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GPS Trajectory Clustering and Visualization Analysis

Author

Listed:
  • Li Cai

    (Fudan University
    Yunnan University)

  • Sijin Li

    (Yunnan University)

  • Shipu Wang

    (Yunnan University)

  • Yu Liang

    (Yunnan University)

Abstract

The trajectory data of taxies containing time dimensional and spatial dimensional information is an important kind of traffic data. How to obtain valuable information from these data has become a hot topic in the field of intelligent transportation. Existing trajectory clustering algorithms can only compute similarities using partial characteristics of the trajectory data, leading to clustering results are not accurate. This study proposes a novel trajectory clustering algorithm named GLTC, which can obtain more accurate number of clusters based on the global and local characteristics of trajectories. This study intuitively displays the laws and knowledge in clustering results using visualization techniques. Experimental results reveal that the GLTC algorithm can discover more accurate clustering results, effectively display spatial-temporal change trends in GPS data, and better assist in analyzing the flow law of urban citizens and urban traffic conditions using visualization methods.

Suggested Citation

  • Li Cai & Sijin Li & Shipu Wang & Yu Liang, 2018. "GPS Trajectory Clustering and Visualization Analysis," Annals of Data Science, Springer, vol. 5(1), pages 29-42, March.
    • Handle: RePEc:spr:aodasc:v:5:y:2018:i:1:d:10.1007_s40745-017-0131-2
    DOI: 10.1007/s40745-017-0131-2
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    Cited by:

    1. Christine Keller & Felix Glück & Carl Friedrich Gerlach & Thomas Schlegel, 2022. "Investigating the Potential of Data Science Methods for Sustainable Public Transport," Sustainability, MDPI, vol. 14(7), pages 1-26, April.

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