Industrial Networks and Intelligent Systems. 3rd International Conference, INISCOM 2017, Ho Chi Minh City, Vietnam, September 4, 2017, Proceedings

Research Article

Swarm Intelligence Inspired Adaptive Traffic Control for Traffic Networks

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  • @INPROCEEDINGS{10.1007/978-3-319-74176-5_1,
        author={Daxin Tian and Yu Wei and Jianshan Zhou and Kunxian Zheng and Xuting Duan and Yunpeng Wang and Wenyang Wang and Rong Hui and Peng Guo},
        title={Swarm Intelligence Inspired Adaptive Traffic Control for Traffic Networks},
        proceedings={Industrial Networks and Intelligent Systems. 3rd International Conference, INISCOM 2017, Ho Chi Minh City, Vietnam, September 4, 2017, Proceedings},
        proceedings_a={INISCOM},
        year={2018},
        month={1},
        keywords={Particle swarm optimization Traffic signal control Adaptive control},
        doi={10.1007/978-3-319-74176-5_1}
    }
    
  • Daxin Tian
    Yu Wei
    Jianshan Zhou
    Kunxian Zheng
    Xuting Duan
    Yunpeng Wang
    Wenyang Wang
    Rong Hui
    Peng Guo
    Year: 2018
    Swarm Intelligence Inspired Adaptive Traffic Control for Traffic Networks
    INISCOM
    Springer
    DOI: 10.1007/978-3-319-74176-5_1
Daxin Tian, Yu Wei, Jianshan Zhou, Kunxian Zheng, Xuting Duan,*, Yunpeng Wang, Wenyang Wang1, Rong Hui1, Peng Guo1
  • 1: China Automotive Technology and Research Center, Automotive Engineering Research Institute
*Contact email: 231665217@qq.com

Abstract

The internet of Vehicles (IoV) technologies have boosted diverse applications related to Intelligent Transportation System (ITS) and Traffic Information Systems (TIS), which have significant potential to advance management of complex and large-scale traffic networks. With the goal of adaptive coordination of a traffic network to achieve high network-wide traffic efficiency, this paper develops a bio-inspired adaptive traffic signal control for real-time traffic flow operations. This adaptive control model is proposed based on swarm intelligence, inspired from particle swarm optimization. It treats each signalized traffic intersection as a particle and the whole traffic network as the particle swarm, then optimizes the global traffic efficiency in a distributed and on-line fashion. Our simulation results show that the proposed algorithm can achieve the performance improvement in terms of the queuing length and traffic flow allocation.