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Encoding Travel Traces by Using Road Networks and Routing Algorithms

  • Pablo Martinez Lerin
  • Daisuke Yamamoto
  • Naohisa Takahashi
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 14)

Abstract

Large numbers of travel traces are collected by vehicles and stored for applications such as optimizing delivery routes, predicting and avoiding traffic, and providing directions. Many of the applications preprocess the travel traces, usually composed of position data, by matching these with links in the underlying road network. This paper addresses the problem of persistent storage of large numbers of vehicle travel traces. We propose two methods for using a routing algorithm and road network to encode a travel trace formed by a sequence of links. An encoded trace, composed of a few links, is useful to store or share and can be decoded into the original travel trace. Considering that drivers tend to proceed from an origin to a destination by using the shortest path or going as straight as possible, the two proposed methods use the following two routing algorithms: a shortest path algorithm; and a following path algorithm, which finds the path that avoids turns. The experimental results for 30 real traces show that a travel trace is encoded into only 5% or 7% of its links on average using the shortest path algorithm or the following path algorithm, respectively.

Keywords

GIS travel trace travel data encoding shortest path following path 

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References

  1. 1.
    Xue, G., Li, Z., Zhu, H., Liu, Y.: Traffic-known urban vehicular route prediction based on partial mobility patterns. In: Proc. ICPADS, pp. 369–375 (2009)Google Scholar
  2. 2.
    Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., Huang, Y.: T-drive: driving directions based on taxi trajectories. In: Proc. GIS, pp. 99–108 (2010)Google Scholar
  3. 3.
    McMaster, R.B.: Automated line generalization. Cartographica 24(2), 74–111 (1987)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Leu, J.G., Chen, L.: Polygonal approximation of 2-D shapes through boundary merging. Pattern Recognition Letters 7(4), 231–238 (1988)CrossRefGoogle Scholar
  5. 5.
    Brakatsoulas, S., Pfoser, D., Salas, R., Wenk, C.: On map-matching vehicle tracking data. In: Proc. 31st Int’l Conf. on Very Large Data Bases (VLDB 2005), pp. 853–864 (2005)Google Scholar
  6. 6.
    Yuan, J., Zheng, Y., Zhang, C., Xie, X., Sun, G.-Z.: An interactive-voting based map matching algorithm. In: Proc. 11th Int’l Conf. on Mobile Data Management (MDM), pp. 43–52 (2010)Google Scholar
  7. 7.
    Cao, H., Wolfson, O., Trajcevski, G.: Spatio-temporal data reduction with deterministic error bounds. VLDB Journal 15(3), 211–228 (2006)CrossRefGoogle Scholar
  8. 8.
    Hönle, N., Grossmann, M., Reimann, S., Mitschang, B.: Usability analysis of compression algorithms for position data streams. In: Proc. 18th ACM SIGSPATIAL Int’l Conf. on Advances in Geographic Information Systems, pp. 240–249 (2010)Google Scholar
  9. 9.
    Takahashi, N.: An elastic map system with cognitive map-based operations. In: International Perspectives on Maps and Internet. Lecture Notes in Geoinformation and Cartography, pp. 73–87 (2008)Google Scholar
  10. 10.
    Yamamoto, D., Ozeki, S., Takahashi, N.: Focus+Glue+Context: an improved fisheye approach for web map services. In: Proc. 17th ACM SIGSPATIAL Int’l Conf. on Advances in Geographic Information Systems, pp. 101–110 (2009)Google Scholar
  11. 11.
    Dijkstra, E.W.: A note on two problems in connection with graph theory. Numerische Mathematik 1, 269–271 (1959)MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    Idwan, S., Etaiwi, W.: Dijkstra algorithm heuristic approach for large graph. Journal of Applied Sciences 11, 2255–2259 (2011)CrossRefGoogle Scholar
  13. 13.
    Cho, H.-J., Lan, C.-L.: Hybrid shortest path algorithm for vehicle navigation. Journal of Supercomputing 49(2), 234–247 (2009)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Huang, Y.-W., Jing, N., Rundensteiner, E.A.: A semi-materialized view approach for route maintenance in IVHS. In: Proc. 2nd ACM Workshop on Geographic Information Systems, pp. 144–151 (1994)Google Scholar
  15. 15.
    Huang, Y.-W., Jing, N., Rundensteiner, E.A.: A hierarchical path view model for path finding in intelligent transportation systems. GeoInformatica 1(2), 125–159 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pablo Martinez Lerin
    • 1
  • Daisuke Yamamoto
    • 1
  • Naohisa Takahashi
    • 1
  1. 1.Dept. of Computer Science and EngineeringNagoya Institute of TechnologyNagoyaJapan

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