A Coordinated and Coupled Control and Optimization Method of Intelligent Connected Vehicle Platoons and Traffic Signals Based on Hierarchical Architecture

To address the challenge of the disconnection between vehicle platoon control and traffic signal optimization in a mixed traffic environment consisting of intelligent connected vehicles (ICV) and human-driven vehicles (HDV), this study investigates a hierarchical "vehicle-signal" cooperative control architecture. The aim is to achieve integrated and efficient allocation of road spatio-temporal resources through dynamic interaction between lower-level ICV car-following control and upper-level signal optimization. In the lower-level control, to enhance platoon stability and robustness, this study improves the classical intelligent driver model (IDM) and constructs an enhanced platoon-based intelligent driver model (PIDM). Its core innovation lies in introducing a multi-predecessor state feedback mechanism, whereby the acceleration of a following vehicle is determined not only by the state of its immediate predecessor but also incorporates the velocity and spacing information of the leader vehicle as a feedforward compensation term. This mechanism is implemented via an adjustable weighting coefficient k, effectively suppressing platoon string instability caused by wave propagation effects. Furthermore, using Lyapunov stability theory, this study rigorously proves that even if a single vehicle experiences short-term acceleration or deceleration failures causing deviation from the equilibrium state, this feedback mechanism ensures the entire platoon system asymptotically returns to a stable operating equilibrium point. In the upper-level control, a signal optimization strategy dynamically coupled with the lower-level platoon states is designed. This strategy pioneers the combination of a "dedicated ICV phase" and an adaptive green wave coordination algorithm: on one hand, it provides exclusive time windows for ICV platoons; on the other hand, based on real-time predictions of platoon average speed and arrival time output by the PIDM, it dynamically adjusts phase sequence and offset to generate a seamless "green wave" band through multiple intersections, thereby minimizing stop delays for both ICV platoons and subsequent HDVs. Simulation experiments demonstrate that the proposed PIDM control model can gradually restore an ICV platoon to its original equilibrium state after short-term acceleration or deceleration failures cause deviation from stability. When the feedback weighting coefficient k falls within the range of [0.075, 0.125], the PIDM achieves satisfactory control performance; ideal control effectiveness is obtained when the response delay time T is 0 s. As the response delay time T increases in the platoon model, the amplitude and frequency of the system control input both increase, yet the platoon system remains stable. Moreover, under an ICV penetration rate of 80%, the cooperative control scheme improves the total intersection throughput by 14.16% and the capacity of the ICV-dedicated lane by 14.78% compared to a scheme without a dedicated phase. The results verify the effectiveness of the hierarchical architecture in significantly enhancing the spatio-temporal resource utilization of ICVs while ensuring the traffic efficiency of HDVs.