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A Hybrid Method for Traffic State Classification Using K-Medoids Clustering and Self-Tuning Spectral Clustering

Author

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  • Qiang Shang

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Yang Yu

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Tian Xie

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

Abstract

As an important part of intelligent transportation systems, traffic state classification plays a vital role for traffic managers when formulating measures to alleviate traffic congestion. The proliferation of traffic data brings new opportunities for traffic state classification. In this paper, we propose a hybrid new traffic state classification method based on unsupervised clustering. Firstly, the k-medoids clustering algorithm is used to cluster the daily traffic speed data from multiple detection points in the selected area, and then the cluster-center detection points of the cluster with congestion are selected for further analysis. Then, the self-tuning spectral clustering algorithm is used to cluster the speed, flow, and occupancy data of the target detection point to obtain the traffic state classification results. Finally, several state-of-the-art methods are introduced for comparison, and the results show that performance of the proposed method are superior to comparable methods.

Suggested Citation

  • Qiang Shang & Yang Yu & Tian Xie, 2022. "A Hybrid Method for Traffic State Classification Using K-Medoids Clustering and Self-Tuning Spectral Clustering," Sustainability, MDPI, vol. 14(17), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:11068-:d:906959
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    References listed on IDEAS

    as
    1. Kaffash, Sepideh & Nguyen, An Truong & Zhu, Joe, 2021. "Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 231(C).
    2. Kerner, Boris S., 2004. "Three-phase traffic theory and highway capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 379-440.
    3. Cheng, Zeyang & Wang, Wei & Lu, Jian & Xing, Xue, 2020. "Classifying the traffic state of urban expressways: A machine-learning approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 411-428.
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