Advertisement

Accuracy and Performance Assessment of a Window-Based Heuristic Algorithm for Real-Time Routing in Map-Based Mobile Applications

  • Hassan A. Karimi
  • Peter Sutovsky
  • Matej Durcik
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

The demand for routing algorithms that produce optimal solutions in real time is continually growing. Real-time routing algorithms are needed in many existing and emerging applications and services. An example is map-based mobile applications where real-time routing is required. Conventional optimal routing algorithms often do not provide acceptable real-time responses when applied to large real road network data. As a result, in certain real-time applications, especially those with limited computing resources (e.g., mobile devices), heuristic algorithms that can provide good solutions, though not necessarily optimal, in real time are employed. In this chapter, we present two approaches for limiting the search space using a window-based heuristic algorithm to compute shortest routes and analyze their solutions and performances using real road network data. The results of a set of experiments on the two approaches show that the window-based heuristic algorithm produces aceptable response times using real road network data and that window sizes and orientations impact accuracy and performance of the algorithm.

Keywords

Window Size Road Network Destination Node Road Segment Optimal Route 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Andersson, G., Francis, R. L., Normark, T., and Rayco, M. B. (1998): “Aggregation method experimentation for large-scale network location problems”. Location Science, 6, 25-39.CrossRefGoogle Scholar
  2. Bertskas, D. P., and Gallager, R. (1992): Data Networks, Englwood Cliffs, NJ: Prentice Hall.Google Scholar
  3. Bovy, P. H. L., and Stern, E. (1990): Route Choice: Wayfinding in Transport Networks, Kluwer Academic Publishers, Boston, MA.Google Scholar
  4. Chabini, I., and Shan, L. (2002): “Adaptations of the A* algorithm for the computation of fastest paths in deterministic discrete-time dynamic networks.” Intelligent Transportation Systems, IEEE Transactions on, 3(1), 60-74.CrossRefGoogle Scholar
  5. Cherkassky, B. V., Goldberg, A. V., and Radzik, T. (1996): “Shortest paths algorithms: theory and experimental evaluation.” Math. Program., 73(2), 129-174.CrossRefGoogle Scholar
  6. Fredman, M. L., and Tarjan, R. E. (1987): “Fibonacci heaps and their uses in improved network optimization algorithms.” Journal of the ACM, 34(3), 596-615.CrossRefGoogle Scholar
  7. Fu, L., Sun, D., and Rilett, L. R. (2006): “Heuristic shortest path algorithms for transportation applications: State of the art”. Computers & Operations Research Part Special Issue: Operations Research and Data Mining, 33(11), 3324-3343.Google Scholar
  8. Gallo, G., and Pallottino, S. (1988): “Shortest path algorithms”. Annals of Operations Research, V13(1), 1-79.CrossRefGoogle Scholar
  9. Huang, B., Wu, Q., and Zhan, F. B. (2006): “A Shortest Path Algorithm with Novel Heuristics for Dynamic Transportation Networks”. International Journal of Geographical Information Science.Google Scholar
  10. Jacob, R., Marathe, M. V., and Nagel, K. (1999): “A computational study of routing algorithms for realistic transportation networks”. ACM Journal of Experimental Algorithmics.vol.4; 1999.Google Scholar
  11. Jagadeesh, G. R., Srikanthan, T., and Quek, K. H. (2002): “Combining hierarchical and heuristic techniques for high-speed route computing on road networks”. Computing and Control Engineering Journal, June 2002, 120-126.Google Scholar
  12. Jung, S., and Pramanik, S. (2002): “An Efficient Path Computation Model for Hierarchically Structured Topographical Road Maps”. IEEE Transactions on Knowledge and Data Engineering, 14(5), 1029-1046.CrossRefGoogle Scholar
  13. Karimi, H. A. (1996): “Real-time optimal-route computation: A heuristic approach”. ITS Journal, 3(2), 111-127.Google Scholar
  14. Kim, S., Lewis, M. E., and White, C. C. (2005a): “State Space Reduction for Nonstationary Stochastic Shortest Path Problems With Real-Time Traffic Information”. Intelligent Transportation Systems, 6(3), 273-284.CrossRefGoogle Scholar
  15. Kim, S., Lewis, M. E., and White, C. C., III. (2005b): “Optimal vehicle routing with real-time traffic information”. Intelligent Transportation Systems, IEEE Transactions on, 6(2), 178-188.CrossRefGoogle Scholar
  16. Liu, B. (1997): “Route finding by using knowledge about the road network”. IEEE Transaction on Systems , Man, and Cybernetics-Part A: Systemes and Humans, 27(4), 436-448.CrossRefGoogle Scholar
  17. Lotan, T. (1997): “Effects of familiarity on route choice behavior in the presence of information”. Transportation Research Part C: Emerging Technologies, 5(3), 225-243.CrossRefGoogle Scholar
  18. Pang, G. K. H., Takabashi, K., Yokota, T., and Takenaga, H. (1999): “Adaptive route selection for dynamic route guidance system based on fuzzy-neural approaches”. Vehicular Technology, IEEE Transactions on, 48(6), 2028-2041.CrossRefGoogle Scholar
  19. Terrovitis, M., Bakiras, S., Papadias, D., and Mouratidis, K. (2005): “Constrained Shortest Path Computation”. Advances in Spatial and Temporal Databases, 9th International Symposium, SSTD, Angra dos Reis, Brazil, 181-199.Google Scholar
  20. TIGER/Line Files. (2002a): “UA Census 2000 TIGER/Line Files Technical Documentation. <http://www.census.gov/geo/www/tiger/tigerua/ua_tgr2k.html>.” The U.S. Census Bureau, Washington DC.Google Scholar
  21. TIGER/Line Files. (2002b):. “UA Census 2000 TIGER/Line Files: machine-readable data files. < http://www.census.gov/geo/www/tiger/tigerua/ua_tgr2k.html>.” The U.S. Census Bureau, Washington DC.Google Scholar
  22. Zhao, Y., and Weymouth, T. E. (1991): “An adaptive route guidance algorithm for intelligent vehicle highway system”. American Control Conference, 3, 2568-2573.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Hassan A. Karimi
    • 1
  • Peter Sutovsky
    • 1
  • Matej Durcik
    • 2
  1. 1.School of Information SciencesUniversity of Pittsburgh
  2. 2.SAHRA-HWRUniversity of Arizona

Personalised recommendations