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Research on traffic congestion characteristics of city business circles based on TPI data: The case of Qingdao, China

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  • Sun, Qiuxia
  • Sun, Yixin
  • Sun, Lu
  • Li, Qing
  • Zhao, Jianli
  • Zhang, Yu
  • He, Hao

Abstract

This study aims to investigate the congestion of the urban business circle based on traffic performance index (TPI) data. A hierarchical clustering algorithm is adopted for the data analysis. A dataset of nearly 64,260 pieces of TPI data from July to October 2017 in Qingdao is collected, and its features, such as interval characteristics, mean time distribution and spatiotemporal correlation, are analyzed. The results show that the southern coastal commercial circle of Qingdao is more congested than the other circles; there exists a morning and afternoon peak, with two peaks on workdays; otherwise, weekend and vacation periods do not show congestion. The congestion level toward the end of the vacation week (October 4th–8th) is lower than that during the beginning (October 1st–3rd). Considering the temporal and spatial dimensions, the causes of the two different congestion states during the holidays are speculated upon The characteristics of traffic congestion in Qingdao business circle and its possible causes are proposed, and a new design and overall plan for Qingdao is promoted.

Suggested Citation

  • Sun, Qiuxia & Sun, Yixin & Sun, Lu & Li, Qing & Zhao, Jianli & Zhang, Yu & He, Hao, 2019. "Research on traffic congestion characteristics of city business circles based on TPI data: The case of Qingdao, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    • Handle: RePEc:eee:phsmap:v:534:y:2019:i:c:s0378437119312841
    DOI: 10.1016/j.physa.2019.122214
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    References listed on IDEAS

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    1. Chrobok, R. & Kaumann, O. & Wahle, J. & Schreckenberg, M., 2004. "Different methods of traffic forecast based on real data," European Journal of Operational Research, Elsevier, vol. 155(3), pages 558-568, June.
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    Cited by:

    1. Wang, Chun & Zhang, Weihua & Wu, Cong & Hu, Heng & Ding, Heng & Zhu, Wenjia, 2022. "A traffic state recognition model based on feature map and deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    2. Chen, Hengrui & Zhou, Ruiyu & Chen, Hong & Lau, Albert, 2022. "A resilience-oriented evaluation and identification of critical thresholds for traffic congestion diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    3. Sun, Qiuxia & Zhang, Yu & Sun, Lu & Li, Qing & Gao, Peng & He, Hao, 2021. "Spatial–temporal differences in operational performance of urban trunk roads based on TPI data: The case of Qingdao," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 568(C).
    4. Jinrui Zang & Pengpeng Jiao & Sining Liu & Xi Zhang & Guohua Song & Lei Yu, 2023. "Identifying Traffic Congestion Patterns of Urban Road Network Based on Traffic Performance Index," Sustainability, MDPI, vol. 15(2), pages 1-22, January.

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