On Efficient Map-Matching According to Intersections You Pass By

  • Yaguang Li
  • Chengfei Liu
  • Kuien Liu
  • Jiajie Xu
  • Fengcheng He
  • Zhiming Ding
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8056)


Map-matching is a hot research topic as it is essential for Moving Object Database and Intelligent Transport Systems. However, existing map-matching techniques cannot satisfy the increasing requirement of applications with massive trajectory data, e.g., traffic flow analysis and route planning. To handle this problem, we propose an efficient map-matching algorithm called Passby. Instead of matching every single GPS point, we concentrate on those close to intersections and avoid the computation of map-matching on intermediate GPS points. Meanwhile, this efficient method also increases the uncertainty for determining the real route of the moving object due to less availability of trajectory information. To provide accurate matching results in ambiguous situations, e.g., road intersections and parallel paths, we further propose Passby*. It is based on the multi-hypothesis technique and manages to maintain a small but complete set of possible solutions and eventually choose the one with the highest probability. The results of experiments performed on real datasets demonstrate that Passby* is efficient while maintaining the high accuracy.


Efficient Map-matching Multi-Hypothesis Technique 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Liu, K., Deng, K., Ding, Z., Li, M., Zhou, X.: Moir/mt: Monitoring large-scale road network traffic in real-time. PVLDB 2(2), 1538–1541 (2009)Google Scholar
  2. 2.
    Gonzalez, H., Han, J., Li, X., Myslinska, M., Sondag, J.P.: Adaptive fastest path computation on a road network: a traffic mining approach. In: VLDB, pp. 794–805 (2007)Google Scholar
  3. 3.
    Li, X., Han, J., Lee, J.-G., Gonzalez, H.: Traffic density-based discovery of hot routes in road networks. In: Papadias, D., Zhang, D., Kollios, G. (eds.) SSTD 2007. LNCS, vol. 4605, pp. 441–459. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    White, C.E., Bernstein, D., Kornhauser, A.L.: Some map matching algorithms for personal navigation assistants. Transportation Research Part C: Emerging Technologies 8(1-6), 91–108 (2000)CrossRefGoogle Scholar
  5. 5.
    Greenfeld, J.S.: Matching gps observations to locations on a digital map. In: Transportation Research Board. Meeting, Washington, D.C (2002)Google Scholar
  6. 6.
    Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., Huang, Y.: Map-matching for low-sampling-rate gps trajectories. In: GIS, Seattle, Washington, pp. 352–361 (2009)Google Scholar
  7. 7.
    Newson, P., Krumm, J.: Hidden markov map matching through noise and sparseness. In: GIS, Seattle, WA, USA, pp. 336–343 (2009)Google Scholar
  8. 8.
    Zheng, K., Zheng, Y., Xie, X., Zhou, X.: Reducing uncertainty of low-sampling-rate trajectories. In: ICDE, Washington, DC, USA, pp. 1144–1155 (2012)Google Scholar
  9. 9.
    Pink, O., Hummel, B.: A statistical approach to map matching using road network geometry, topology and vehicular motion constraints. In: ITSC, pp. 862–867. IEEE (2008)Google Scholar
  10. 10.
    Wenk, C., Salas, R., Pfoser, D.: Addressing the need for map-matching speed: Localizing globalb curve-matching algorithms. In: SSDBM, Washington, DC, USA, pp. 379–388 (2006)Google Scholar
  11. 11.
    Brakatsoulas, S., Pfoser, D., Salas, R., Wenk, C.: On map-matching vehicle tracking data. In: VLDB, Trondheim, Norway, pp. 853–864 (2005)Google Scholar
  12. 12.
    Quddus, M.A., Ochieng, W.Y., Noland, R.B.: Current map-matching algorithms for transport applications: State-of-the art and future research directions. Transportation Research Part C: Emerging Technologies 15(5), 312–328 (2007)CrossRefGoogle Scholar
  13. 13.
    Syed, S., Cannon, M.: Fuzzy logic based-map matching algorithm for vehicle navigation system in urban canyons. In: National Technical Meeting of The Institute of Navigation, San Diego, CA, pp. 982–993 (2004)Google Scholar
  14. 14.
    Reid, D.: An algorithm for tracking multiple targets. IEEE Transactions on Automatic Control 24(6), 843–854 (1979)CrossRefGoogle Scholar
  15. 15.
    Pyo, J.S., Shin, D.H., Sung, T.K.: Development of a map matching method using the multiple hypothesis technique. In: Proceedings of 2001 Intelligent Transportation Systems, pp. 23–27. IEEE, Oakland (2001)Google Scholar
  16. 16.
    Abdallah, F., Nassreddine, G., Denoeux, T.: A multiple-hypothesis map-matching method suitable for weighted and box-shaped state estimation for localization. IEEE Transactions on Intelligent Transportation Systems 12(4), 1495–1510 (2011)CrossRefGoogle Scholar
  17. 17.
    Liu, K., Li, Y., He, F., Xu, J., Ding, Z.: Effective map-matching on the most simplified road network. In: GIS, Redondo Beach, CA, USA, pp. 609–612 (2012)Google Scholar
  18. 18.
    Zhou, J., Golledge, R.: A three-step general map matching method in the gis environment: travel/transportation study perspective. International Journal of Geographical Information System 8(3), 243–260 (2006)Google Scholar
  19. 19.
    Alt, H., Guibas, L.: Discrete geometric shapes: Matching, interpolation, and approximation. In: Handbook of Computational Geometry, Amsterdam, pp. 121–153 (1999)Google Scholar
  20. 20.
    ACM SIGSPATIAL Cup 2012: Training data sets (2012),

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yaguang Li
    • 1
    • 3
  • Chengfei Liu
    • 2
  • Kuien Liu
    • 1
  • Jiajie Xu
    • 1
  • Fengcheng He
    • 1
    • 3
  • Zhiming Ding
    • 1
  1. 1.Institute of SoftwareChinese Academy of SciencesBeijingChina
  2. 2.FICTSwinburne University of TechnologyAustralia
  3. 3.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations