Network-Level Hierarchical Bottleneck Congestion Control Method for a Mixed Traffic Network

Due to the escalating transportation demand and the significant ramifications of traffic congestion, there is an imperative to investigate the sources of congestion, known as "congestion bottlenecks". The implementation of control methods ahead of the occurrence of congestion is crucial. Connected and autonomous vehicles (CAVs) have a high potential within the field of traffic control. CAVs are exceptionally controllable and facilitate management feasibility. This study utilizes the high compliance of CAVs to provide an effective solution for the congestion management problem at the network level when mixed traffic flows are saturated. A linear programming model to reduce average travel time over the road network is developed. By utilizing a genetic algorithm, the optimal traffic demand regulation scheme can be obtained and the departure time of CAVs optimized. The effectiveness of the proposed method is validated through simulation across various road network scales, CAVs penetration rates, and controlled CAV proportions. The proposed method can only control a specific amount of CAVs, which, according to an analysis of the simulation results, significantly improves the performance of the transportation system. The importance of employing advanced control methods to improve the sustainability of urban transportation development and the travel experience is underscored in the conclusion.