IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0188796.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0188796
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0188796&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0188796?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Shan & Zhang, Ya & Wang, Zhengli & Gu, Shiyi, 2023. "AdaBoost-Bagging deep inverse reinforcement learning for autonomous taxi cruising route and speed planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    2. Chaogui Kang & Dongwan Fan & Hongzan Jiao, 2021. "Validating activity, time, and space diversity as essential components of urban vitality," Environment and Planning B, , vol. 48(5), pages 1180-1197, June.
    3. Zhang, Shen & Liu, Xin & Tang, Jinjun & Cheng, Shaowu & Qi, Yong & Wang, Yinhai, 2018. "Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 537-551.
    4. D. Woods & A. Cunningham & C. E. Utazi & M. Bondarenko & L. Shengjie & G. E. Rogers & P. Koper & C. W. Ruktanonchai & E. zu Erbach-Schoenberg & A. J. Tatem & J. Steele & A. Sorichetta, 2022. "Exploring methods for mapping seasonal population changes using mobile phone data," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-17, December.
    5. Yang, Zhuo & Franz, Mark L. & Zhu, Shanjiang & Mahmoudi, Jina & Nasri, Arefeh & Zhang, Lei, 2018. "Analysis of Washington, DC taxi demand using GPS and land-use data," Journal of Transport Geography, Elsevier, vol. 66(C), pages 35-44.
    6. 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).
    7. Tang, Jinjun & Zhang, Shen & Zhang, Wenhui & Liu, Fang & Zhang, Weibin & Wang, Yinhai, 2016. "Statistical properties of urban mobility from location-based travel networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 694-707.
    8. Lei Zhang & Guoxing Zhang & Zhizheng Liang & Ekene Frank Ozioko, 2018. "Multi-features taxi destination prediction with frequency domain processing," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-22, March.
    9. Zhao, Pengxiang & Kwan, Mei-Po & Qin, Kun, 2017. "Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel," Journal of Transport Geography, Elsevier, vol. 62(C), pages 122-135.
    10. 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.
    11. 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.
    12. Yong, Nuo & Ni, Shunjiang & Shen, Shifei & Ji, Xuewei, 2020. "A study of fluctuations in subway traffic from the control properties of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    13. He, Zhengbing, 2020. "Spatial-temporal fractal of urban agglomeration travel demand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    14. Jiawei Gui & Qunqi Wu, 2020. "Multiple Utility Analyses for Sustainable Public Transport Planning and Management: Evidence from GPS-Equipped Taxi Data in Haikou," Sustainability, MDPI, vol. 12(19), pages 1-46, September.
    15. Tong Zhou & Xintao Liu & Zhen Qian & Haoxuan Chen & Fei Tao, 2019. "Dynamic Update and Monitoring of AOI Entrance via Spatiotemporal Clustering of Drop-Off Points," Sustainability, MDPI, vol. 11(23), pages 1-20, December.
    16. Jincheng Wang & Qunqi Wu & Feng Mao & Yilong Ren & Zilin Chen & Yaqun Gao, 2021. "Influencing Factor Analysis and Demand Forecasting of Intercity Online Car-Hailing Travel," Sustainability, MDPI, vol. 13(13), pages 1-19, July.
    17. Xia, Dong & Zheng, Linjiang & Tang, Yi & Cai, Xiaolin & Chen, Li & Sun, Dihua, 2022. "Dynamic traffic prediction for urban road network with the interpretable model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    18. Li, Zhen-Hua & Zheng, Shi-Teng & Jiang, Rui & Tian, Jun-Fang & Zhu, Kai-Xuan & Di Pace, Roberta, 2022. "Empirical and simulation study on traffic oscillation characteristic using floating car data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    19. Jing Wu & Changlong Ling & Xinzhuo Li, 2019. "Study on the Accessibility and Recreational Development Potential of Lakeside Areas Based on Bike-Sharing Big Data Taking Wuhan City as an Example," Sustainability, MDPI, vol. 12(1), pages 1-20, December.
    20. Mepparambath, Rakhi Manohar & Soh, Yong Sheng & Jayaraman, Vasundhara & Tan, Hong En & Ramli, Muhamad Azfar, 2023. "A novel modelling approach of integrated taxi and transit mode and route choice using city-scale emerging mobility data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0188796. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.