FATES: Finding A Time dEpendent Shortest path

  • Hae Don Chon
  • Divyakant Agrawal
  • Amr El Abbadi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2574)


We model a moving object as a sizable physical entity equipped with GPS, wireless communication capability, and a computer. Based on a grid model, we develop a distributed system, FATES, to manage data for moving objects in a two-dimensional space. The system is used to provide time-dependent shortest paths for moving objects. The performance study shows that FATES yields shorter average trip time when there is a more congested route than any other routes in the domain space.


Global Position System Short Path Moving Object Range Query Short Path Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    R. K. Ahuja, T. L. Magnanti, and J. B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, 1993.Google Scholar
  2. [2]
    N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger. The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 322–331, 1990.Google Scholar
  3. [4]
    H. D. Chon, D. Agrawal, and A. El Abbadi. Storage and Retrieval of Moving Objects. In Proceedings of the Int. Conf. on Mobile Data Management, pages 173–184, 2001.Google Scholar
  4. [5]
    H. D. Chon, D. Agrawal, and A. El Abbadi. Query processing for moving objects with space-time grid storage model. In Proceedings of the Int. Conf. on Mobile Data Management, 2002.Google Scholar
  5. [6]
    S. Handley, P. Langley, and F. Rauscher. Learning to predict the duration of an automobile trip. In Proceedings of the Int. Conf. on Knowledge Discovery and Data Mining, pages 219–223, 1998.Google Scholar
  6. [7]
    G. Kollios, D. Gunopulos, and V. J. Tsotras. On Indexing Moving Objects. In Proceedings of ACM Symp. on Principles of Database Systems, pages 261–272, 1999.Google Scholar
  7. [8]
    K. Nachtigall. Time depending shortest-path problems with applications to railway networks. European Journal of Operational Research, 83:154–166, 1995.zbMATHCrossRefGoogle Scholar
  8. [9]
  9. [10]
    A. Orda and R. Rom. Shortest-path and minimum-delay algorithms in networks with time-dependent edge-length. Journal of the ACM, 37(3):607–625, 1990.zbMATHCrossRefMathSciNetGoogle Scholar
  10. [11]
    S. Pallottino and M. G. Scutella. Shortest path algorithms in transportation models: classical and innovative aspects. In In Equilibrium and Advanced Transportation Modelling, Kluwer, pages 245–281, 1998.Google Scholar
  11. [12]
    C. E. Perkins. Mobile IP. IEEE Communications Magazine, pages 84–99, May 1997.Google Scholar
  12. [13]
    D. Pfoser and C. S. Jensen. Capturing the Uncertainty of Moving-Object Representations. In Proc. of the Int. Symposium on Spatial Databases, SSD, pages 111–132, 1999.Google Scholar
  13. [14]
    D. Pfoser, C. S. Jensen, and Y. Theodoridis. Novel Approaches to the Indexing of Moving Object Trajectories. In Proceedings of the Int. Conf. on Very Large Data Bases, pages 395–406, 2000.Google Scholar
  14. [15]
    S. Saltenis, C. S. Jensen, S. T. Leutenegger, and M. A. Lopez. Indexing the Positions of Continuously Moving Objects. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 331–342, 2000.Google Scholar
  15. [16]
    D. Schrank and T. Lomax. The 2001 Urban Mobility Report. Technical report, Texas Transportation Institute, 2001.Google Scholar
  16. [17]
    A. P. Sistla, O. Wolfson, S. Chamberlain, and S. Dao. Modeling and Querying Moving Objects. In Proceedings of the Int. Conf. on Data Engineering, pages 422–432, 1997.Google Scholar
  17. [18]
    J. Tayeb, O. Ulusoy, and O. Wolfson. A Quadtree Based Dynamic Attribute Indexing Method. The Computer Journal, 41(3):185–200, 1998.zbMATHCrossRefGoogle Scholar
  18. [19]
    M. Vazirgiannis and O. Wolfson. A Spatiotemporal Model and Language for Moving Objects on Road Networks. In Int. Symposium on Spatial and Temporal Databases, pages 20–35, 2001.Google Scholar
  19. [20]
    O. Wolfson, B. Xu, S. Chamberlain, and L. Jiang. Moving Objects Databases: Issues and Solutions. In Proceedings of the 10th International Conference on Scientific and Statistical Database Management, pages 111–122, July 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hae Don Chon
    • 1
  • Divyakant Agrawal
    • 2
  • Amr El Abbadi
    • 2
  1. 1.Samsung ElectronicsKorea
  2. 2.Department of Computer ScienceUniversity of CaliforniaSanta Barbara

Personalised recommendations