Abstract
Prediction of uncertain trajectories in moving objects databases has recently become a new paradigm for tracking wireless and mobile devices in an accurate and efficient manner, and is critical in law enforcement applications such as criminal tracking analysis. However, existing approaches for prediction in spatio-temporal databases focus on either mining frequent sequential patterns at a certain geographical position, or constructing kinematical models to approximate real-world routes. The former overlooks the fact that movement patterns of objects are most likely to be local, and constrained in some certain region, while the later fails to take into consideration some important factors, e.g., population distribution, and the structure of traffic networks. To cope with those problems, we propose a general trajectory prediction algorithm called E3TP (an Effective, Efficient, and Easy Trajectory Prediction algorithm), which contains four main phases: (i) mining “hotspot” regions from moving objects databases; (ii) discovering frequent sequential routes in hotspot areas; (iii) computing the speed of a variety of moving objects; and (iv) predicting the dynamic motion behaviors of objects. Experimental results demonstrate that E3TP is an efficient and effective algorithm for trajectory prediction, and the prediction accuracy is about 30% higher than the naive approach. In addition, it is easy-to-use in real-world scenarios.
This work is supported by the National Natural Science Foundation of China under Grant No. 60773169, the 11th Five Years Key Programs for Science and Technology Development of China under Grant No. 2006BAI05A01, the Youth Software Innovation Project of Sichuan Province under Grant No. 2007AA0032 and 2007AA0028.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Qiao, S., Tang, C., Jin, H., Dai, S., Chen, X.: Constrained K-Closest Pairs Query Processing Based on Growing Window in Crime Databases. In: 2008 IEEE International Conference on Intelligence and Security Informatics, ISI 2008, Taipei, pp. 58–63 (2008)
Morzy, M.: Mining frequent trajectories of moving objects for location prediction. In: Perner, P. (ed.) MLDM 2007. LNCS, vol. 4571, pp. 667–680. Springer, Heidelberg (2007)
Lee, J., Han, J., Whang, K.: Trajectory Clustering: A Partition-and-Group Framework. In: SIGMOD 2007, Beijing, China, pp. 593–604. ACM, New York (2007)
Trajcevski, G., Wolfson, O., Zhang, F., Chamberlain, S.: The geometry of uncertainty in moving objects databases. In: Jensen, C.S., Jeffery, K., Pokorný, J., Šaltenis, S., Bertino, E., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 233–250. Springer, Heidelberg (2002)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: SIGMOD 2000: Proceedings of the 2000 ACM SIGMOD international conference on Management of data, pp. 1–12. ACM, New York (2000)
Trajcevski, G., Wolfson, O., Hinrichs, K., Chamberlain, S.: Managing uncertainty in moving objects databases. ACM Trans. Database Syst. 29(3), 463–507 (2004)
Giannotti, F., Nanni, M., Pedreschi, D.: Efficient mining of temporally annotated sequences. In: SDM 2006: Proceedings of the 6th SIAM International Conference on Data Mining, pp. 346–357. SIAM, Bethesda (2006)
Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F.: Mining sequences with temporal annotations. In: SAC 2006: Proceedings of the 2006 ACM symposium on Applied computing, pp. 593–597. ACM, New York (2006)
Brinkhoff, T.: A framework for generating network-based moving objects. Geoinformatica 6(2), 153–180 (2002)
Halliday, D., Resnick, R., Walker, J.: Fundamentals of Physics, 8th edn. Wiley, Chichester (2007)
Qiao, S., Tang, C., Peng, J., Fan, H., Xiang, Y.: VCCM Mining: Mining Virtual Community Core Members Based on Gene Expression Programming. In: Chen, H., Wang, F.-Y., Yang, C.C., Zeng, D., Chau, M., Chang, K. (eds.) WISI 2006. LNCS, vol. 3917, pp. 133–138. Springer, Heidelberg (2006)
Qiao, S., Tang, C., Peng, J., Hu, J., Zhang, H.: BPGEP: Robot Path Planning based on Backtracking Parallel-Chromosome GEP. In: Proceedings of the International Conference on Sensing, Computing and Automation, ICSCA 2006, DCDIS series B: Application and Algorithm, vol. 13(e), pp. 439–444. Watam Press (2006)
Qiao, S., Tang, C., Peng, J., Yu, Z., Jiang, Y., Han, N.: A Novel Prescription Function Reduction Algorithm based on Neural Network. In: Proceedings of the International Conference on Sensing, Computing and Automation, ICSCA 2006, DCDIS series B: Application and Algorithm, vol. 13(e), pp. 939–944. Watam Press (2006)
Shao-jie, Q., Chang-jie, T., Shu-cheng, D., Chuan, L., Yu, C., Jiang-tao, Q.: SIGA: A novel self-adaptive immune genetic algorithm. Acta Scientiarum Natralium Universitatis Sunyatseni 47(3), 6–9 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Long, T., Qiao, S., Tang, C., Liu, L., Li, T., Wu, J. (2009). E3TP: A Novel Trajectory Prediction Algorithm in Moving Objects Databases. In: Chen, H., Yang, C.C., Chau, M., Li, SH. (eds) Intelligence and Security Informatics. PAISI 2009. Lecture Notes in Computer Science, vol 5477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01393-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-01393-5_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01392-8
Online ISBN: 978-3-642-01393-5
eBook Packages: Computer ScienceComputer Science (R0)