Backpressure-Based Distributed Dynamic Route Control for Connected and Automated Vehicles

This study focuses on the dynamic route control (DRC) problem for connected and automated vehicles (CAVs). The problem is usually solved using two popular methods, i.e., dynamic shortest path (DSP) and dynamic system optimal assignment (DSO). However, the DSP algorithm is unproductive under congested conditions, while the DSO, although can achieve system optimal, possesses considerable algorithm complexity. Furthermore, the two methods are usually solved in a centralized system due to the need for global information of the entire network, which is both communicationally and computationally ineffective. With the emergence of mobile edge computing (MEC), a new distributed DRC method only relying on local information is more achievable and practical. In this context, this research developed a novel DRC algorithm based on the distributed Backpressure (BP) principle for the MEC-enabled CAVs. The BP herein is modified as a function of the real-time density, which avoids the unrealistic point queue assumption in the original BP algorithm. In addition, the real-time traffic state is pre-identified to determine whether re-routing is essential, thereby reducing the possibility of CAVs guided to unnecessarily long routes. Results from the case study simulated by the traffic simulation software Simulation of Urban MObility (SUMO) indicate that the proposed method outperforms the BP method without congestion identification in low-demand cases, and the performance is significantly better than the DSP while approximating the DSO in congested cases. More importantly, both communicational cost and algorithm execution time was greatly reduced when compared with the DSO.