Abstract
With the ongoing information explosion, it is necessary for us to design an efficient, effective and easy analytic mechanisms to detect the real-time representative paths, that followed by many objects at almost the same time and space. In this paper, we propose a novel classification method based on local trajectory intersection under road-constraint instead of traditional algorithm based on distance. Our trajectory intersection algorithm is applied for each segment that has been simplified, forming a local trajectory track record. Through the analysis on the records, the most representative paths of a trajectory can be detected. The experimental results show that, the proposed method provides an improved performance in terms of computational costs and practicability.
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References
Panagiotakis, C., Pelekis, N., Kopanakis, I.: Trajectory Voting and Classification Based on Spatiotemporal Similarity in Moving Object Databases. In: IDA 2009: Proc. of the Advances in Intelligent Data Analysis, pp. 131–142 (2009)
Van De Weghe, N., Cohn, A., Bogaert, P., Maeyer, P.: Representation of Moving Objects along a Road Network. In: Proceedings of the Twelfth International Conference on Geoinformatics- Geospatial Information Research, pp. 187–194 (2004).
Pelekis, N., Kopanakis, I., Marketos, G., Ntoutsi, I., Andrienko, G., Theodoridis, Y.: Similarity Search in Trajectory Databases. In: TIME 2007: Proc. of the 14th Int. Symposium on Temporal Representation and Reasoning, pp. 129–140 (2007)
Speicys, L., Jensen, C.S., Kligys, A.: Computational Data Modeling for Network-constrained Moving Objects. In: Proceedings of the Eleventh ACM International Symposium on Advances in Geographic Information Systems, pp. 118–125 (2003)
Anagnostopoulos, A., Vlachos, M., Hadjieleftheriou, M., Keogh, E., Yu, P.S.: Global Distance-based Segmentation of Trajectories. In: KDD 2006: Proc. of the 12th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 34–43 (2006)
Lee, J.G., Han, J., Whang, K.Y.: Trajectory Clustering: a Partition-and-group Framework. In: SIGMOD 2007: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 593–604 (2007)
Hwang, J.-R., Kang, H.-Y., Li, K.-J.: Spatial-temporal Similarity Analysis between Trajectories on Road Networks. In: ER Workshops, pp. 280–289 (2005)
Hwang, J.-R., Kang, H.-Y., Li, K.-J.: Searching For Similar Trajectories on Road Networks using Spatial-temporal Similarity. In: Manolopoulos, Y., Pokorný, J., Sellis, T.K. (eds.) ADBIS 2006. LNCS, vol. 4152, pp. 282–295. Springer, Heidelberg (2006)
Sacharidis, D., Patroumpas, K., Terrovitis, M., Kantere, V., Potamias, M., Mouratidis, K., Sellis, T.: On-line Discovery of Hot Motion Paths. In: EDBT 2008: Proc. of the 11th Int. Conf. on Extending Database Technology, pp. 392–403 (2008)
Tiakas, E., Papadopoulos, A.N., Nanopoulos, A., Manolpoulos, Y., Stojanovic, D.: Searching for Similar Trajectories in Spatial Networks. Journal of Systems and Software 82, 772–788 (2009)
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Xiang, X., Pi, D., Jiang, J. (2010). Trajectory Simplification and Classification for Moving Object with Road-Constraint. In: Huang, DS., McGinnity, M., Heutte, L., Zhang, XP. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Communications in Computer and Information Science, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14831-6_22
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DOI: https://doi.org/10.1007/978-3-642-14831-6_22
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