High Performance Multimodal Networks

  • Erik G. Hoel
  • Wee-Liang Heng
  • Dale Honeycutt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3633)


Networks often form the core of many users’ spatial databases. Networks are used to support the rapid navigation and analysis of linearly connected data such as that found in transportation networks. Common types of analysis performed on such networks include shortest path, traveling salesman, allocation, and distance matrix computation.

Network data models are usually represented as a small collection of tables: a junction table and an edge table. In the context of networks used to model transportation infrastructure, it is also necessary to model turn restrictions and impedances (delays). Network data is frequently persisted in normalized relational tables that are accessible via standard SQL-based queries. We propose a different approach where the network connectivity information is persisted using a compressed binary storage representation in a relational database. The connectivity information is accessible via standard Java, .NET, and COM APIs that are tailored to common access patterns used in the support of high performance network engines. These network engines run on the client or application server tier rather than as extensions on the relational server.

In this paper, we discuss the problem of building a robust and scalable implementation of a network data model. The fundamental and central requirements are enumerated. These requirements include support for hierarchical networks, turn restrictions, and logical z elevations. We propose a different approach to representing network topology that addresses many of the high-end modeling requirements of network systems. Our approach supports all of the listed requirements in addition to multimodal modeling (e.g., coexistent road, bus, and rail networks) within the context of multi-user, long transaction databases.


Transportation Network Line Feature Line Graph Network Element Turn Restriction 
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.
    Añez, J., de la Barra, T., Pérez, B.: Dual Graph Representation of Transport Networks. Transportation Research 30(3) (June 1996)Google Scholar
  2. 2.
    Caldwell, T.: On Finding Minimum Routes in a Network with Turn Penalties. Communications of the ACM 4(2) (February 1961)Google Scholar
  3. 3.
    Caliper. TransCAD: Transportation GIS Software Ref. Man, Newton, MA (1996)Google Scholar
  4. 4.
    Cormen, T., Leiserson, C., Rivest, R., Stein, C.: Introduction to Algorithms, 2nd edn. MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  5. 5.
    Dueker, K., Butler, J.: GIS-T Enterprise Data Model with Suggested Implementation Choices. PR101, Center for Urban Studies, Portland State (1997)Google Scholar
  6. 6.
    Evans, J., Minieka, E.: Optimization Algorithms for Networks and Graphs. In: Dekker, M. (ed.), New York (1992)Google Scholar
  7. 7.
    ESRI. Network Analysis; Workspation ARC/INFO version 8.1. Prepared by Environmental Systems Research Institute, Redlands, California (2001)Google Scholar
  8. 8.
    Foresman, T. (ed.): The History of Geographical Information Systems. Prentice Hall PTR, Upper Saddle River (1998)Google Scholar
  9. 9.
    Goodchild, M.: Geographic Information Systems and Disaggregate Transportation Planning. Geographical Systems 5 (1998)Google Scholar
  10. 10.
    Güting, R., de Almeida, V., Ding, Z.: Modeling and Querying Moving Objects in Networks. FernUniversität in Hagen, Informatik-Report 308 (2004)Google Scholar
  11. 11.
    Hoel, E., Menon, S., Morehouse, S.: Building a Robust Relational Implementation of Topology. In: Hadzilacos, T., Manolopoulos, Y., Roddick, J., Theodoridis, Y. (eds.) SSTD 2003. LNCS, vol. 2750. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  12. 12.
    Huang, Y.-W., Jing, N., Rundensteiner, E.: Optimizing Path Query Performance: Graph Clustering Strategies. Transportation Research Part C 8 (2000)Google Scholar
  13. 13.
    International Organization for Standardization (ISO). ISO International Standard: Database Language SQL – Part 2: Foundation (SQL/Foundation), ANSI/ISO.IEC 9075-2:99 (September 1999)Google Scholar
  14. 14.
    Jensen, C., Pedersen, T., Speičys, L., Timko, I.: Data Modeling for Mobile Services in the Real World. In: Hadzilacos, T., Manolopoulos, Y., Roddick, J., Theodoridis, Y. (eds.) SSTD 2003. LNCS, vol. 2750. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  15. 15.
    Kirby, R., Potts, R.: The Minimum Route Problem for Networks with Turn Penalties and Prohibitions. Transportation Research 3 (1969)Google Scholar
  16. 16.
    Kothuri, R., Godfrind, A., Beinat, E.: Pro Oracle Spatial. A Press, Berkeley (2004)Google Scholar
  17. 17.
    Lang, L.: Transportation GIS. ESRI Press, Redlands (1999)Google Scholar
  18. 18.
    Longley, P., Goodchild, M., Maguire, D., Rhind, D. (eds.): Geographical Information Systems, vol. 2. John Wiley & Sons, New York (1999)Google Scholar
  19. 19.
    Mainguenaud, M.: Modeling the Network Component of Geographical Information Systems. International Journal of Geographic Information Systems 9(6) (1995)Google Scholar
  20. 20.
    Marx, R.: The TIGER System: Automating the Geographic Structure of the United States Census. Government Publications Review 13 (1986)Google Scholar
  21. 21.
    Miller, H., Shaw, S.-L.: Geographic Information Systems for Transportation. Oxford University Press, Oxford (2001)Google Scholar
  22. 22.
    Morehouse, S.: ARC/INFO: A Geo-Relational Model for Spatial Information. In: Proc. of the 7th Intl. Symp. on Computer Assisted Cartography (Auto-Carto 7), Washington, DC (March 1985)Google Scholar
  23. 23.
    Oracle.: Oracle Database 10g Network Data Model. Prepared by Oracle Corporation, Redwood Shores, California (2003)Google Scholar
  24. 24.
    Papadias, D., Zhang, J., Mamoulis, N., Tao, Y.: Query Processing in Spatial Network Databases. In: Proc. of the 29th VLDB Conf (VLDB 2003), Berlin (2003)Google Scholar
  25. 25.
    Peano, G.: Sur une Courbe Qui Remplit Toute une Aire Plaine. Mathematische Annalen 36, 157–160 (1890)CrossRefMathSciNetGoogle Scholar
  26. 26.
    Ralston, B.: GIS and ITS Traffic Assignment: Issues in Dynamic User-Optimal Assignments. GeoInformatica 4(2) (June 2000)Google Scholar
  27. 27.
    Rigaux, P., Scholl, M., Voisard, A.: Spatial Databases with Application to GIS. Morgan Kaufmann, San Francisco (2002)Google Scholar
  28. 28.
    Speičys, L., Jensen, C., Kligys, A.: Computational Data Modeling for Network-Constrained Moving Objects. In: Proc. of the 11th ACM Intl. Workshop on Advances in Geographic Information Systems (ACM-GIS 2003), New Orleans (November 2003)Google Scholar
  29. 29.
    Southworth, F., Peterson, B.: Intermodal and International Freight Network Modeling. Transportation Research Part C 8 (2000)Google Scholar
  30. 30.
    Winter, S.: Modeling Costs of Turns in Route Planning. GeoInformatica 6(4) (2002)Google Scholar
  31. 31.
    Zeiler, M.: Modeling Our World: The ESRI Guide to Geodatabase Design. ESRI Press, Redlands (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Erik G. Hoel
    • 1
  • Wee-Liang Heng
    • 1
  • Dale Honeycutt
    • 1
  1. 1.Environmental Systems Research InstituteRedlandsUSA

Personalised recommendations