Skip to main content
SpringerLink
Log in

Online Learning for Accurate Real-Time Map Matching

  • Conference paper
  • First Online:
Advances in Knowledge Discovery and Data Mining (PAKDD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9652))

Included in the following conference series:

Abstract

For the reason that deviation exists between GPS traces obtained by real-time positioning system and actual paths, real-time map matching which identifies the correct traveling road segment, becomes increasingly important. In order to effectively improve map matching accuracy, most state-of-art real-time map matching algorithms use machine learning which calls for time-consuming human labeling in advance. We propose an accurate real-time map matching method using online learning called OLMM. It takes into account a small piece of trajectory data and their matching result to support the subsequent matching process. We evaluate the effectiveness of the proposed approach using ground truth data. The results demonstrate that our approach can obtain more accurate matching results than existing methods without any human labeling beforehand.

This research is supported by the Natural Science Foundation of China (Grant No. 61572043, 61300003).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Li, Z., Zhu, Y., Zhu, H., et al.: Compressive sensing approach to urban traffic sensing. In: 2011 31st International Conference on IEEE Distributed Computing Systems (ICDCS), pp. 889–898 (2011)

    Google Scholar 

  2. Li, M., Zhang, Y., Wang, W.: Analysis of congestion points based on probe car data. In: 12th International IEEE Conference on Intelligent Transportation Systems, ITS C 2009, pp. 1–5. IEEE (2009)

    Google Scholar 

  3. De Fabritiis, C., Ragona, R., Valenti, G.: Traffic estimation and prediction based on real time floating car data. In: 11th International IEEE Conference on Intelligent Transportation Systems, ITSC 2008, pp. 197–203. IEEE (2008)

    Google Scholar 

  4. Goh, C.Y., Dauwels, J., Mitrovic, N., et al.: Online map matching based on Hidden Markov Model for real-time traffic sensing applications. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems (ITSC). IEEE, pp. 776–781 (2012)

    Google Scholar 

  5. Newson, P., Krumm, J.: Hidden Markov map matching through noise and sparseness. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 336–343. ACM (2009)

    Google Scholar 

  6. Quddus, M.A., Ochieng, W.Y., Noland, R.B.: Current map-matching algorithms for transport applications: state-of-the art and future research directions. Transp. Res. Part C Emerg. Technol. 15(5), 312–328 (2007)

    Article  Google Scholar 

  7. Ferenc, K., Derek, B.: An introduction to map matching for personal navigation assistants. Geom. Distrib. 122(7), 1082–1083 (1996)

    Google Scholar 

  8. White, C.E., Bernstein, D., Kornhauser, A.L.: Some map matching algorithms for personal navigation assistants. Transp. Res. Part C Emerg. Technol. 8(1), 91–108 (2000)

    Article  Google Scholar 

  9. Greenfeld, J.S.: Matching GPS observations to locations on a digital map. In: Proceedings of Annual Meeting of the Transportation Research Board, Washington D.C., 2002

    Google Scholar 

  10. Quddus, M.A., Ochieng, W.Y., Zhao, L., et al.: A general map matching algorithm for transport telematics applications. GPS Solutions 7(3), 157–167 (2003)

    Article  Google Scholar 

  11. Newby, R.M., Annett, D.A., Berry, M.J., et al.: Vehicle location and navigation system (2013). EP, EP1864084

    Google Scholar 

  12. Ochieng, W.Y., Quddus, M.A., Noland, R.B.: Map-matching in complex urban road networks. Rev. Bras. De Cartografia 2, 1–14 (2003)

    Google Scholar 

  13. Szwed, P., Pekala, K.: An incremental map-matching algorithm based on Hidden Markov Model. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part II. LNCS, vol. 8468, pp. 579–590. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  14. El Najjar, M.E., Bonnifait, P.: A road-matching method for precise vehicle localization using belief theory and Kalman filtering. Auton. Robots 19(2), 173–191 (2005)

    Article  Google Scholar 

  15. Nassreddine, G., Abdallah, F., Denoeux, T.: Map matching algorithm using interval analysis and Dempster-Shafer theory. In: 2009 IEEE Intelligent Vehicles Symposium, pp. 494–499 (2009)

    Google Scholar 

  16. Krumm, J., Letchner, J., Horvitz, E.: Map matching with travel time constraints. In: SAE World Congress (2007)

    Google Scholar 

  17. Thiagarajan, A., Ravindranath, L., Lacurts, K., et al.: VTrack: accurate, energy-aware road traffic delay estimation using mobile phones. Sensys Proc. ACM Conf. Embed. Netw. Sens. Syst. 5554(1), 85–98 (2009)

    Article  Google Scholar 

  18. Najjar, M.E.E., Bonnifait, P.: A road-matching method for precise vehicle localization using belief theory and Kalman filtering. Auton. Robots 19(2), 173–191 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronics and Computer Engineering (ECE), Peking University, Shenzhen, 518055, China

    Biwei Liang, Tengjiao Wang & Kai Lei

  2. School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Tengjiao Wang, Wei Chen & Hongyan Li

  3. Key Laboratory of Machine Perception, Peking University, Ministry of Education, Beijing, 100871, China

    Hongyan Li

  4. Key Laboratory of High Confidence Software Technologies, Peking University, Ministry of Education, Beijing, 100871, China

    Biwei Liang, Tengjiao Wang & Wei Chen

  5. School of Information Science and Technology, University of International Relations, Beijing, 100871, China

    Shun Li

Authors
  1. Biwei Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tengjiao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hongyan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kai Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tengjiao Wang .

Editor information

Editors and Affiliations

  1. The University of Melbourne, Melbourne, Victoria, Australia

    James Bailey

  2. The University of Texas at Dallas, Richardson, Texas, USA

    Latifur Khan

  3. Osaka University, Osaka, Japan

    Takashi Washio

  4. University of Auckland, Auckland, New Zealand

    Gill Dobbie

  5. Shenzhen University, Shenzhen, China

    Joshua Zhexue Huang

  6. Massey University, Auckland, New Zealand

    Ruili Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Liang, B., Wang, T., Li, S., Chen, W., Li, H., Lei, K. (2016). Online Learning for Accurate Real-Time Map Matching. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J., Wang, R. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9652. Springer, Cham. https://doi.org/10.1007/978-3-319-31750-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-31750-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31749-6

  • Online ISBN: 978-3-319-31750-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation