Skip to main content
Log in

Enabling Location-based Services—Multi-Graph Representation of Transportation Networks

  • Published:
GeoInformatica Aims and scope Submit manuscript

Abstract

Advances in wireless communications, positioning technologies, and consumer electronics combine to enable a range of applications that use a mobile user’s geo-spatial location to deliver on-line, location-enhanced services, often referred to as location-based services. This paper assumes that the service users are constrained to a transportation network, and it delves into the modeling of such networks, points of interest, and the service users with the objective of supporting location-based services. In particular, the paper presents a framework that encompasses two interrelated models—a two-dimensional, spatial representation and a multi-graph presentation. The former, high-fidelity model may be used for the positioning of content and users in the infrastructure (e.g., using map matching). The latter type of model is recognized as an ideal basis for a variety of query processing tasks, e.g., route and distance computations. Together, the two models capture central aspects of the problem domain needed in order to support the different types of queries that underlie location-based services. Notably, the framework is capable of capturing roads with lanes, lane shift and u-turn regulations, and turn restrictions. As part of the framework, the paper constructively demonstrates how it is possible map instances of the semantically rich two-dimensional model to instances of the graph model that preserve the topology of the two-dimensional model instances. In doing so, the paper demonstrates how a wealth of previously proposed query processing techniques based on graphs are applicable even in the context of complex transportation networks. The paper also presents means of compacting graphs while preserving aspects of the graphs that are important for the intended applications.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. V.T. de Almeida and R.H. Güting. “Using Dijkstra’s algorithm to incrementally find the k-nearest neighbors in spatial network databases,” in Proc. ACM Symp. on Appl. Comp. (SAC), pp. 58–62, Dijon, France, April 2006.

  2. Assisted GPS. http://en.wikipedia.org/wiki/Assisted_GPS , current as of November, 2006.

  3. R. Benetis, C.S. Jensen, G. Karčiauskas, and S. Šaltenis. “Nearest neighbor and reverse nearest neighbor queries for moving objects,” in Proc. Int. Database Eng. Applic. Symp. (IDEAS), pp. 44–53, Edmonton, Canada, July 2002.

  4. T. Caldwell. “On finding minimum routes in a network with turn penalties,” Communications of the ACM, Vol. 4(2):107–108, 1961.

    Article  Google Scholar 

  5. A. Civilis, C.S. Jensen, J. Nenortaite, and S. Pakalnis. “Efficient tracking of moving objects with precision guarantees,” in Proc. Int. Conf. on Mob. and Ubiq. Syst., pp. 164–173, 2004.

  6. A. Civilis, C.S. Jensen, and S. Pakalnis. “Techniques for efficient road-network-based tracking of moving objects,” in IEEE Trans. on Knowl. Data Eng., Vol. 17(5), pp. 698–712, 2005.

    Article  Google Scholar 

  7. E.W. Dijkstra. “A note on two problems in connection with graphs,” Numerische Mathematik, Vol. 1:269–71, 1959.

    Article  Google Scholar 

  8. K. Dueker and J.A. Butler. “GIS-T enterprise data model with suggested implementation choices,” Journal of the Urban and Regional Information Systems Association, Vol. 10(1):12–36, 1998.

    Google Scholar 

  9. European Geostationary Navigation Overlay Service (EGNOS). “The European space agency,” in http://www.esa.int/esaNA/egnos.html , current as of November, 2006.

  10. P. Fohl, K.M. Curtin, M.F. Goodchild, and R.L. Church, “A non-planar, lanebased navigable data model for ITS,” in Proc. International Symposium on Spatial Data Handling, pp. 17–29, 1996.

  11. R.H. Güting and M. Schneider. Moving Objects Databases. Morgan Kaufmann, 2005.

  12. Galileo. “The European Space Agency,” in http://www.esa.int/esaNA/galileo.html , current as of November 2006.

  13. J. Gottsegen, M. Goodchild, and R. Church. “A conceptual navigable database model for intelligent vehicle highway systems,” in Proc. GIS/LIS, pp. 371–380, 1994.

  14. GIS for Transportation (GIS-T) Symposium. http://www.gis-t.org/ .

  15. R.H. Güting, V.T. de Almeida, and Z. Ding. “Modeling and querying moving objects in networks,” VLDB Journal, Vol. 15(2):165–190, 2006.

    Article  Google Scholar 

  16. R.H. Güting, M.H. Böhlen, M. Erwig, C.S. Jensen, N.A. Lorentzos, M. Schneider, and M. Vazirgiannis. “A foundation for representing and querying moving objects,” ACM Transactions on Database Systems, Vol. 25(1):1–42, 2000.

    Article  Google Scholar 

  17. C.S. Jensen. “Database Aspects of location-based services,” in J. Schiller and A. Voisard (Eds.), Location-Based Services, 115–148, Morgan Kaufmann, 2004.

  18. G. Kollios, D. Gunopulos, and V.J. Tsotras. “Nearest neighbor queries in a mobile environment,” in Proc. Int. Workshop on Spatio-Temp. Database Management, (STDBM), pp. 119–134, Edinburgh, Scotland, September 1999.

  19. M. Kolahdouzan and C. Shahabi. “Voronoi-based k nearest neighbor search for spatial network databases,” in Proc. 30th Int. Conf. on Very Large Data Bases (VLDB), pp. 840–851, Toronto, Canada, August 2004.

  20. K. Mouratidis, M.L. Yiu, D. Papadias, and N. Mamoulis. “Continuous nearest neighbor monitoring in road networks,” in Proc. 32nd Int. Conf. on Very Large Data Bases, pp. 43–54, Seoul, Korea, September 2006.

  21. NCHRP. A Generic Data Model for Linear Referencing Systems, Transportation Research Board: Washington, DC, 1997.

    Google Scholar 

  22. Oracle database 10g: oracle spatial network data model, An Oracle Technical White Paper, 2005.

  23. D. Papadias, J. Zhang, N. Mamoulis, and Y. Tao. “Query Processing in spatial network databases,” in Proc. VLDB, pp. 802–813, 2003.

  24. S.F. Rounds, Y. Bock, L. Bock, and J. Fayman. “Epoch-by-EpochTM Real-Time GPS Positioning with the L-3 Communications / Interstate Electronics Corporation GPS Receiver,” Presented at the Joint Navig. Conf., 2004.

  25. J. Sankaranarayanan, H. Alborzi, and H. Samet. “Efficient query processing on spatial networks,” in Proc. 13th ACM Int. Workshop on Geogr. Inf. Syst. (ACM GIS), pp. 200–209, November 2005.

  26. C. Shahabi, M.R. Kolahdouzan, and M. Sharifzadeh. “A road network embedding technique for k-nearest neighbor search in moving object databases,” in Proc. 10th ACM Int. Sym. on Adv. in Geogr. Inf. Syst., pp. 94–100, McLean, VA, USA, November 2002.

  27. A.P. Sistla, O. Wolfson, S. Chamberlain, and S. Dao. “Modeling and querying moving objects,” in Proc. 13th Int. Conf. on Data Eng. (ICDE), pp. 422–432, Birmingham, UK, April 1997.

  28. S. Šaltenis, C.S. Jensen, S.T. Leutenegger, and M.A. Lopez. “Indexing the positions of continuously moving objects,” in Proc. Int. Conf. on Management of Data (ACM SIGMOD), pp. 331–342, Dallas, Texas, USA, May 2000.

  29. U.S. Department of Transportation. “Federal-aid highway length—2001 miles by lane width,” in http://www.fhwa.dot.gov/ohim/hs01/hm33.htm , current as of November 2006.

  30. Wide Area Augmentation System (WAAS). “Federal Aviation Administration,” in http://gps.faa.gov/Programs/WAAS/waas.htm , current as of November 2006.

  31. O. Wolfson, L. Jiang, A.P. Sistla, S. Chamberlain, N. Rishe, and M. Deng. “Databases for tracking mobile units in real time,” in Proc., 7th Intt. Conf. Database Theory (ICDT), pp. 169–186, Jerusalem, Israel, January 1998.

  32. M. Zeiler. Modeling Our World, ESRI Press, 1999.

  33. B. Zheng, W.C. Lee, and D.L. Lee. “Search k nearest neighbors on air,” in Proc. 4th Int. Conf. on Mobile Data Management (MDM), pp. 181–195, Melbourne, Australia, January 2003.

Download references

Acknowledgements

We would like to thank Irina Aleksandrova and Augustas Kligys for their collaborations during the early stages of this work. This work was supported in part by a grant from the Electronics and Telecommunications Research Institute, South Korea.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Laurynas Speičys.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Speičys, L., Jensen, C.S. Enabling Location-based Services—Multi-Graph Representation of Transportation Networks. Geoinformatica 12, 219–253 (2008). https://doi.org/10.1007/s10707-007-0032-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10707-007-0032-1

Keywords

Navigation