影响因子：7.9
DOI码：10.1016/j.trc.2025.105298
所属单位：Hefei University of Technology
发表刊物：Transportation Research Part C
刊物所在地：UK
项目来源：National Natural Science Foundation of China
关键字：Freeway; Merging zone; Mixed-vehicle traffic flow; Cooperative control; Model predictive control (MPC)
摘要：The manner and intensity of vehicle interactions in a mixed-vehicle traffic flow differ from those in a typical traffic flow. This difference leads to greater potential conflicts and decreased efficiency in freeway merging zones, which involve a large amount of vehicle crossing behaviour. To avoid the deterioration of traffic status, cooperative control of safety and efficiency for mixedvehicle traffic flow using connected and automated vehicles (CAVs) in freeway merging zones is proposed. First, a multi-objective nonlinear mixed-integer program model for cooperative safety and efficiency is presented at the vehicle level to optimize CAV’s behavioural decisions using historical predicted data. Second, a Transformer neural network is adopted to forecast the traffic state under different control weights, accounting for the dynamic characteristics of the traffic system. An adaptive weighting model is constructed to choose the optimal solution from the Pareto frontier derived from the multi-objective problem. To ensure the feasibility of vehicle level decisions and to facilitate system-level optimization, CAVs are capable of sharing and coordinating their behaviour decisions through iterations. A typical scenario involving a two-lane freeway merging area is analysed, and the results show that the cooperative control strategy can effectively optimize the traffic state. Even at 20% CAV penetration rates, this strategy reduces total parking delays by 48.7% and time-integrated time-to-collision (TIT) by 72.2%.
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：179
页面范围：105298
字数：14000
是否译文：否
发表时间：2025-07-16
收录刊物：SCI、EI
发布期刊链接：https://doi.org/10.1016/j.trc.2025.105298