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A quantum particle swarm optimization driven urban traffic light scheduling model

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Abstract

Urban traffic congestion becomes a severe problem for many cities all around the world. How to alleviate traffic congestions in real cities is a challenging problem. Benefited from concise and efficient evolution rules, the Biham, Middleton and Levine (BML) model has a great potential to provide favorable results in the dynamic and uncertain traffic flows within an urban network. In this paper, an enhanced BML model (EBML) is proposed to effectively simulate the urban traffic where the timing scheduling optimization algorithm (TSO) based on the quantum particle swarm optimization is creatively introduced to optimize the timing scheduling of traffic light. The main contributions include that: (1) The actual urban road network with different two-way multi-lane roads is firstly mapped into the theoretical lattice space of BML. And the corresponding updating rules of each lattice site are proposed to control vehicle dynamics; (2) compared with BML, a much deeper insight into the phase transition and traffic congestions is provided in EBML. And the interference among different road capacities on forming traffic congestions is elaborated; (3) based on the scheduling simulation of EBML, TSO optimizes the timing scheduling of traffic lights to alleviate traffic congestions. Extensive comparative experiments reveal that TSO can achieve excellent optimization performances in real cases.

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Acknowledgments

This work is supported by the National Natural Science Foundation, China (No. 61572369), the Hubei Province Natural Science Foundation (No. 2015CFB423), and Wenbin Hu is the grant recipient of these two foundations.

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Authors and Affiliations

  1. School of Computer, Wuhan University, Wuhan City, 430072, Hubei Province, China

    Wenbin Hu, Huan Wang, Zhenyu Qiu, Cong Nie & Liping Yan

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  1. Wenbin Hu
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  2. Huan Wang
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  3. Zhenyu Qiu
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  5. Liping Yan
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Correspondence to Wenbin Hu.

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Wenbin Hu declares that he has no conflict of interest in this paper.

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Hu, W., Wang, H., Qiu, Z. et al. A quantum particle swarm optimization driven urban traffic light scheduling model. Neural Comput & Applic 29, 901–911 (2018). https://doi.org/10.1007/s00521-016-2508-0

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  • DOI: https://doi.org/10.1007/s00521-016-2508-0

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