Abstract
Traffic congestion has an impact on traffic efficiency and the quality of life. To address this issue, this paper proposes a distributed, cooperative negotiation method for connected vehicles in traffic flow optimization. In particular, when the connected vehicles obtain the traffic congestion alerts from the roadside units, they exchange their routing information and distribute the traffic flows across the roads by using a collective learning algorithm that does not rely on a centralized controller. Results exported from Simulation of Urban Mobility show that the proposed method outperforms traditional routing methods. In a high traffic demand scenario, the average travel time of the proposed method decreases by 35% and 12% compared with the shortest path routing and the dynamic traffic routing methods, respectively.
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Acknowledgements
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000051001) supervised by the IITP (Institute of Information and communications Technology Planning and Evaluation) in 2021. Also, this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2019K1A3A1A80113259).
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Nguyen, TH., Li, G., Jo, H., Jung, J.J., Camacho, D. (2022). Cooperative Negotiation in Connected Vehicles for Mitigating Traffic Congestion. In: Camacho, D., Rosaci, D., Sarné, G.M.L., Versaci, M. (eds) Intelligent Distributed Computing XIV. IDC 2021. Studies in Computational Intelligence, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-96627-0_12
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