A double-layer collaborative apportionment method for personalized and balanced routing

Improving the travel efficiency of citizens and the operation efficiency of urban has always been the goal of Intelligent Transportation System. Due to the neglect of strong traffic demand and driving behaviour preferences, coupled with the insufficient of communication and computing power, the existing measures based on the centralized control of vehicles or mandatory traffic restrictions lead to the traffic efficiency dramatically deviates from the system optimum. Which puts forward an urgent demand for multi-vehicle collaborative apportionment, but also brings challenges. In this paper, a double-layer collaborative apportionment method for connected vehicles, 2L-CoV for short, is proposed under the assistance of Space-air-ground integrated networks. 2L-CoV includes the traffic flow scheduling in global-layer and the vehicle routing planning in local-layer. Firstly, a distributed collaborative framework based SAGIN is presented to make a large-scale of virtual vehicles can interact with each other. Then, at the global-layer, traffic flow is guided speedily by an improved back-pressure algorithm to complete traffic flow scheduling; at the local-layer, considering the driving behaviour preferences, a game evolution online learning approach based on dominant strategy is proposed to plan the vehicle routing. Finally, the simulation results show that 2L-CoV can effectively balance the traffic network, improves the network throughput, and reduces the total travel time.