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Discovering urban mobility patterns with PageRank based traffic modeling and prediction

Author

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  • Wang, Minjie
  • Yang, Su
  • Sun, Yi
  • Gao, Jun

Abstract

Urban transportation system can be viewed as complex network with time-varying traffic flows as links to connect adjacent regions as networked nodes. By computing urban traffic evolution on such temporal complex network with PageRank, it is found that for most regions, there exists a linear relation between the traffic congestion measure at present time and the PageRank value of the last time. Since the PageRank measure of a region does result from the mutual interactions of the whole network, it implies that the traffic state of a local region does not evolve independently but is affected by the evolution of the whole network. As a result, the PageRank values can act as signatures in predicting upcoming traffic congestions. We observe the aforementioned laws experimentally based on the trajectory data of 12000 taxies in Beijing city for one month.

Suggested Citation

  • Wang, Minjie & Yang, Su & Sun, Yi & Gao, Jun, 2017. "Discovering urban mobility patterns with PageRank based traffic modeling and prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 485(C), pages 23-34.
    • Handle: RePEc:eee:phsmap:v:485:y:2017:i:c:p:23-34
    DOI: 10.1016/j.physa.2017.04.155
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    References listed on IDEAS

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    1. Xiaolei Ma & Haiyang Yu & Yunpeng Wang & Yinhai Wang, 2015. "Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-17, March.
    2. Su Yang & Shixiong Shi & Xiaobing Hu & Minjie Wang, 2015. "Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    3. Tang, Jinjun & Liu, Fang & Wang, Yinhai & Wang, Hua, 2015. "Uncovering urban human mobility from large scale taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 140-153.
    4. Cai, Hua & Zhan, Xiaowei & Zhu, Ji & Jia, Xiaoping & Chiu, Anthony S.F. & Xu, Ming, 2016. "Understanding taxi travel patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 590-597.
    5. Chengbin Peng & Xiaogang Jin & Ka-Chun Wong & Meixia Shi & Pietro Liò, 2012. "Collective Human Mobility Pattern from Taxi Trips in Urban Area," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-8, April.
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    Cited by:

    1. Yuquan Zhou & Yingzhi Wang & Feng Zhang & Hongye Zhou & Keran Sun & Yuhan Yu, 2023. "GATR: A Road Network Traffic Violation Prediction Method Based on Graph Attention Network," IJERPH, MDPI, vol. 20(4), pages 1-18, February.
    2. Zhang, Mengyao & Huang, Tao & Guo, Zhaoxia & He, Zhenggang, 2022. "Complex-network-based traffic network analysis and dynamics: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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