Integrated Control for Mixed CAV and CV Traffic Flow in Expressway Merge Zones Combined with Variable Speed Limit, Ramp Metering, and Lane Changing

Abstract

Abstract-Facing the congestion challenges of mixed road networks comprising expressways and arterial road networks, traditional control solutions fall short. To effectively alleviate traffic congestion in mixed road networks, it is crucial to clear the interaction between expressways and arterial networks and achieve orderly coordination between them. This study employs the multi-class cell transmission model (CTM) combined with the macroscopic fundamental diagram (MFD) to model the traffic dynamics of expressway systems and arterial subregions, enabling vehicle path tracking across these two systems. Consequently, a comprehensive traffic transmission model suitable for mixed road networks has been integrated. Utilizing the SUMO software, a simulation platform for the mixed road network is established, and the average trip lengths within the model have been calibrated. Based on the proposed traffic model, this study constructs a route guidance model for mixed road networks and develops an integrated model predictive control (MPC) strategy that merges route guidance, perimeter control, and ramp metering to address the challenges of mixed road networks' traffic flow control. A case study of a scenario in which a bidirectional expressway connects two subregions is conducted, and the results validate the effectiveness of the proposed cooperative guidance and control (CGC) method in reducing overall congestion in mixed road networks.

Reinforcement Learning-Based Dynamic Zone Positions for Mixed Traffic Flow Variable Speed Limit Control with Congestion Detection

“…The proposed optimal differential VSL control method was developed to implement varying speed limits for individual lanes, incorporating a multi-objective approach near the off-ramp. A comprehensive approach to managing traffic flow in the merging area of highways was introduced in [27], integrating CAVs' active lane change technology with conventional VSL and RM strategies. Traffic state estimation of the mainline and on-ramp traffic flow was performed by using an upgraded cell transmission model, while the genetic algorithm was used to compute the optimal speed limit of mainline traffic flow, the number of mainline vehicles changing lanes, and RM control to maximize the traffic flow in the merging area.…”