Advertisement

E3TP: A Novel Trajectory Prediction Algorithm in Moving Objects Databases

  • Teng Long
  • Shaojie Qiao
  • Changjie Tang
  • Liangxu Liu
  • Taiyong Li
  • Jiang Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5477)

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.

Keywords

trajectory prediction moving objects databases criminal tracking analysis hotspot regions frequent sequential routes 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    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)Google Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Trajcevski, G., Wolfson, O., Hinrichs, K., Chamberlain, S.: Managing uncertainty in moving objects databases. ACM Trans. Database Syst. 29(3), 463–507 (2004)CrossRefGoogle Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    Brinkhoff, T.: A framework for generating network-based moving objects. Geoinformatica 6(2), 153–180 (2002)CrossRefzbMATHGoogle Scholar
  10. 10.
    Halliday, D., Resnick, R., Walker, J.: Fundamentals of Physics, 8th edn. Wiley, Chichester (2007)zbMATHGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    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)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Teng Long
    • 1
  • Shaojie Qiao
    • 2
    • 1
    • 3
  • Changjie Tang
    • 1
  • Liangxu Liu
    • 4
    • 3
  • Taiyong Li
    • 1
    • 5
  • Jiang Wu
    • 5
  1. 1.School of Computer ScienceSichuan UniversityChengduChina
  2. 2.School of Information Science and TechnologySouthwest JiaoTong UniversityChengduChina
  3. 3.School of ComputingNational University of SingaporeSingaporeSingapore
  4. 4.School of Electronic and Information EngineeringNingbo University of TechnologyNingboChina
  5. 5.School of Economic Information EngineeringSouthwest University of Finance and EconomicsChengduChina

Personalised recommendations