Cooperative Negotiation in Connected Vehicles for Mitigating Traffic Congestion
- Authors
- Nguyen, Tri-Hai; Li, Gen; Jo, Hyoenseong; Jung, 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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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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