Distributed Dynamic Route Guidance and Signal Control for Mobile Edge Computing-Enhanced Connected Vehicle Environment

Abstract

The benefit of real-time joint dynamic route guidance and signal control (DRG-SC) is usually compromised by a centralized framework since it naturally leads to an un-timely solution with the growing data-processing needs and problem-solving complexity. Mobile edge computing (MEC) pushes the data storage and computation from the remote cloud to local infrastructures and hence reduces response time and improves network bandwidth when further combined with 5G. As such, our study first develops a novel distributed framework to facilitate DRG-SC in connected vehicle (CV) environment with clarifying the MEC’s vital role. The method captures the interaction of vehicles’ routing and signal control, wherein we use a more realistic and accurate way to define the relationship between travel time and traffic volume. Vehicles make route decisions and cooperate to reach user optimal (UO) or system optimal (SO) traffic state. In tandem, the developed adaptive signal control (ASC) adjusts the signal timing plan with considering both the adjacent intersections’ traffic volume and the vehicles’ waiting time. Our method achieves significant reductions in vehicles’ average departure delay, waiting time and travel time when justified by a comprehensive case study implemented in SUMO. Moreover, the effectiveness of adopting such a distributed framework in saving computation time is verified. Overall, our study provides valuable and practical insights into the intelligent operation and control.