Influence-Aware Predictive Density Queries Under Road-Network Constraints

  • Lasanthi HeendaliyaEmail author
  • Michael Wisely
  • Dan Lin
  • Sahra Sedigh Sarvestani
  • Ali Hurson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9239)


Density query is a very useful query type that informs users about highly concentrated/dense regions, such as a traffic jam, so as to reschedule their travel plans to save time. However, existing products and research work on density queries still have several limitations which, if can be resolved, will bring more significant benefits to our society. For example, we identify an important problem that has never been studied before. That is none of the existing works on traffic prediction consider the influence of the predicted dense regions on the subsequent traffic flow. Specifically, if road A is estimated to be congested at timestamp \(t_1\), the prediction of the condition on other roads after \(t_1\) should consider the traffic blocked by road A. In this paper, we formally model such influence between multiple density queries and propose an efficient query algorithm. We conducted extensive experiments and the results demonstrate both the effectiveness and efficiency of our approach.


Query Density Road Network Constraints Query Algorithm Threshold Cell Density Road Segment 
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.


  1. 1.
    Achtert, E., Kriegel, H.-P., Schubert, E., Zimek, A.: Interactive data mining with 3d-parallel-coordinate-trees. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2013)Google Scholar
  2. 2.
    Barth, M., Boriboonsomsin, K.: Real-world carbon dioxide impacts of traffic congestion. Transport. Res. Rec. J. Transport. Res. Board 2058, 163–171 (2008)CrossRefGoogle Scholar
  3. 3.
    Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B.: The R*-tree: an efficient and robust access method for points and rectangles (1990)Google Scholar
  4. 4.
    Bok, K.S., Yoon, H.W., Seo, D.M., Kim, M.H., Yoo, J.S.: Indexing of continuously moving objects on road networks. IEICE Trans. Inf. Syst. E91–D, 2061–2061 (2008)CrossRefGoogle Scholar
  5. 5.
    Brinkhoff, T.: A framework for generating network-based moving objects. GeoInformatica 6, 153–180 (2004)CrossRefGoogle Scholar
  6. 6.
    Chen, J.-D., Meng, X.-F.: Indexing future trajectories of moving objects in a constrained network. J. Comput. Sci. Technol. 22(2), 245–251 (2007)CrossRefGoogle Scholar
  7. 7.
    Fan, P., Li, G., Yuan, L., Li, Y.: Vague continuous K-nearest neighbor queries over moving objects with uncertain velocity in road networks. Inf. Syst. 37(1), 13–32 (2012)CrossRefGoogle Scholar
  8. 8.
    Feng, J., Lu, J., Zhu, Y., Mukai, N., Watanabe, T.: Indexing of moving objects on road network using composite structure. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part II. LNCS (LNAI), vol. 4693, pp. 1097–1104. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  9. 9.
    Feng, J., Lu, J., Zhu, Y., Watanabe, T.: Index method for tracking network-constrained moving objects. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part II. LNCS (LNAI), vol. 5178, pp. 551–558. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  10. 10.
    Hao, X., Meng, X., Xu, J.: Continuous density queries for moving objects. In: Proceedings of the Seventh ACM International Workshop on Data Engineering for Wireless and Mobile Access, MobiDE 2008 (2008)Google Scholar
  11. 11.
    Heendaliya, L., Lin, D., Hurson, A.: Continuous predictive line queries for on-the-go traffic estimation. In: Hameurlain, A., Küng, J., Wagner, R., Decker, H., Lhotska, L., Link, S. (eds.) TLDKS XVIII. LNCS, vol. 8980, pp. 80–114. Springer, Heidelberg (2015) Google Scholar
  12. 12.
    Heendaliya, L., Lin, D., Hurson, A.R.: Predictive line queries for traffic forecasting. In: Database and Expert Systems Applications (2012)Google Scholar
  13. 13.
    Jensen, C.S., Lin, D., Beng, C.O., Zhang, R.: Effective density queries on continuously moving objects. In: Proceedings of the 22nd International Conference on Data Engineering (2006)Google Scholar
  14. 14.
    Kyoung-Sook, K., Si-Wan, K., Tae-Wan, K., Ki-Joune, L.: Fast indexing and updating method for moving objects on road networks. In: Proceedings of the 4th International Conference on Web Information Systems Engineering Workshops (2003)Google Scholar
  15. 15.
    Lai, C., Wang, L., Chen, J., Meng, X., Zeitouni, K.: Effective Density queries for moving objects in road networks. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds.) APWeb/WAIM 2007. LNCS, vol. 4505, pp. 200–211. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  16. 16.
    Gunopulos, D., Hadjieleftheriou, M., Kollios, G., Tsotras, V.J.: On-line discovery of dense areas in spatio-temporal databases. In: International Symposium on Advances in Spatial and Temporal Databases, SSTDn (2003)Google Scholar
  17. 17.
    Min, W., Wynter, L.: Real-time road traffic prediction with spatio-temporal correlations. Transp. Res. Part C 19(4), 606–616 (2011)CrossRefGoogle Scholar
  18. 18.
    Morgan, L.: The effects of traffic congestion (2014)Google Scholar
  19. 19.
    Ni, J., Ravishankar, C.V.: Pointwise-dense region queries in spatio-temporal databases. In: IEEE 23rd International Conference on Data Engineering (2007)Google Scholar
  20. 20.
    Quek, C., Pasquier, M., Lim, B.B.S.: Pop-traffic: a novel fuzzy neural approach to road traffic analysis and prediction. IEEE Trans. Intell. Transp. Syst. 7(2), 133–146 (2006)CrossRefGoogle Scholar
  21. 21.
    Smith, B.L., Williams, B.M., Oswald, R.K.: Comparison of parametric and nonparametric models for traffic flow forecasting. Transp. Res. Part C 10(4), 303–321 (2002)CrossRefGoogle Scholar
  22. 22.
    Wen, J., Meng, X., Hao, X., Xu, J.: An efficient approach for continuous density queries. Front. Comput. Sci. 6(5), 581–595 (2012)MathSciNetGoogle Scholar
  23. 23.
    Yiu, M.L., Tao, Y., Mamoulis, N.: The Bdual-tree: indexing moving objects by space filling curves in the dual space. VLDB J. 17(3), 379–400 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Lasanthi Heendaliya
    • 1
    Email author
  • Michael Wisely
    • 1
  • Dan Lin
    • 1
  • Sahra Sedigh Sarvestani
    • 1
  • Ali Hurson
    • 1
  1. 1.Department of Computer ScienceMissouri University of Science and TechnologyRollaUSA

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