American Journal of Traffic and Transportation Engineering

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Mining Urban Congestion Evolution Characteristics Based on Taxi GPS Trajectories

Received: 07 February 2020    Accepted: 24 February 2020    Published: 28 February 2020
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Abstract

The taxi GPS trajectories involve sufficient temporal and spatial characteristics and make it easy for us to obtain potential knowledge for understanding human mobility pattern and urban traffic network dynamics. Sensing urban traffic conditions not only enables traffic management authority to improve urban traffic management. It can also provide decision-making for residents and taxi drivers. A spectral clustering method is proposed for sensing traffic congestion using taxi GPS trajectories. First, taxi GPS trajectories are pre-processed and matched with the urban road network established based on the primal graph representation. Second, the average speed of the road segments is obtained according to the taxi GPS trajectories and a dynamic weighted graph of urban road network is constructed to capture complicated urban traffic network. Then, a spectral clustering method is developed to detect the urban traffic congestion. Finally, the congestion evolution characteristics in Lanzhou, China are visualized and analyzed during different periods in the weekdays and weekends. Experimental results show that the proposed method can effectively detect traffic congestion, and the results are consistent with the usual actual experience. Compared with other traffic congestion methods, the proposed method can detect urban traffic congestion with wider coverage and lower cost. Therefore, the proposed method can be integrated into the classic intelligent traffic system, assisting urban traffic prediction, personal travel route plan, route planning and navigation application.

DOI 10.11648/j.ajtte.20200501.11
Published in American Journal of Traffic and Transportation Engineering (Volume 5, Issue 1, January 2020)
Page(s) 1-7
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Urban Traffic, Traffic Congestion Evolution, Spectral Clustering, Dynamic Weighted Graph, Visualization, Taxi GPS Trajectories

References
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[17] Sun, S. N., Chen J., Sun, J. (2019). Traffic congestion prediction based on GPS trajectories data. International Journal of Distributed Sensor Networks, 15 (5), 1-18.
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Author Information
  • School of Mathematics and Statistics, Lanzhou University, Lanzhou, China

  • School of Mathematics and Statistics, Lanzhou University, Lanzhou, China

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  • APA Style

    Weiyan Xu, Yumei Huang. (2020). Mining Urban Congestion Evolution Characteristics Based on Taxi GPS Trajectories. American Journal of Traffic and Transportation Engineering, 5(1), 1-7. https://doi.org/10.11648/j.ajtte.20200501.11

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    Weiyan Xu; Yumei Huang. Mining Urban Congestion Evolution Characteristics Based on Taxi GPS Trajectories. Am. J. Traffic Transp. Eng. 2020, 5(1), 1-7. doi: 10.11648/j.ajtte.20200501.11

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    AMA Style

    Weiyan Xu, Yumei Huang. Mining Urban Congestion Evolution Characteristics Based on Taxi GPS Trajectories. Am J Traffic Transp Eng. 2020;5(1):1-7. doi: 10.11648/j.ajtte.20200501.11

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  • @article{10.11648/j.ajtte.20200501.11,
      author = {Weiyan Xu and Yumei Huang},
      title = {Mining Urban Congestion Evolution Characteristics Based on Taxi GPS Trajectories},
      journal = {American Journal of Traffic and Transportation Engineering},
      volume = {5},
      number = {1},
      pages = {1-7},
      doi = {10.11648/j.ajtte.20200501.11},
      url = {https://doi.org/10.11648/j.ajtte.20200501.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtte.20200501.11},
      abstract = {The taxi GPS trajectories involve sufficient temporal and spatial characteristics and make it easy for us to obtain potential knowledge for understanding human mobility pattern and urban traffic network dynamics. Sensing urban traffic conditions not only enables traffic management authority to improve urban traffic management. It can also provide decision-making for residents and taxi drivers. A spectral clustering method is proposed for sensing traffic congestion using taxi GPS trajectories. First, taxi GPS trajectories are pre-processed and matched with the urban road network established based on the primal graph representation. Second, the average speed of the road segments is obtained according to the taxi GPS trajectories and a dynamic weighted graph of urban road network is constructed to capture complicated urban traffic network. Then, a spectral clustering method is developed to detect the urban traffic congestion. Finally, the congestion evolution characteristics in Lanzhou, China are visualized and analyzed during different periods in the weekdays and weekends. Experimental results show that the proposed method can effectively detect traffic congestion, and the results are consistent with the usual actual experience. Compared with other traffic congestion methods, the proposed method can detect urban traffic congestion with wider coverage and lower cost. Therefore, the proposed method can be integrated into the classic intelligent traffic system, assisting urban traffic prediction, personal travel route plan, route planning and navigation application.},
     year = {2020}
    }
    

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  • TY  - JOUR
    T1  - Mining Urban Congestion Evolution Characteristics Based on Taxi GPS Trajectories
    AU  - Weiyan Xu
    AU  - Yumei Huang
    Y1  - 2020/02/28
    PY  - 2020
    N1  - https://doi.org/10.11648/j.ajtte.20200501.11
    DO  - 10.11648/j.ajtte.20200501.11
    T2  - American Journal of Traffic and Transportation Engineering
    JF  - American Journal of Traffic and Transportation Engineering
    JO  - American Journal of Traffic and Transportation Engineering
    SP  - 1
    EP  - 7
    PB  - Science Publishing Group
    SN  - 2578-8604
    UR  - https://doi.org/10.11648/j.ajtte.20200501.11
    AB  - The taxi GPS trajectories involve sufficient temporal and spatial characteristics and make it easy for us to obtain potential knowledge for understanding human mobility pattern and urban traffic network dynamics. Sensing urban traffic conditions not only enables traffic management authority to improve urban traffic management. It can also provide decision-making for residents and taxi drivers. A spectral clustering method is proposed for sensing traffic congestion using taxi GPS trajectories. First, taxi GPS trajectories are pre-processed and matched with the urban road network established based on the primal graph representation. Second, the average speed of the road segments is obtained according to the taxi GPS trajectories and a dynamic weighted graph of urban road network is constructed to capture complicated urban traffic network. Then, a spectral clustering method is developed to detect the urban traffic congestion. Finally, the congestion evolution characteristics in Lanzhou, China are visualized and analyzed during different periods in the weekdays and weekends. Experimental results show that the proposed method can effectively detect traffic congestion, and the results are consistent with the usual actual experience. Compared with other traffic congestion methods, the proposed method can detect urban traffic congestion with wider coverage and lower cost. Therefore, the proposed method can be integrated into the classic intelligent traffic system, assisting urban traffic prediction, personal travel route plan, route planning and navigation application.
    VL  - 5
    IS  - 1
    ER  - 

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