. Based on this, we construct a framework using computer vision technology for automatically identifying trajectory patterns and measuring sideslip risk. We conducted an empirical study on a highway-curved segment with high sideslip risk in China and collected two datasets under clear and rainy conditions for analysis. The classification results show that the proposed method can effectively classify trajectories according to nine trajectory patterns. Comparative analysis reveals that vehicle trajectories in both the inside and outside lanes are notably more affected by rainfall compared to the middle lane. Meanwhile, trucks demonstrate a higher susceptibility to rainfall than cars. In addition, the analysis of the sideslip risk for different trajectory patterns discovers several high-risk patterns. This study provides an effective approach for monitoring and analyzing the sideslip risk on curved segments, thereby contributing to the enhancement of road design and traffic safety management., Author(s): Bo Wang [1,2,3]; Yiik Diew Wong (corresponding author) [2]; Chi Zhang (corresponding author) [1,3]; Hong Zhang [4]; Yanyang Gao [1,3] 1. Introduction Rainfall poses a severe threat to highway [...]

Driving behavior has frequently been overlooked in previous road traffic crash research. Hereby, abnormal (extreme) driving behavior data transmitted by the onboard navigation systems were collected for vehicles involved in traffic crashes, including sharp-lane-change, sharp-acceleration, and sudden-braking behaviors. Using these data in conjunction with expressway crash records, multiple classification learners were trained to establish a behavior-driven risk prediction model. To further investigate the influence of driving behavior on crash risk, partial dependence plots (PDPs) were applied. Regression analyses indicate that models have a stronger effect when derivative features such as frequency of specific deviant behavior, speed, and acceleration in the behavior process are included. The behavioral RUSBoost model surpasses other models, achieving an AUC prediction metric of 0.782 and outperforming traditional traffic-flow-driven machine learning models. PDP analysis demonstrates that the sudden-braking behavior is the leading contributory factor of expressway crashes, particularly when the acceleration exceeds 0.5G. This study confirms the potential of predicting crash risks through augmenting behavior data from navigation software; the findings lay a foundation for countermeasures., Author(s): Xiao-chi Ma [1,2,3,4]; Jian Lu (corresponding author) [1,2,3]; Yiik Diew Wong [4] 1. Introduction Traffic is a complex system that consists of the driver, vehicle, road, and environment, in [...]

As a cutting‐edge strategy to reduce travel delay and fuel consumption, platooning of connected and autonomous vehicles (CAVs) at signal‐free intersections has become increasingly popular in academia. However, when determining optimal platoon size, few studies have attempted to comprehensively consider the relations between the size of a CAV platoon and traffic conditions around an intersection. To this end, this study develops an adaptive platoon‐based autonomous intersection control model, named INTEL‐PLT, which adopts deep reinforcement learning technique to realize the optimization of multiple dynamic objectives (e.g., efficiency, fairness, and energy saving). The framework of INTEL‐PLT has a two‐level structure: The first level employs a reservation‐based policy integrated with a nonconflicting lane selection mechanism to determine the lanes' releasing priorities; and the second level uses a deep Q‐network algorithm to identify the optimal platoon size based on real‐time traffic conditions (e.g., traffic density, vehicle movement, etc.) of an intersection. The model is validated and examined on the simulator Simulation of Urban Mobility. It is found that the proposed model exhibits superior performances on both travel efficiency and fuel conservation as compared with state‐of‐the‐art methods in three typical traffic conditions. Moreover, several in‐depth insights learned from the simulations are provided in this paper, which could better explain the relation between platoon size and traffic condition. [ABSTRACT FROM AUTHOR]

Safety effects of Green-Man Countdown Device (GMCD) at signalized pedestrian crosswalks are evaluated. Pedestrian behavior at GMCD and non-GMCD crosswalks is observed and analyzed. A microsimulation model is developed based on field observations to estimate safety performance. Simulation outputs allow analysts to assess the impacts of GMCD at various conditions with different geometric layout, traffic and pedestrian volumes, and the green time. According to simulation results, it is found that the safety impact of GMCD is affected by traffic condition as well as different time duration within green-man signal phase. In general, GMCD increases average walking velocity, especially during the last few seconds. The installation of GMCD improves safety performance generally, especially at more crowded crossings. Conflict severity is increased during last 10 s after GMCD installation. Findings from this study suggest that the current practice, which is to install GMCD at more crowded crosswalks or near the school zone, is effective. Moreover, at crosswalks with GMCD, longer all red signal phase is suggested to improve pedestrian safety during intergreen period., 1. Introduction For an urban environment as Singapore, conflicts between pedestrians and vehicles occur frequently at the signalized pedestrian crosswalks. There are more than 1,400 signalized road intersections in Singapore, [...]

This paper develops a game-theoretic model to analyze the competition between two container freight transportation modes (shipping and railway) using competitive game strategic interactions method, namely, deterrence, by taking account of the most cost-effective scale of the transportation capacity settings. The competition was set against the background of China's Belt and Road (B&R) Initiative as a new situation for intercontinental Sino-Europe container freight transportation. The behavior of each mode (modeled as a carrier, resp.) is characterized by an optimization model with the objective of minimizing its cost by setting optimal basic freight rate and transportation deployment. A firm can use this method to compare the difference in the time value of the cargos and reduce the expense during the whole transportation process. Finally, the developed model is numerically evaluated by a case study of intercontinental transportation between Hefei (China) and Hamburg (Germany). The results show that deterrence effects largely depend on the deterrence objective, and the differential in the cost of two transportation modes tends to be stable with higher values in the deterrence objective. In the new intercontinental circumstance, the mode of railway transportation provides a new way to transport the cargos between China and Europe., 1. Introduction In recent years, shippers with regular transport demands (e.g., manufacturing enterprise and sales companies) are always eager to minimize their transportation cost in the process. They always endeavor [...]