The VLDB Journal

, Volume 23, Issue 3, pp 449–468 | Cite as

Personalized trajectory matching in spatial networks

  • Shuo ShangEmail author
  • Ruogu Ding
  • Kai Zheng
  • Christian S. Jensen
  • Panos Kalnis
  • Xiaofang Zhou
Regular Paper


With the increasing availability of moving-object tracking data, trajectory search and matching is increasingly important. We propose and investigate a novel problem called personalized trajectory matching (PTM). In contrast to conventional trajectory similarity search by spatial distance only, PTM takes into account the significance of each sample point in a query trajectory. A PTM query takes a trajectory with user-specified weights for each sample point in the trajectory as its argument. It returns the trajectory in an argument data set with the highest similarity to the query trajectory. We believe that this type of query may bring significant benefits to users in many popular applications such as route planning, carpooling, friend recommendation, traffic analysis, urban computing, and location-based services in general. PTM query processing faces two challenges: how to prune the search space during the query processing and how to schedule multiple so-called expansion centers effectively. To address these challenges, a novel two-phase search algorithm is proposed that carefully selects a set of expansion centers from the query trajectory and exploits upper and lower bounds to prune the search space in the spatial and temporal domains. An efficiency study reveals that the algorithm explores the minimum search space in both domains. Second, a heuristic search strategy based on priority ranking is developed to schedule the multiple expansion centers, which can further prune the search space and enhance the query efficiency. The performance of the PTM query is studied in extensive experiments based on real and synthetic trajectory data sets.


Personalized trajectory matching Efficiency Optimization Spatial networks Spatiotemporal databases 



This research is partially supported by the Natural Science Foundation of China (Grant No. 61232006), the National 863 High-tech Program (Grant No. 2012AA011001), the Australian Research Council (Grants No. DP110103423 and No. DP120102829), and the European Union (Grant No. FP7-PEOPLE-2010-ITN-264994). The research was performed when C. S. Jensen was with Aarhus University. Part of Shuo Shang’s work was done when he was a research assistant professor in Aalborg University.


  1. 1.
    Agrawal, R., Faloutsos, C., Swami, A.N.: Efficient similarity search in sequence databases. In: FODO, pp. 69–84 (1993)Google Scholar
  2. 2.
    Alt, H., Efrat, A., Rote, G., Wenk, C.: Matching planar maps. In: SODA, pp. 589–598 (2003)Google Scholar
  3. 3.
    Brakatsoulas, S., Pfoser, D., Salas, R., Wenk, C.: On map-matching vehicle tracking data. In: VLDB, pp. 853–864 (2005)Google Scholar
  4. 4.
    Cai, Y., Ng, R.: Indexing spatio-temporal trajectories with Chebyshev polynomials. In: SIGMOD, pp. 599–610 (2004)Google Scholar
  5. 5.
    Chan, K.-P., Fu, A.W.-C.: Efficient time series matching by wavelets. In: ICDE, pp. 126–133 (1999)Google Scholar
  6. 6.
    Chen, L., Ng, R.: On the marriage of lp-norms and edit distance. In: VLDB, pp. 792–803 (2004)Google Scholar
  7. 7.
    Chen, L., Ozsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: SIGMOD, pp. 491–502 (2005)Google Scholar
  8. 8.
    Chen, Z., Shen, H.T., Zhou, X., Zheng, Y., Xie, X.: Searching trajectories by locations: an efficiency study. In: SIGMOD, pp. 255–266 (2010)Google Scholar
  9. 9.
    Dijkstra, E.W.: A note on two problems in connection with graphs. Numerische Math 1, 269–271 (1959)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: SIGMOD, pp. 419–429 (1994)Google Scholar
  11. 11.
    Frentzos, E., Gratsias, K., Pelekis, N., Theodoridis, Y.: Algorithms for nearest neighbor search on moving object trajectories. Geoinformatica 11(2), 159–193 (2007)CrossRefGoogle Scholar
  12. 12.
    Frentzos, E., Gratsias, K., Theodoridis, Y.: Index-based most similar trajectory search. In: ICDE, pp. 816–825 (2007)Google Scholar
  13. 13.
    Gonzalez, H., Han, J., Li, X., Myslinska, M., Sondag, J.: Adaptive fastest path computation on a road network: a traffic mining approach. In VLDB, pp. 794–805 (2007)Google Scholar
  14. 14.
    Greenfeld, J.: Matching GPS observations to locations on a digital map. In: 81th Annual Meeting of the Transportation Research Board (2002)Google Scholar
  15. 15.
    Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: SIGMOD, pp. 47–57 (1984)Google Scholar
  16. 16.
    Jagadish, H.V., Ooi, B.C., Tan, K.-L., Yu, C., Zhang, R.: iDistance: an adaptive B+-tree based indexing method for nearest neighbor search. ACM TODS 30(2), 364–397 (2005)CrossRefGoogle Scholar
  17. 17.
    Keogh, E.: Exact indexing of dynamic time warping. In: VLDB, pp. 406–417 (2002)Google Scholar
  18. 18.
    Lin, B., Su, J.: Shapes based trajectory queries for moving objects. In: ACM, GIS, pp. 21–30 (2005)Google Scholar
  19. 19.
    Liu, K., Deng, K., Ding, Z., Li, M., Zhou, X.: Moir/mt: monitoring large-scale road network traffic in real-time. In: VLDB, pp. 1538–1541 (2009)Google Scholar
  20. 20.
    Liu, K., Li, Y., He, F., Xu, J., Ding, Z.: Effective map-matching on the most simplified road network. In: SIGSPATIAL, GIS, pp. 609–612 (2012)Google Scholar
  21. 21.
    Morse, M.D., Patel, J.M.: An efficient and accurate method for evaluating time series similarity. In: SIGMOD, pp. 569–580 (2007)Google Scholar
  22. 22.
    Shang, S., Ding, R., Yuan, B., Xie, K., Zheng, K., Kalnis, P.: User oriented trajectory search for trip recommendation. In: EDBT, pp. 156–167 (2012)Google Scholar
  23. 23.
    Sherkat, R., Rafiei, D.: On efficiently searching trajectories and archival data for historical similarities. PVLDB 1(1), 896–908 (2008)Google Scholar
  24. 24.
    Tang, L.A., Zheng, Y., Xie, X., Yuan, J., Yu, X., Han, J.: Retrieving k-nearest neighboring trajectories by a set of point locations. In: SSTD, pp. 223–241 (2011)Google Scholar
  25. 25.
    Tiakas, E., Papadopoulos, A., Nanopoulos, A., Manolopoulos, Y., Stojanovic, D., Djordjevic-Kajan, S.: Searching for similar trajectories in spatial networks. J. Syst. Softw. 82(5), 772–788 (2009)Google Scholar
  26. 26.
    Tiakas, E., Papadopoulos, A.N., Nanopoulos, A., Manolopoulos, Y., Stojanovic, D., Djordjevic-Kajan, S.: Trajectory similarity search in spatial networks. In: IDEAS, pp. 185–192 (2006)Google Scholar
  27. 27.
    Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: ICDE, pp. 673–684 (2002)Google Scholar
  28. 28.
    Wenk, C., Salas, R., Pfoser, D.: Addressing the need for map-matching speed: Localizing global curve-matching algorithms. In: SSDBM, pp. 379–388 (2006)Google Scholar
  29. 29.
    Yanagisawa, Y., Akahani, J., Satoh, T.: Shape-based similarity query for trajectory of mobile objects. In: Mobile Data Management, pp. 63–77 (2003) Google Scholar
  30. 30.
    Yi, B.-K., Jagadish, H., Faloutsos, C.: Efficient retrieval of similar time sequences under time warping. In: ICDE, pp. 201–208 (1998)Google Scholar
  31. 31.
    Zarchan, P.: Global positioning system theory and applications. In: Progress in Astronautics and Aeronautics, vol. 163, pp. 1–781. American Institute of Aeronautics and Astronautics (1996)Google Scholar
  32. 32.
    Zheng, Y., Xie, X., Ma, W.-Y.: Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33(2), 32–39 (2010)Google Scholar
  33. 33.
    Zheng, Y., Zhou, X. (eds.): Computing with Spatial Trajectories. Springer, Berlin (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shuo Shang
    • 1
    Email author
  • Ruogu Ding
    • 3
  • Kai Zheng
    • 4
  • Christian S. Jensen
    • 2
  • Panos Kalnis
    • 3
  • Xiaofang Zhou
    • 4
  1. 1.Department of Software EngineeringChina University of Petroleum-BeijingBeijingPeople’s Republic of China
  2. 2.Department of Computer ScienceAalborg UniversityAalborgDenmark
  3. 3.King Abdullah University of Science and TechnologyThuwalSaudi Arabia
  4. 4.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia

Personalised recommendations