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Understanding Urban Mobility Pattern with Cellular Phone Data: A Case Study of Residents and Travelers in Nanjing

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  • Fan Yang

    (Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China
    School of Transportation, Southeast University, Nanjing 211189, China)

  • Zhenxing Yao

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Fan Ding

    (Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China
    School of Transportation, Southeast University, Nanjing 211189, China)

  • Huachun Tan

    (Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China
    School of Transportation, Southeast University, Nanjing 211189, China)

  • Bin Ran

    (Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China
    School of Transportation, Southeast University, Nanjing 211189, China)

Abstract

The rapid development of urban metropolises has attracted a growing number of immigrants and travelers, increasing the burden on transportation systems. Previous research on urban mobility patterns have ignored the temporal variations and heterogeneity in divergent urban trip makers due to the limited data resolution and coverage. In this paper, we analyzed cellular phone data of more than five million travelers for one month in Nanjing, China and proposed a method to extract trip origin and destination information from cellular phone signal data. We found that mobility patterns are different for urban residents, short-term travelers, and transfer travelers, and that trip length distributions can best be described by gamma and exponential distributions. In addition to the daily trip length distribution models, we utilized the agglomerative hieratical clustering method in order to group similar hourly trip patterns and further proposed within-day trip length distribution models under different times of the day and days of the week.

Suggested Citation

  • Fan Yang & Zhenxing Yao & Fan Ding & Huachun Tan & Bin Ran, 2019. "Understanding Urban Mobility Pattern with Cellular Phone Data: A Case Study of Residents and Travelers in Nanjing," Sustainability, MDPI, vol. 11(19), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5502-:d:273603
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    References listed on IDEAS

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    5. Anastasios Noulas & Salvatore Scellato & Renaud Lambiotte & Massimiliano Pontil & Cecilia Mascolo, 2012. "A Tale of Many Cities: Universal Patterns in Human Urban Mobility," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-10, May.
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    Cited by:

    1. Miller, Seth & Laan, Zachary Vander & Marković, Nikola, 2020. "Scaling GPS trajectories to match point traffic counts: A convex programming approach and Utah case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    2. Fan Yang & Linchao Li & Fan Ding & Huachun Tan & Bin Ran, 2020. "A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers," Sustainability, MDPI, vol. 12(18), pages 1-15, September.

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