A routing method using chaotic neurodynamics for the transportation networks with the next generation vehicles

Traffic congestion in the transportation networks, which increases vehicle travel times, energy consumption, and environmental pollution, is one of the major social problems occurring in most urban areas. One of the ways to reduce traffic congestion is to use effective routing methods to guide vehicles to their destinations that avoid or reduce traffic congestion. However, routing for vehicles, especially in urban areas, is difficult due to the dynamic and unpredictable nature of the transportation networks. To deal with these characteristics, the use of connected vehicles, which successfully obtain real-time data by using Internet communication, is attracting as the vehicles in the transportation networks. The ability to communicate over the Internet allows connected vehicles to communicate with other connected vehicles and infrastructure to monitor road congestion. In this work, we propose a routing method using chaotic neurodynamics for the transportation networks in which connected vehicle move and evaluate its effectiveness. In addition, we use Simulation of Urban MObility (SUMO) traffic simulator to construct realistic traffic environments. Numerical experiments using SUMO show that the routing method using chaotic neuro-dynamics shows effective routing performance compared to other routing methods in both the number of arriving vehicles and the average travel time.