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Cooperative Negotiation in Connected Vehicles for Mitigating Traffic Congestion

Authors
Nguyen, Tri-HaiLi, GenJo, HyoenseongJung, Jason J.Camacho, David
Issue Date
2022
Publisher
Springer Science and Business Media Deutschland GmbH
Citation
Studies in Computational Intelligence, v.1026, pp 125 - 134
Pages
10
Journal Title
Studies in Computational Intelligence
Volume
1026
Start Page
125
End Page
134
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/58205
DOI
10.1007/978-3-030-96627-0_12
ISSN
1860-949X
1860-9503
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. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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