Movement similarity assessment using symbolic representation of trajectories

International Journal of Geographical Information Science

Volumn 26, Issue 9, 2012, Pages 1563-1588

  Dodge, Somayeh ^a   Laube, Patrick ^a   Weibel, Robert ^a  

^a UNIVERSITY OF ZURICH   (Switzerland)

Author keywords

movement parameter; movement patterns; Movement similarity; trajectory clustering; trajectory segmentation

Indexed keywords

CLUSTER ANALYSIS; GPS; MOVEMENT; SEGMENTATION; TRACKING; TRAJECTORY;

EID: 84866723284     PISSN: 13658816     EISSN: 13623087     Source Type: Journal    
DOI: 10.1080/13658816.2011.630003     Document Type: Article

References (39)

1. Alt, H. 2009. "The computational geometry of comparing shapes". In Efficient algorithms. Vol. 5760 of Lecture Notes in Computer Science, Edited by: Albers, S., Alt, H. and Näher, S. Vol. 5760 of, 235-248. Berlin, Heidelberg: Springer.
2. Anagnostopoulos, A. Global distance-based segmentation of trajectories. Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. New York. pp.34-43. ACM.
3. Aßfalg, J. 2008. Similarity search in multimedia time series data using amplitude-level features. Advances in multimedia modeling,: 123-133.
4. Bozkaya, T., Yazdani, N. and Özsoyolu, M. Proceedings of the sixth international conference on Information and knowledge management - CIKM ′97. New York. Matching and indexing sequences of different lengths, pp.128-135. ACM Press.
5. Buchin, K. Finding long and similar parts of trajectories. Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems-GIS ′09. New York. pp.296-305. ACM Press.
6. Buchin, M. An algorithmic framework for segmenting trajectories based on spatio-temporal criteria. Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems-GIS ′10. New York. pp.202-211. ACM.
7. Chen, L., Özsu, M.T. and Oria, V. Symbolic representation and retrieval of moving object trajectories. MIR ′04: Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval. New York. pp.227-234. ACM.
8. Chen, L., Özsu, M.T. and Oria, V. Robust and fast similarity search for moving object trajectories. SIGMOD ′05: Proceedings of the 2005 ACM SIGMOD international conference on Management of data. New York. pp.491-502. ACM.
9. Ding, H., Trajcevski, G. and Scheuermann, P. Efficient similarity join of large sets of moving object trajectories. 15th international symposium on temporal representation and reasoning. Washington, DC. pp.79-87. IEEE.
10. Ding, H. 2008 b. Querying and mining of time series data: experimental comparison of representations and distance measures. Proceedings VLDB Endowment, 1(2): 1542-1552.
11. Dodge, S., Weibel, R. and Forootan, E. 2009. Revealing the physics of movement: comparing the similarity of movement characteristics of different types of moving objects. Computers, Environment and Urban Systems, 33(6): 419-434.
12. Dodge, S., Weibel, R. and Lautenschütz, A.K. 2008. Towards a taxonomy of movement patterns. Information Visualization, 7(3-4): 240-252.
13. Doherty, S. 2001. Moving beyond observed outcomes: integrating global positioning systems and interactive computer-based travel behaviour surveys. Transportation Research E-Circular, C, 26: 449-466.
14. Du Mouza, C., Rigaux, P. and Scholl, M. On-line aggregation and filtering of pattern-based queries. Scientific and statistical database management, 2006. 18th international conference on IEEE. Washington, DC. pp.333-342. IEEE.
15. Du Mouza, C., Rigaux, P. and Scholl, M. 2007. Parameterized pattern queries. Data & Knowledge Engineering, 63(2): 433-456.
16. Elsner, J. and Kara, A. 1999. Hurricanes of the North Atlantic: climate and society, New York Oxford: Oxford University Press.
17. Etienne, L., Devogele, T. and Bouju, A. Spatio-temporal trajectory analysis of mobile objects following the same itinerary. Proceedings of the international symposium on spatial data handling (SDH). Hong Kong.
18. Faloutsos, C. A signature technique for similarity-based queries. Proceedings. Compression and complexity of SEQUENCES 1997 (Cat. No.97TB100171). Washington, DC. pp.2-20. IEEE Computer Society.
19. Foster, G. 1996. Time series analysis by projection. I. Statistical properties of Fourier analysis. The Astronomical Journal, 111: 541-554.
20. Frentzos, E., Gratsias, K. and Theodoridis, Y. Index-based Most Similar Trajectory Search. ICDE 2007. IEEE 23rd international conference on data engineering. Washington, DC. pp.816-825. IEEE.
21. Giannotti, F. and Pedreschi, D. 2008. Mobility, data mining and privacy, Berlin, Heidelberg: Springer-Verlag.
22. Kisilevich, S. Spatio-Temporal Clustering: a Survey. Italy: Springer.
23. Laube, P. 2007. Movement beyond the snapshot dynamic analysis of geospatial lifelines. Computers, Environment and Urban Systems, 31(5): 481-501.
24. Levenshtein, V. 1966. Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics-Doklady, 10: 707-710.
25. Lin, B. and Su, J. Shapes based trajectory queries for moving objects. GIS ′05: Proceedings of the 13th annual ACM international workshop on Geographic information systems. New York. pp.21-30. ACM.
26. Little, J.J. and Gu, Z. Video retrieval by spatial and temporal structure of trajectories. Proceedings of SPIE, the International Society for Optical Engineering. Bellingham, WA. pp.545-552. SPIE.
27. Malkin, W. and Holzworth, G.C. 1954. Hurricane edna, 1954. Monthly Weather Review, 82(9): 267-279.
28. Miller, H.J. and Han, J. 2009. Geographic data mining and knowledge discovery, 2nd, London: Taylor & Francis Group.
29. Nanni, M. and Pedreschi, D. 2006. Time-focused density-based clustering of trajectories of moving objects. Intelligent Information Systems, 27: 267-289.
30. Pelekis, N. Similarity search in trajectory databases. TIME′07: proceedings of the 14th international symposium on temporal representation and reasoning. Washington, DC. pp.129-140. IEEE Computer Society.
31. Sinha, G. and Mark, D.M. 2005. Measuring similarity between geospatial lifelines in studies of environmental health. Journal of Geographical Systems, 7(1): 115-136.
32. Trajcevski, G. Dynamics-aware similarity of moving objects trajectories. GIS ′07: proceedings of the 15th annual ACM international symposium on Advances in geographic information systems. New York. pp.1-8. ACM.
33. van Kreveld, M. and Luo, J. 2The definition and computation of trajectory and subtrajectory similarity. GIS ′07: proceedings of the 15th annual ACM international symposium on Advances in geographic information systems. New York. pp.1-4. ACM.
34. Vlachos, M., Gunopulos, D. and Kollios, G. Discovering similar multidimensional trajectories. ICDE ′02: Proceedings 18th International Conference on Data Engineering. Washington, DC. pp.673-684. IEEE Computer Societ.
35. Vlachos, M., Gunopulos, D. and Das, G. Rotation invariant distance measures for trajectories. KDD ′04: Proceedings of the 2004 ACM SIGKDD international conference on knowledge discovery and data mining. New York, New York, USA. pp.707-712. ACM Press.
36. Vlachos, M., Gunopulos, D. and Kollios, G. Robust similarity measures for mobile object trajectories. DEXA′02: proceedings of the 13th international workshop on database and expert systems applications. Los Alamitos. pp.721-728. IEEE Computer Society.
37. Yan, Z. A hybrid model and computing platform for spatio-semantic trajectories. The semantic web: research and applications. pp.60-75. Berlin: Springer.
38. Yanagisawa, Y., Akahani, J.i. and Satoh, T. Shape-Based Similarity Query for Trajectory of Mobile Objects. MDM ′03: proceedings of the 4th international conference on mobile data management. London. pp.63-77. Springer-Verlag.
39. Yujian, L. and Bo, L. 2007. A normalized levenshtein distance metric. IEEE transactions on pattern analysis and machine, 29(6): 1091-1095.