关键词: 智能交通, 分层协同控制, 虚拟编队, 智能网联车辆, 匝道合流, 时空约束

Abstract: To address the issues such as low traffic efficiency and frequent vehicle conflicts in highway ramp merging areas caused by the coupling of spatial and temporal constraints for intelligent connected vehicles, this paper proposes a hierarchical cooperative control framework using a "global optimization-local tracking" structure. The upper layer generates a globally optimal target merging time sequence through a mixed-integer linear programming (MILP) scheduler, while the lower layer uses a virtual platoon based distributed controller to achieve precise vehicle trajectory tracking. Based on a co-simulation platform combining SUMO and Python, the study constructs ramp merging scenarios and analyzes indicators such as average vehicle speed, average delay, and throughput under different traffic volumes. The results show that the proposed method can effectively mitigate the "bottleneck effect" and disturbance the propagation caused by vehicle waiting under uncontrolled conditions, keeping the per-vehicle delay within 5 seconds; compared with the uncontrolled natural merging, the cooperative merging control method can increase the speed of average vehicle by 23.4% ~46.7% and reduce the average delay by 37.2%~74.9% across different traffic demand levels; compared with the first-come-first-served merging control, it can increase the speed of average vehicle by 2.1%~15.9% and reduce the average delay by 4.7%~33.3% under high ramp traffic demand. Furthermore, when the combined density of mainline and ramp traffic reaches 2 800 vehicles per hour, the system throughput tends to saturate, with the total number of vehicles passing stabilizing at around 119, which reflects good traffic stability.

Key words: intelligent transportation, hierarchical coordinated control, virtual platooning, intelligent connected vehicles, ramp merging, spatiotemporal constraints