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Collaborative Vehicle Rerouting System With Dynamic Vehicle Selection
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AI
DOI：10.1109/TITS.2023.3300326.png)
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Abstract
En 中文
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 vanet 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 co(2) emission, and average fuel consumption through traffic simulations in two transportation networks, which are simple grid network and real-world 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.
Keywords:
Roads
Traffic congestion
Vehicle dynamics
Fuels
Collaboration
Vehicular ad hoc networks
Vehicles
Vehicle rerouting
traffic congestion
traffic simulation
simulation of urban mobility (SUMO)
traffic prediction
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6.3W
