Collaborative Vehicle Rerouting System With Dynamic Vehicle Selection

• Published in: IEEE transactions on intelligent transportation systems 2023-12, Vol.24 (12), p.14546-14555
• Main Authors: Ho, Mun Chon, Lim, Joanne Mun-Yee, Chong, Chun Yong, Chua, Kah Keong, Siah, Alvin Kuok Lim
• Format: Article
• Language: English
• Subjects: Carbon dioxide; Collaboration; Energy consumption; Fuels; Mobile ad hoc networks; Network topologies; Rerouteing; Roads; Roads & highways; simulation of urban mobility (SUMO); Traffic congestion; Traffic information; traffic prediction; traffic simulation; Transportation networks; Travel time; Vehicle dynamics; Vehicle rerouting; Vehicles; Vehicular ad hoc networks
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2023.3300326

Traffic congestion represents a prevailing challenge encountered in urban areas due to the substantial volume of vehicles. To alleviate traffic congestion during rush hours, an improved vehicle rerouting strategy designed for vehicular ad hoc networks (VANETs) is proposed in this paper. Prior studies have predominantly relied on the number of hops upstream from a congested road to select vehicles to be rerouted away from traffic congestion. Selecting vehicles in this way causes the distance between the selected vehicles and the congested road to become sensitive to the network topology, especially when the road lengths vary significantly. Different from the previous research, the proposed vehicle rerouting strategy considers travel time, which is a dynamic traffic information, in selecting vehicles to be rerouted. Furthermore, to achieve collaborative rerouting among vehicles, the proposed vehicle rerouting strategy updates the remaining capacity of roads each time a new route is assigned to a selected vehicle. This is to ensure that the roads are not overutilized and become congested in the near future. The performance of the proposed rerouting strategy is compared with other state-of-the-art strategies in terms of average travel time, average carbon dioxide (CO2) emission, and average fuel consumption through traffic simulations in two transportation networks, which are simple grid network and real-world Kuala Lumpur (KL) network. Based on the simulation results, the proposed vehicle rerouting strategy outperforms other strategies by at least 4.39% in Kuala Lumpur (KL) network in terms of average travel time.