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Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data

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  • Xia, Dawen
  • Jiang, Shunying
  • Yang, Nan
  • Hu, Yang
  • Li, Yantao
  • Li, Huaqing
  • Wang, Lin

Abstract

Mobile trajectory big data, such as global positioning system (GPS) trajectories of taxicabs, have enormous social and economic values in intelligent transportation systems, and efficient mining and in-depth analysis of these data can provide beneficial decision-making for traffic operation and especially for passenger travel. To this end, in this paper, we discover the temporal, spatial, and spatiotemporal characteristics of passenger travel by analyzing the travel duration and the travel distance with large-scale taxi trajectory data, which can not only effectively reveal the resident travel patterns but also dynamically perceive the traffic conditions. Specifically, we propose an information framework to preprocess the taxi GPS trajectory data that can thoroughly identify passenger travel trajectories. Moreover, we develop a novel distribution model to explore the rules of citizen travel accurately. Finally, we put forward a parallel clustering approach on Spark, which can discover the spatiotemporal characteristics of passenger travel in a fine-grained manner, to obtain the characteristic changes of inhabitant travel at different periods of the day. In particular, the experimental results from an empirical study show that the travel duration and the travel distance follow a log-normal distribution, and the tail of the travel distance is highly fitting to a three-parameter gamma distribution and the long-distance travel of the day is mainly to the airport between 5:00 and 6:00 AM.

Suggested Citation

  • Xia, Dawen & Jiang, Shunying & Yang, Nan & Hu, Yang & Li, Yantao & Li, Huaqing & Wang, Lin, 2021. "Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    • Handle: RePEc:eee:phsmap:v:578:y:2021:i:c:s0378437121003290
    DOI: 10.1016/j.physa.2021.126056
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    References listed on IDEAS

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    Cited by:

    1. Yu, Liping & Liu, Huiran & Fang, Zhiming & Ye, Rui & Huang, Zhongyi & You, Yayun, 2023. "A new approach on passenger flow assignment with multi-connected agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    2. Xu, Xinpeng & Tao, Hongfei & Wu, Weiguo & Liu, Song, 2023. "An instant discovery method for companion vehicles based on incremental and parallel calculation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    3. Xia, Dawen & Jiang, Shunying & Li, Yunsong & Yang, Nan & Hu, Yang & Li, Yantao & Li, Huaqing, 2023. "An ASM-CF model for anomalous trajectory detection with mobile trajectory big data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
    4. Jinrui Zang & Pengpeng Jiao & Guohua Song & Zhihong Li & Tingyi Peng, 2022. "A Novel Environment Estimation Method of Whole Sample Traffic Flows and Emissions Based on Multifactor MFD," IJERPH, MDPI, vol. 19(24), pages 1-26, December.

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