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Identifying traffic clusters in urban networks based on graph theory using license plate recognition data

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  • Yu, Yi
  • Cui, Yanlei
  • Zeng, Jiaqi
  • He, Chunguang
  • Wang, Dianhai

Abstract

Monitoring traffic states of urban road networks is essential to relieving traffic burdens and designing traffic management strategies. However, most traffic state estimation methods are data-driven and barely consider the traffic network topology characteristics. In this paper, we propose a model-driven approach to partition the network into several traffic clusters, providing a macro-perspective for traffic state monitoring and potential support for perimeter controls. Traffic state and network topology information are both considered in the approach. We obtain the traffic state index of each segment with license plate recognition data and traffic network topology information, considering the heterogeneity and direction of segments. The road graph is constructed based on graph theory, and we modify the RatioCut algorithm and turn hyper-parameters automatically to identify traffic clusters in the road graph. We verified the proposed approach by a large-scale urban network in Hangzhou, China. The visualization and comparison results with conventional spectral clustering, DBSCAN, and K-means demonstrate the superiority and application value of the proposed approach.

Suggested Citation

  • Yu, Yi & Cui, Yanlei & Zeng, Jiaqi & He, Chunguang & Wang, Dianhai, 2022. "Identifying traffic clusters in urban networks based on graph theory using license plate recognition data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    • Handle: RePEc:eee:phsmap:v:591:y:2022:i:c:s037843712100947x
    DOI: 10.1016/j.physa.2021.126750
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    References listed on IDEAS

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    Cited by:

    1. Xueting Zhao & Liwei Hu & Xingzhong Wang & Jiabao Wu, 2022. "Study on Identification and Prevention of Traffic Congestion Zones Considering Resilience-Vulnerability of Urban Transportation Systems," Sustainability, MDPI, vol. 14(24), pages 1-23, December.

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