A Multi-Centralized Strategy for Trajectory-Based Active Traffic Management With Cooperative Automation

Active Traffic Management (ATM) plays a crucial role in alleviating congestion. However, traditional ATM strategies struggle to precisely control traffic demand to match bottleneck capacity. This limitation not only worsens congestion but also negatively impacts traffic, resulting in increased vehicle cruising discomfort and traffic flow fluctuations. To address this issue, this paper proposes a multi-centralized Trajectory-based Traffic Management (TTM) strategy. The goal is to enable precise demand control for multi-segment scenarios with the help of Connected and Automated Vehicles (CAVs). It is able to regulate traffic demand by precisely matching bottleneck capacity. The proposed TTM strategy bears three novel features: 1) stronger demand regulation capability in terms of control range and precision; 2) reduced negative impacts to achieve improved vehicle cruising comfort and traffic flow stability; and 3) enhanced computation efficiency to avoid the curse of dimensionality. The proposed TTM is evaluated against the conventional ATM strategy, named variable speed limit. The results confirm the aforementioned features of the proposed method and demonstrate its superiority over the conventional approach. Additional discussion highlights that the proposed TTM enhances lane-change smoothness by 78.6%. It is also revealed that the multi-centralized structure of the proposed TTM is critical in achieving a balance between global optimality and computational efficiency.