Joint Route Planning and Traffic Signal Timing for Connected Vehicles: An Edge Cloud Enabled Multi-Agent Game Method

Rising traffic congestion has become a major challenge to urban areas. Route planning and traffic signal timing are widely used methods to improve traffic efficiency. Although these two optimization problems are tightly coupled with each other, they have traditionally been studied separately in existing works. In this paper, we propose a decentralized framework that enables dynamic orchestration of route planning and traffic signal timing to improve traffic efficiency. In this framework, vehicles and traffic lights coordinate to offload traffic optimization tasks to their respective virtual agents on the edge cloud. Furthermore, considering the group rationality of agents, we formulate the joint optimization problem as an evolutionary game. With effective interactions, the agents leverage replicator dynamics to find the Nash equilibria for the evolutionary game, which can coordinate the integrated behavior of multiple vehicles and traffic lights. The simulation results show that the proposed method outperforms the baseline solutions, which optimizes the route planning and signal timing separately.