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A Map Matching Algorithm Combining Twice Gridding and Weighting Factors Methods

  • Ketu Cao
  • Lixiao WangEmail author
  • Zhi Zuo
  • Xiaohui Sun
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 127)

Abstract

Current map matching algorithms suffer the problem that matching accuracy and matching efficiency cannot be achieved both in the face of massive floating car data. A novel map matching algorithm combining twice gridding and weighting factors methods has been proposed in this study, selection of candidate road segments and determination of the shortest path are based on twice gridding method; Factors of driving direction and trajectory angle are served as improvement of weighting factors method, and matching accuracy rate in specific situations such as parallel road segments, intersection areas, intensive road segments and large positioning errors has been improved. Meanwhile, to avoid driving direction and trajectory angle failing at low speeds, which results in interference problems with map matching, the weights of driving directions and trajectory angle are dynamically adjusted by instantaneous speed and interval length between the trajectories. Through map matching case study of actual data, results show that the improved weighting factors method in this study performs outstandingly in improving the matching accuracy rate, and combining the method of twice gridding effectively improves matching efficiency in principle, which makes the map matching algorithm in this study can balance the matching accuracy and matching efficiency well when facing massive floating car data.

Keywords

Map matching Weighting factors Twice gridding Floating car data 

References

  1. 1.
    Hashemi, M.: A critical review of real-time map-matching algorithms: current issues and future directions. Comput. Environ. Urban Syst. 48, 153–165 (2014)CrossRefGoogle Scholar
  2. 2.
    Kim, J.: Node based map matching algorithm for car navigation system. In: International Symposium on Automotive Technology & Automation, Florence. Global deployment of advanced transportation telematics/ITS (1996)Google Scholar
  3. 3.
    Bernstein, D.: An introduction to map matching for personal navigation assistants. Geom. Distrib. 122(7), 1082–1083 (1998)Google Scholar
  4. 4.
    White, C.E., Bernstein, D., Kornhauser, A.L.: Some map matching algorithms for personal navigation assistants. Transp. Res. Part C 8(1), 91–108 (2000)CrossRefGoogle Scholar
  5. 5.
    Taylor, G.: Road reduction filtering for GPS-GIS navigation. Trans. GIS 5(3), 193–207 (2001)CrossRefGoogle Scholar
  6. 6.
    Morikawa, T., Miwa, T.: Preliminary analysis on dynamic route choice behavior: using probe-vehicle data. J. Adv. Transp. 40(2), 140–163 (2006)CrossRefGoogle Scholar
  7. 7.
    Greenfeld, J.S.: Matching GPS observations to locations on a digital map. In: Transportation Research Board 81st Annual Meeting (2002)Google Scholar
  8. 8.
    Quddus, M.A.: A general map matching algorithm for transport telematics applications. GPS Solutions 7(3), 157–167 (2003)CrossRefGoogle Scholar
  9. 9.
    Syed, S., Cannon, M.E.: Fuzzy logic based-map matching algorithm for vehicle navigation system in urban canyons. In: Ion National Technical Meeting (2004)Google Scholar
  10. 10.
    Ren, M.: A fuzzy logic map matching for wheelchair navigation. GPS Solutions 16(3), 273–282 (2012)CrossRefGoogle Scholar
  11. 11.
    Ren, M., Karimi, H.A.: A hidden Markov model-based map-matching algorithm for wheelchair navigation. J. Navig. 62(3), 383–395 (2009)CrossRefGoogle Scholar
  12. 12.
    Wu, D.: Research on the Computational Method based on Markov chain for Road Weight of Traffic Network. Jilin University (2011)Google Scholar
  13. 13.
    Cao, P.: Research on map matching algorithm for large scale probe vehicle data. Tsinghua University (2011)Google Scholar
  14. 14.
    Zhao, S., Zhang, J., Qu, R.: An improved map matching algorithm for floating car. Bull. Surv. Mapp. 01, 97–102 (2018)Google Scholar
  15. 15.
    Ochieng, W.Y., Quddus, M., Noland, R.B.: Map-matching in complex urban road networks. Revista Brasileira De Cartografia 55(2), 1–14 (2003)Google Scholar
  16. 16.
    Fan, N., Yu, Q.-q., Kang, J.: Map matching algorithm for urban road network based on dynamic weight. Meas. Control Technol. (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Civil Engineering and ArchitectureXinjiang UniversityUrumqiChina

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