Skip to main content
SpringerLink
Log in

Direction-aware KNN queries for moving objects in a road network

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Recently more and more people focus on k-nearest neighbor (KNN) query processing over moving objects in road networks, e.g., taxi hailing and ride sharing. However, as far as we know, the existing k-nearest neighbor (KNN) queries take distance as the major criteria for nearest neighbor objects, even without taking direction into consideration. The main issue with existing methods is that moving objects change their locations and directions frequently over time, so the information updates cannot be processed in time and they run the risk of retrieving the incorrect KNN results. They may fail to meet users’ needs in certain scenarios, especially in the case of querying k-nearest neighbors for moving objects in a road network. In order to find the top k-nearest objects moving toward a query point, this paper presents a novel algorithm for direction-aware KNN (DAKNN) queries for moving objects in a road network. In this method, R-tree and simple grid are firstly used as the underlying index structure, where the R-tree is used for indexing the static road network and the simple grid is used for indexing the moving objects. Then, it introduces the notion of “azimuth” to represent the moving direction of objects in a road network, and presents a novel local network expansion method to quickly judge the direction of the moving objects. By considering whether a moving object is moving farther away from or getting closer to a query point, the object that is definitely not in the KNN result set is effectively excluded. Thus, we can reduce the communication cost, meanwhile simplify the computation of moving direction between moving objects and query point. Comprehensive experiments are conducted and the results show that our algorithm can achieve real-time and efficient queries in retrieving objects moving toward query point in a road network.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29

Similar content being viewed by others

References

  1. Papadias, D., Zhang, J., Mamoulis, N., et al.: Query processing in spatial network databases[J]. VLDB. 29, 802–813 (2003)

    Google Scholar 

  2. Kolahdouzan, M.: Shahabi C.Voronoi-based k nearest neighbor search for spatial network databases[C].proceedings of the thirtieth international conference on very large data bases-volume 30. VLDB Endowment. 840–851 (2004)

  3. Huang, X., Jensen, C.S., Šaltenis, S.: The islands approach to nearest neighbor querying in spatial networks[J]. Lect. Notes Comput. Sci. 3633, 73–90 (2006)

    Article  Google Scholar 

  4. Zhang, P.F., Lin, H.Z., Gao, Y.J., et al.: Aggregate keyword nearest neighbor queries on road networks [J]. GeoInformatica. 22, 237–268 (2018)

    Article  Google Scholar 

  5. Lee, K., Lee, W.-C., Zheng, B., Tian, Y.: Road: a new spatial object search framework for road networks. TKDE. 24(3), 547–560 (2012)

    Google Scholar 

  6. R. Zhong, G. Li, K.-L. Tan, and L. Zhou. G-tree: an efficient index for knn search on road networks. In CIKM, pages 39–48, 2013

  7. Zhong, R., Li, G., Tan, K., Zhou, L., Gong, Z.: G-tree: an efficient and scalable index for spatial search on road networks. TKDE. 27(8), 2175–2189 (2015)

    Google Scholar 

  8. Guttman A. R-Trees: a Dynamic Index Structure for Spatial Searching[M]. ACM, 1984

  9. Beckmann N, Kriegel H P, Schneider R, et al. The R*-Tree: an Efficient and Robust Access Method for Points and Rectangles[M]. ACM, 1990

  10. Frentzos, E.: Indexing objects moving on fixed networks.[J]. Lect. Notes Comput. Sci. 2750, 289–305 (2003)

    Article  Google Scholar 

  11. Tao Y, Faloutsos C, Papadias D, et al. Prediction and indexing of moving objects with unknown motion patterns[C].Proceedings of the 2004 ACM SIGMOD international conference on Management of data. ACM, 2004: 611–622

  12. Jensen C S, Lu H, Yang B. Indexing the trajectories of moving objects in symbolic indoor space[M]. Advances in Spatial and Temporal Databases. Springer Berlin Heidelberg, 2009: 208–227

  13. Huang X, Jensen C S, Lu H, et al. S-GRID: A versatile approach to efficient query processing in spatial networks[M]. Advances in Spatial and Temporal Databases. Springer Berlin Heidelberg, 2007: 93–111

  14. Chen, J., Meng, X.: Update-efficient indexing of moving objects in road networks[J]. GeoInformatica. 13(4), 397–424 (2009)

    Article  Google Scholar 

  15. Gu, Y., Zhang, H., Wang, Z.G., et al.: Efficient moving k nearest neighbor queries over line segment objects[J]. World Wide Web. 19, 653–677 (2016)

    Article  Google Scholar 

  16. Xu, X.J., Bao, J.S., Yao, B., Zhou, J.Y., Tang, F.L., Guo, M.Y., Xu, J.Q.: Reverse furthest neighbors query in road networks. J. Comput. Sci. Technol. 32(1), 155–167 (2017)

    Article  MathSciNet  Google Scholar 

  17. Wang H, Zimmermann R. location-based query processing on moving objects in road networks[C].proc. Intl. Conf. on Very Large Data Bases (VLDB 2007). 2007: 321–332

  18. Hendawi A M, Bao J, Mokbel M F, et al. Predictive tree: an efficient index for predictive queries on road networks. International Conference on Data Engineering, 2015

  19. Li G, Feng J, Xu J. Desks: direction-aware spatial keyword search[C]. IEEE 28th International Conference on Data Engineering (ICDE), 2012: 474–485

  20. Lee, M.J., Choi, D.W., Kim, S.Y., Park, H.M., Choi, S., Chung, C.W.: The direction-constrained k nearest neighbor query[J]. GeoInformatica. 20(3), 471–502 (2016)

    Article  Google Scholar 

  21. Lee K W, Choi D W, Chung C W. Dart: an Efficient Method for Direction-Aware Bichromatic Reverse K Nearest Neighbor Queries[M]. Advances in Spatial and Temporal Databases. Springer Berlin Heidelberg, 2013: 295–311

  22. Sharifzadeh M, Shahabi C. VoR-tree: R-trees with Voronoi diagrams for efficient processing of spatial nearest neighbor queries.[J]. Proceedings of the Vldb Endowment, 2010:1231–1242, 3

  23. Cary A, Wolfson O, Rishe N. Efficient and scalable method for processing top-k spatial boolean queries[C].Scientific and Statistical Database Management. Springer Berlin Heidelberg, 2010: 87–95

  24. Zheng K, Shang S, Yuan N J, et al. Toward efficient search for activity trajectories[C]. 2013 IEEE 29th international conference on data engineering (ICDE). IEEE, 2013: 230–241

  25. Yu X, Pu K Q, Koudas N. monitoring k-nearest neighbor queries over moving objects[C]. 2014 IEEE 30th international conference on data engineering IEEE computer Society, 2005:631–642

  26. Brinkhoff, T.: A framework for generating network-based moving objects[J]. GeoInformatica. 6(2), 153–180 (2002)

    Article  MATH  Google Scholar 

  27. Dong, T., Cheng, Q., Cao, B., Shi, J.: A novel approach to distributed rule matching and multiple firing based on MapReduce[J]. J. Database Manag. 29(2), 62–84 (2018)

    Article  Google Scholar 

  28. Huang, S.-C., Jiau, M.-K., Lin, C.-H.: Optimization of the carpool service problem via a fuzzy-controlled genetic algorithm[C]. IEEE Trans. Fuzzy Syst. 23, 1698–1712 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by following foundations: National Natural Science Foundation of China (No.61672464, No.61572437), Key Research and Development Project of Zhejiang Province (No.2015C01034, No.2017C01013), and Major Science and Technology Innovation Project of Hangzhou (No.20152011A03). Corresponding authors are Fan Jing (fanjing@zjut.edu.cn) and Cao Bin (bincao@zjut.edu.cn).

Author information

Authors and Affiliations

  1. College of Computer, Zhejiang University of Technology, Hangzhou, 310023, China

    Dong Tianyang, Yuan Lulu, Cheng Qiang, Cao Bin & Fan Jing

Authors
  1. Dong Tianyang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuan Lulu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cheng Qiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cao Bin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fan Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fan Jing.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tianyang, D., Lulu, Y., Qiang, C. et al. Direction-aware KNN queries for moving objects in a road network. World Wide Web 22, 1765–1797 (2019). https://doi.org/10.1007/s11280-019-00657-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-019-00657-1

Keywords

Search

Navigation