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An adaptive map-matching algorithm based on hierarchical fuzzy system from vehicular GPS data

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  • Jinjun Tang
  • Shen Zhang
  • Yajie Zou
  • Fang Liu

Abstract

An improved hierarchical fuzzy inference method based on C-measure map-matching algorithm is proposed in this paper, in which the C-measure represents the certainty or probability of the vehicle traveling on the actual road. A strategy is firstly introduced to use historical positioning information to employ curve-curve matching between vehicle trajectories and shapes of candidate roads. It improves matching performance by overcoming the disadvantage of traditional map-matching algorithm only considering current information. An average historical distance is used to measure similarity between vehicle trajectories and road shape. The input of system includes three variables: distance between position point and candidate roads, angle between driving heading and road direction, and average distance. As the number of fuzzy rules will increase exponentially when adding average distance as a variable, a hierarchical fuzzy inference system is then applied to reduce fuzzy rules and improve the calculation efficiency. Additionally, a learning process is updated to support the algorithm. Finally, a case study contains four different routes in Beijing city is used to validate the effectiveness and superiority of the proposed method.

Suggested Citation

  • Jinjun Tang & Shen Zhang & Yajie Zou & Fang Liu, 2017. "An adaptive map-matching algorithm based on hierarchical fuzzy system from vehicular GPS data," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-11, December.
    • Handle: RePEc:plo:pone00:0188796
    DOI: 10.1371/journal.pone.0188796
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Tang, Jinjun & Liang, Jian & Zhang, Shen & Huang, Helai & Liu, Fang, 2018. "Inferring driving trajectories based on probabilistic model from large scale taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 566-577.
    2. Lingling Fan & Liang Tang & Shaokuan Chen, 2018. "Optimizing location of variable message signs using GPS probe vehicle data," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-20, July.
    3. Yang, Qiaoli & Shi, Zhongke, 2018. "The evolution process of queues at signalized intersections under batch arrivals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 413-425.

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