Distributed adaptive signal control optimization and stability analysis based on nonlinear small-gain theorem

Network-wide signal control optimization is of practical importance to shorten and stabilize travel time, improve productivity, enhance energy consumption efficiency, mitigate congestion, and reduce vehicle emissions. In this study, a deep learning–empowered distributed control strategy is developed to adaptively optimize network-wide traffic signal control coordination. To simplify the problem formulation and enhance its applicability, the entire traffic system is decomposed into multiple areas, and multilayer perceptron concepts are used to formulate traffic control system operations in each area. The distributed deep learning, velocity-based model predictive control (MPC) strategy is designed to optimize traffic signal coordination. Furthermore, a gain-scheduling control model is developed to linearize each learned nonlinear system around its most recent operating status, and then a distributed MPC controller is applied to the linearized systems. Simulation results demonstrate that the proposed control strategy can effectively reduce travel time by 15.1% compared with fixed-time control plans and by 8.0% compared with a decentralized control plan. This study is the first research effort to integrate the deep learning framework and multiagent MPC to optimize traffic control coordination. Moreover, a sufficient condition is theoretically formulated for the bounded-input, bounded-output stability of the closed-loop, large-scale traffic system based on the nonlinear small-gain theorem.