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A survey of trajectory distance measures and performance evaluation

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Abstract

The proliferation of trajectory data in various application domains has inspired tremendous research efforts to analyze large-scale trajectory data from a variety of aspects. A fundamental ingredient of these trajectory analysis tasks and applications is distance measures for effectively determining how similar two trajectories are. We conduct a comprehensive survey of the trajectory distance measures. The trajectory distance measures are classified into four categories according to the trajectory data type and whether the temporal information is measured. In addition, the effectiveness and complexity of each distance measure are studied. The experimental study is also conducted on their effectiveness in the six different trajectory transformations.

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References

  1. Abid, A., Zou, J.Y.: Learning a warping distance from unlabeled time series using sequence autoencoders. In: Advances in Neural Information Processing Systems, pp. 10568–10578 (2018)

  2. Abul, O., Bonchi, F., Nanni, M.: Never walk alone: Uncertainty for anonymity in moving objects databases. In: IEEE 24th International Conference on Data Engineering. ICDE 2008, pp. 376–385. IEEE (2008)

  3. Andrienko, G., Andrienko, N., Rinzivillo, S., Nanni, M., Pedreschi, D., Giannotti, F.: Interactive visual clustering of large collections of trajectories. In: IEEE Symposium on Visual Analytics Science and Technology. VAST 2009, pp. 3–10. IEEE (2009)

  4. Andrienko, G., Andrienko, N., Wrobel, S.: Visual analytics tools for analysis of movement data. ACM SIGKDD Explor. Newsl. 9(2), 38–46 (2007)

    Google Scholar 

  5. Beck, M., Robins, S.: Computing the Continuous Discretely. Springer, Berlin (2007)

    MATH  Google Scholar 

  6. Bourrier, F., Dorren, L., Nicot, F., Berger, F., Darve, F.: Toward objective rockfall trajectory simulation using a stochastic impact model. Geomorphology 110(3–4), 68–79 (2009)

    Google Scholar 

  7. Cai, Y., Ng, R.: Indexing spatio-temporal trajectories with Chebyshev polynomials. In: SIGMOD, pp. 99–610 (2004)

  8. Cao, W., Wu, Z., Wang, D., Li, J., Wu, H.: Automatic user identification method across heterogeneous mobility data sources. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 978–989. IEEE (2016)

  9. Chakka, V., Everspaugh, A., Patel, J.: Indexing large trajectory data sets with SETI. In: CIDR (2003)

  10. Chan, F.K.-P., Fu, A.W.-C., Yu, C.: Haar wavelets for efficient similarity search of time-series: with and without time warping. IEEE Trans. Knowl. Data Eng. 3, 686–705 (2003)

    Google Scholar 

  11. Chan, T.M.: A simple trapezoid sweep algorithm for reporting red/blue segment intersections. In: CCCG, pp. 263–268 (1994)

  12. Chang, J.-W., Bista, R., Kim, Y.-C., Kim, Y.-K.: Spatio-temporal similarity measure algorithm for moving objects on spatial networks. In: International Conference on Computational Science and Its Applications, pp. 1165–1178. Springer (2007)

  13. Chazelle, B., Edelsbrunner, H.: An optimal algorithm for intersecting line segments in the plane. J. ACM 39(1), 1–54 (1992)

    MathSciNet  MATH  Google Scholar 

  14. Chen, L., Ng, R.: On the marriage of lp-norms and edit distance. In: PVLDB, pp. 792–803 (2004)

  15. Chen, L., Özsu, M., Oria, V.: Robust and fast similarity search for moving object trajectories. In: SIGMOD, pp. 491–502 (2005)

  16. Chen, Q., Chen, L., Lian, X., Liu, Y., Yu, J.X.: Indexable PLA for efficient similarity search. In: Proceedings of the 33rd International Conference on Very large Data Bases, pp. 435–446. VLDB Endowment (2007)

  17. Chow, C.-Y., Mokbel, M.F., Aref, W.G.: Casper*: query processing for location services without compromising privacy. ACM Trans. Database Syst. (TODS) 34(4), 24 (2009)

    Google Scholar 

  18. Clarke, F.: Optimal solutions to differential inclusions. J. Optim. Theory Appl. 19(3), 469–478 (1976)

    MathSciNet  MATH  Google Scholar 

  19. Cudre-Mauroux, P., Wu, E., Madden, S.: Trajstore: an adaptive storage system for very large trajectory data sets. In: ICDE, pp. 109–120 (2010)

  20. D’Auria, M., Nanni, M., Pedreschi, D.: Time-focused density-based clustering of trajectories of moving objects. In: Proceedings of the Workshop on Mining Spatio-Temporal Data (MSTD-2005), Porto (2005)

  21. Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: experimental comparison of representations and distance measures. Proc. VLDB Endow. 1(2), 1542–1552 (2008)

    Google Scholar 

  22. Dodge, S., Laube, P., Weibel, R.: Movement similarity assessment using symbolic representation of trajectories. Int. J. Geogr. Inf. Sci. 26(9), 1563–1588 (2012)

    Google Scholar 

  23. Eiter, T., Mannila, H.: Computing discrete fréchet distance. Technical report, Citeseer (1994)

  24. Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast Subsequence Matching in Time-Series Databases, vol. 23. ACM, New York (1994)

    Google Scholar 

  25. Feng, Z., Zhu, Y.: A survey on trajectory data mining: techniques and applications. IEEE Access 4, 2056–2067 (2016)

    Google Scholar 

  26. Flesch, T., Wilson, J.: A two-dimensional trajectory-simulation model for non-Gaussian, inhomogeneous turbulence within plant canopies. Bound. Layer Meteorol. 61(4), 349–374 (1992)

    Google Scholar 

  27. Frentzos, E., Gratsias, K., Pelekis, N., Theodoridis, Y.: Nearest neighbor search on moving object trajectories. In: SSTD, pp. 328–345 (2005)

  28. Frentzos, E., Gratsias, K., Theodoridis, Y.: Index-based most similar trajectory search. In: IEEE 23rd International Conference on Data Engineering, 2007. ICDE 2007, pp. 816–825. IEEE (2007)

  29. Gan, G., Ma, C., Wu, J.: Data Clustering: Theory, Algorithms, and Applications, vol. 20. SIAM, Philadelphia (2007)

    MATH  Google Scholar 

  30. Gasmelseed, A., Mahmood, N.: Study of hand preferences on signature for right-handed and left-handed peoples. Int. J. Adv. Eng. Technol. 1(5), 41–46 (1963)

    Google Scholar 

  31. Gauthier, T.D.: Detecting trends using Spearman’s rank correlation coefficient. Environ. Forensics 2(4), 359–362 (2001)

    Google Scholar 

  32. Geurts, P.: Pattern extraction for time series classification. In: European Conference on Principles of Data Mining and Knowledge Discovery, pp. 115–127. Springer, Berlin (2001)

    Google Scholar 

  33. Gudmundsson, J., Laube, P., Wolle, T.: Computational movement analysis. In: Kresse, W., Danko, D.M. (eds.) Springer handbook of geographic information, pp. 423–438. Springer, Berlin (2011)

    Google Scholar 

  34. Guo, N., Ma, M., Xiong, W., Chen, L., Jing, N.: An efficient query algorithm for trajectory similarity based on Fréchet distance threshold. ISPRS Int. J. Geo-Inf. 6(11), 326 (2017)

    Google Scholar 

  35. Guttman, A.: R-trees: A Dynamic Index Structure for Spatial Searching, vol. 14. ACM, Philadelphia (1984)

    Google Scholar 

  36. Heckbert, P., Garland, M.: Survey of polygonal surface simplification algorithms. Technical report, DTIC Document (1997)

  37. Huang, Z., Shen, H.T., Shao, J., Cui, B., Zhou, X.: Practical online near-duplicate subsequence detection for continuous video streams. IEEE Trans. Multimed. 12(5), 386–398 (2010)

    Google Scholar 

  38. Ichiye, T., Karplus, M.: Collective motions in proteins: a covariance analysis of atomic fluctuations in molecular dynamics and normal mode simulations. Proteins Struct. Funct. Bioinform. 11(3), 205–217 (1991)

    Google Scholar 

  39. Ismail, A., Vigneron, A.: A new trajectory similarity measure for GPS data. In: Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming, pp. 19–22. ACM (2015)

  40. Jeung, H., Shen, H., Zhou, X.: Convoy queries in spatio-temporal databases. In: ICDE, pp. 1457–1459 (2008)

  41. Jeung, H., Yiu, M., Zhou, X., Jensen, C., Shen, H.: Discovery of convoys in trajectory databases. In: PVLDB, vol. 1, pp. 1068–1080. VLDB Endowment (2008)

    Google Scholar 

  42. Jonker, R., Leve, G.D., Velde, J.A.V.D., Volgenant, A.: Technical note–rounding symmetric traveling salesman problems with an asymmetric assignment problem. Oper. Res. 28(3-part-i), 623–627 (1980)

    MATH  Google Scholar 

  43. Kahveci, T., Singh, A., Gurel, A.: Similarity searching for multi-attribute sequences. In: 14th International Conference on Scientific and Statistical Database Management. Proceedings, pp. 175–184. IEEE (2002)

  44. Kearney, J., Hansen, S.: Stream editing for animation. Technical report, DTIC Document (1990)

  45. Keogh, E., Pazzani, M.: Scaling up dynamic time warping for datamining applications. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 285–289. ACM (2000)

  46. Keogh, E., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7(3), 358–386 (2005)

    Google Scholar 

  47. Kim, Y., Kim, D., Kim, T., Sung, J., Yoo, S.: Target classification in sparse sampling acoustic sensor networks using iddc algorithm. In: International Conference on Embedded and Ubiquitous Computing, pp. 568–578. Springer, Berlin (2007)

  48. Kruskal, J.B.: An overview of sequence comparison: time warps, string edits, and macromolecules. SIAM Rev. 25(2), 201–237 (1983)

    MathSciNet  MATH  Google Scholar 

  49. Lee, J.-G., Han, J., Whang, K.-Y.: Trajectory clustering: a partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 593–604. ACM (2007)

  50. Li, H., Liu, J., Wu, K., Yang, Z., Liu, R.W., Xiong, N.: Spatio-temporal vessel trajectory clustering based on data mapping and density. IEEE Access 6, 58939–58954 (2018)

    Google Scholar 

  51. Li, L., Li, X., Yang, Y., Dong, J.: Indoor tracking trajectory data similarity analysis with a deep convolutional autoencoder. Sustain. Cities Soc. 45, 588–595 (2018)

    Google Scholar 

  52. Li, Z., Ding, B., Han, J., Kays, R.: Swarm: mining relaxed temporal moving object clusters. PVLDB 3, 723–734 (2010)

    Google Scholar 

  53. Lin, B., Su, J.: Shapes based trajectory queries for moving objects. In: Proceedings of the 13th Annual ACM International Workshop on Geographic Information Systems, pp. 21–30. ACM (2005)

  54. Liu, H., Jin, C., Zhou, A.: Popular route planning with travel cost estimation. In: International Conference on Database Systems for Advanced Applications, pp. 403–418. Springer, Berlin (2016)

    Google Scholar 

  55. Morse, M.D., Jignesh, Patel, M.: Efficient algorithms for similarity and skyline summary, Doctoral Committee on Multidimensional datasets. University of Michigan (2007)

  56. Müller, M.: Dynamic time warping. In: Müller, M. (ed.) Information Retrieval for Music and Motion, pp. 69–84. Springer, Berlin (2007)

    Google Scholar 

  57. Myers, C., Rabiner, L., Rosenberg, A.: Performance tradeoffs in dynamic time warping algorithms for isolated word recognition. IEEE Trans. Acous. Speech Signal Process. 28(6), 623–635 (1980)

    MATH  Google Scholar 

  58. Nascimento, M.A., Silva, J.R.: Towards historical R-trees. In: Proceedings of the 1998 ACM Symposium on Applied Computing, pp. 235–240. ACM (1998)

  59. Ni, J., Ravishankar, C.: Indexing spatio-temporal trajectories with efficient polynomial approximations. TKDE 19(5), 663–678 (2007)

    Google Scholar 

  60. OpenStreetMap. http://www.openstreetmap.org/. Accessed 30 Dec 2018

  61. Ostendorf, M., Roukos, S.: A stochastic segment model for phoneme-based continuous speech recognition. IEEE Trans. Acoust. Speech Signal Process. 37(12), 1857–1869 (1989)

    Google Scholar 

  62. Panagiotakis, C., Pelekis, N., Kopanakis, I.: Trajectory voting and classification based on spatiotemporal similarity in moving object databases. In: International Symposium on Intelligent Data Analysis, pp. 131–142. Springer, Berlin (2009)

    Google Scholar 

  63. Patel, P., Keogh, E., Lin, J., Lonardi, S.: Mining motifs in massive time series databases. In: Proceedings. 2002 IEEE International Conference on Data Mining. ICDM 2003, pp. 370–377. IEEE (2002)

  64. Pelekis, N., Kopanakis, I., Marketos, G., Ntoutsi, I., Andrienko, G., Theodoridis, Y.: Similarity search in trajectory databases. In: 14th International Symposium on Temporal Representation and Reasoning, pp. 129–140. IEEE (2007)

  65. Pelekis, N., Kopanakis, I., Marketos, G., Ntoutsi, I., Andrienko, G., Theodoridis, Y.: Similarity search in trajectory databases. In: Proceedings of the 14th International Symposium on Temporal Representation and Reasoning, TIME ’07, pp. 129–140, Washington, DC, USA, 2007. IEEE Computer Society (2007)

  66. Pesara, A.C., Patil, V., Atrey, P.K.: Secure computing of gps trajectory similarity: a review. In: Proceedings of the 2nd ACM SIGSPATIAL Workshop on Recommendations for Location-Based Services and Social Networks, p. 3. ACM (2018)

  67. Pfeifer, P.E., Deutrch, S.J.: A three-stage iterative procedure for space-time modeling. Technometrics 22(1), 35–47 (1980)

    Google Scholar 

  68. Pfoser, D., Jensen, C.S., Theodoridis, Y., et al.: Novel approaches to the indexing of moving object trajectories. In: VLDB, pp. 395–406 (2000)

  69. Picton, T., Hunt, M., Mowrey, R., Rodriguez, R., Maru, J.: Evaluation of brain-stem auditory evoked potentials using dynamic time warping. Electroencephalogr. Clin. Neurophysiol. Evoked Potentials Sect. 71(3), 212–225 (1988)

    Google Scholar 

  70. Priestley, M.: State-dependent models: a general approach to non-linear time series analysis. J. Time Series Anal. 1(1), 47–71 (1980)

    MathSciNet  MATH  Google Scholar 

  71. Ranu, S., Deepak, P., Telang, A.D., Deshpande, P., Raghavan, S.: Indexing and matching trajectories under inconsistent sampling rates. In: 2015 IEEE 31st International Conference on Data Engineering (ICDE), pp. 999–1010. IEEE (2015)

  72. Richalet, J., Rault, A., Testud, J., Papon, J.: Model predictive heuristic control: applications to industrial processes. Automatica 14(5), 413–428 (1978)

    Google Scholar 

  73. Rinzivillo, S., Pedreschi, D., Nanni, M., Giannotti, F., Andrienko, N., Andrienko, G.: Visually driven analysis of movement data by progressive clustering. Inf. Vis. 7(3–4), 225–239 (2008)

    Google Scholar 

  74. Robinson, M.: The temporal development of collision cascades in the binary-collision approximation. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. Atoms 48(1–4), 408–413 (1990)

    Google Scholar 

  75. Sakurai, Y., Yoshikawa, M., Faloutsos, C.: FTW: fast similarity search under the time warping distance. In: Proceedings of the Twenty-Fourth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 326–337. ACM (2005)

  76. Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)

    Google Scholar 

  77. Sanderson, A., Wong, A.: Pattern trajectory analysis of nonstationary multivariate data. IEEE Trans. Syst. Man Cybern. 10(7), 384–392 (1980)

    Google Scholar 

  78. Shahin, M.K., Tharwat, A., Gaber, T., Hassanien, A.E.: A wheelchair control system using human-machine interaction: single-modal and multimodal approaches. J. Intell. Syst. 28(1), 115–132 (2017)

    Google Scholar 

  79. Shang, S., Chen, L., Wei, Z., Jensen, C.S., Zheng, K., Kalnis, P.: Trajectory similarity join in spatial networks. Proc. VLDB Endow. 10(11), 1178–1189 (2017)

    Google Scholar 

  80. Shang, S., Ding, R., Zheng, K., Jensen, C.S., Kalnis, P., Zhou, X.: Personalized trajectory matching in spatial networks. VLDB J. Int. J. Very Large Data Bases 23(3), 449–468 (2014)

    Google Scholar 

  81. Soong, F., Rosenberg, A.: On the use of instantaneous and transitional spectral information in speaker recognition. IEEE Trans. Acoust. Speech Signal Proc. 36(6), 871–879 (1988)

    MATH  Google Scholar 

  82. Su, H., Zheng, K., Huang, J., Wang, H., Zhou, X.: Calibrating trajectory data for spatio-temporal similarity analysis. VLDB J. 24(1), 93–116 (2015)

    Google Scholar 

  83. Su, H., Zheng, K., Wang, H., Huang, J., Zhou, X.: Calibrating trajectory data for similarity-based analysis. In: SIGMOD, pp. 833–844. ACM (2013)

  84. Ta, N., Li, G., Xie, Y., Li, C., Hao, S., Feng, J.: Signature-based trajectory similarity join. IEEE Trans. Knowl. Data Eng. 29(4), 870–883 (2017)

    Google Scholar 

  85. Takens, F.: Motion under the influence of a strong constraining force. In: Nitecki, Z., Robinson, C. (eds.) Global Theory of Dynamical Systems, pp. 425–445. Springer, Berlin (1980)

    Google Scholar 

  86. Tao, Y., Papadias, D.: Efficient historical R-trees. In: Proceedings of Thirteenth International Conference on Scientific and Statistical Database Management. SSDBM 2001, pp. 223–232. IEEE (2001)

  87. Tao, Y., Papadias, D.: MV3R-Tree: a spatio-temporal access method for timestamp and interval queries. VLDB 1, 431–440 (2001)

    Google Scholar 

  88. Toohey, K., Duckham, M.: Trajectory similarity measures. Sigspatial Spec. 7(1), 43–50 (2015)

    Google Scholar 

  89. Vlachos, M., Gunopulos, D., Kollios, G.: Robust similarity measures for mobile object trajectories. In: 13th International Workshop on Database and Expert Systems Applications. Proceedings, pp. 721–726. IEEE (2002)

  90. Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: 18th International Conference on Data Engineering. Proceedings, pp. 673–684. IEEE (2002)

  91. Wang, H., Zheng, K., Xu, J., Zheng, B., Zhou, X., Sadiq, S.: Sharkdb: an in-memory column-oriented trajectory storage. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 1409–1418. ACM (2014)

  92. Ward, A.: A generalization of the Frechet distance of two curves. Proc. Natl. Acad. Sci. U. S. A. 40(7), 598 (1954)

    MathSciNet  MATH  Google Scholar 

  93. Xie, D., Li, F., Phillips, J.M.: Distributed trajectory similarity search. Proc. VLDB Endow. 10(11), 1478–1489 (2017)

    Google Scholar 

  94. Xu, X., Han, J., Lu, W.: RT-tree: an improved R-tree indexing structure for temporal spatial databases. In: The International Symposium on Spatial Data Handling (SDH), pp. 1040–1049. Zurich (1990)

  95. Yamaguchi, S., Saito, Y., Anami, S., Michizono, S.: Trajectory simulation of multipactoring electrons in an S-band pillbox RF window. IEEE Trans. Nucl. Sci. 39(2), 278–282 (1992)

    Google Scholar 

  96. Yi, B., Jagadish, H., Faloutsos, C.: Efficient retrieval of similar time sequences under time warping. In: 14th International Conference on Data Engineering. Proceedings, pp. 201–208. IEEE (1998)

  97. Zhang, X., Han, K.-L.: High-order symplectic integration in quasi-classical trajectory simulation: case study for O(\(^1\)D) + H\(_2\). Int. J. Quantum Chem. 106(8), 1815–1819 (2006)

    Google Scholar 

  98. Zheng, K., Su, H., Bolong, Z., Shuo, S., Xu, J., Liu, J., Zhou, X.: Interactive top-k spatial keyword queries. In: IEEE International Conference on Data Engineering (2015)

  99. Zheng, K., Zhao, Y., Lian, D., Zheng, B., Liu, G., Zhou, X.: Reference-based framework for spatio-temporal trajectory compression and query processing. IEEE Trans. Knowl. Data Eng. (2019). https://doi.org/10.1109/TKDE.2019.2914449

    Article  Google Scholar 

  100. Zheng, K., Zheng, Y., Yuan, N.J., Shang, S., Zhou, X.: Online discovery of gathering patterns over trajectories. IEEE Trans. Knowl. Data Eng. 26(8), 1974–1988 (2013)

    Google Scholar 

  101. Zheng, Y., Zhang, L., Xie, X., Ma, W.: Mining interesting locations and travel sequences from GPS trajectories. In: WWW, pp. 791–800. ACM (2009)

  102. Zuo, X., Jin, X.: General hierarchical model (GHM) to measure similarity of time series. ACM SIGMOD Rec. 36(1), 13–18 (2007)

    Google Scholar 

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Acknowledgements

This research is supported by the NSFC (Grant Nos. 618020 54, 61972069, 61836007, 61832017, 61532018, 61902134), the Central Universities (UESTC: Grants No. ZYGX2016K YQD135, HUST: Grants Nos. 2019kfyXKJC021, 2019kfyX JJS091), and Dongguan Innovative Research Team Program (No. 2018607201008).

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  1. University of Electronic Science and Technology of China, Chengdu, China

    Han Su, Shuncheng Liu & Kai Zheng

  2. Huazhong University of Science and Technology, Wuhan, China

    Bolong Zheng

  3. University of Queensland, Brisbane, Australia

    Xiaofang Zhou

  4. Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan, China

    Xiaofang Zhou

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Su, H., Liu, S., Zheng, B. et al. A survey of trajectory distance measures and performance evaluation. The VLDB Journal 29, 3–32 (2020). https://doi.org/10.1007/s00778-019-00574-9

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