Optimized Multicar Dynamic Route Planning Based on Time-Hierarchical Graph Model

Traffic congestion has become a major concern in most cities all over the world. The proper guidance of cars with an effective route planning method has become a fundamental and smart way to alleviate congestion under existing urban road facilities. Current route planning methods mainly focus on a single car, but ignoring the dynamic effect between cars may lead to severe congestion during the actual driving guidance. In this article, we extend the study of route planning to the case of multiple cars and present a novel multicar shortest travel-time routing problem. The objective is to minimize the average travel time by considering the dynamic effect of the induced traffic congestion on travel speed, while ensuring that each car's travel distance is within an acceptable range. We construct a time-hierarchical graph model for structuring the spatiotemporal dynamic properties of the urban road network and then develop a two-level multicar route planning optimization method for complex problem solving. The experimental results show that our path recommendations reduce the average travel time by 51.74% and 38.87% on average compared to two representative methods. Our research will become more important in the years ahead as self-driving cars become more commonplace.