Joint Optimization of Personalized Route Planning and Global Traffic Scheduling: A DT-Enabled Integrated Framework

Efficient route planning and traffic scheduling are the key enabling technologies to achieve intelligent transportation systems (ITS). However, the existing schemes that use driving time or driving distance as indicators cannot meet the personalized requirements of vehicles. In addition, the existing research usually optimizes the route planning and traffic scheduling separately, which is difficult to effectively improve the traffic efficiency in practical applications. In this paper, by considering the personalized requirements of vehicles and the target of the traffic manager (TM), a digital-twin (DT) enabled integrated framework is proposed to jointly optimize the personalized route planning and global traffic scheduling. Specifically, based on the collection of traffic data, we first develop a DT architecture for route planning and traffic scheduling. With this DT architecture, we then consider the traffic condition of each road section and design a global traffic scheduling algorithm to help the DT of the TM (DT-TM) set the optimal reward for each road section to motivate and guide the DTs of vehicles (DT-Vs) to select their routes. After that, by considering the preference of each DT-V and the reward of each road section, a personalized route planning algorithm is designed to plan the optimal route for the DT-V. Since the reward of each road section set by the DT-TM and the route selected by each DT-V affect each other, we thus design a joint optimization algorithm based on dynamic iteration to obtain an equilibrium solution for the DT-Vs and the DT-TM. The simulation results show that the proposed framework can schedule the traffic more effectively and bring higher utility to both the vehicles and the TM than the benchmark schemes.