Advertisement

GIS-based Map-matching: Development and Demonstration of a Postprocessing Map-matching Algorithm for Transportation Research

  • Ron DalumpinesEmail author
  • Darren M. Scott
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC, volume 1)

Abstract

This paper presents a GIS-based map-matching algorithm that makes use of geometric, buffer, and network functions in a GIS – to illustrate the suitability of a GIS platform in developing a postprocessing mapmatching algorithm for transportation research applications such as route choice analysis. This algorithm was tested using a GPS-assisted time-use survey that involved nearly 2,000 households in Halifax, Nova Scotia, Canada. Actual routes taken by household members who travelled to work by car were extracted using the GPS data and the GIS-based map-matching algorithm. The algorithm produced accurate results in a reasonable amount of time. The algorithm also generated relevant route attributes such as travel time, travel distance, and number of left and right turns that serve as explanatory variables in route choice models. The ease and flexibility of the Python scripting language used in developing the GIS-based mapmatching algorithm make this tool easy to develop and implement. It can be improved to suit data inputs and specific fields of application in transportation research.

Keywords

Global Position System Transportation Research Route Choice Global Position System Data Short Path Algorithm 
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. Bel Hadj Ali, A. (1997) Appariement geometrique des objets géographiques et étude des indicateurs de qualité. Saint-Mandé (Paris), Laboratoire COGIT.Google Scholar
  2. Brakatsoulas, S., Pfoser, D., Salas, R. and Wenk, C. (2005) On Map-Matching Vehicle Tracking Data. VLDB 2005, pp. 853-864.Google Scholar
  3. Bricka, S. (2008) Non-Response Challenges in GPS-Based Surveys, paper at the 8th International Conference on Travel Survey Methods, May 2006, Annecy, France.Google Scholar
  4. Cao, H. and Wolfson, O. (2005) Nonmaterialized Motion Information in Transport Networks. ICDT 2005, pp. 173-188.Google Scholar
  5. Casas, J., and Arce, C. H. (1999) Trip Reporting in Household Travel Diaries: A Comparison to GPS-Collected Data. Presented at 78th Annual Meeting of the Transportation Research Board, Washington, D.C.Google Scholar
  6. Chung, E., and Shalaby, A. (2005) A trip reconstruction tool for GPS-based personal travel surveys. Transportation Planning and Technology, Vol. 28, No. 5, pp. 381-401.CrossRefGoogle Scholar
  7. Devogele, T. (2002) A new merging process for data integration based on the discrete Frechet distance. In Advances in Spatial Data Handling, D. Richardson and P. van Oosterom. New York, Springer Verlag, pp. 167-181.Google Scholar
  8. Doherty, S. (2001) Meeting the Data Needs of Activity Scheduling Process Modeling and Analysis. Presented at 80th Annual Meeting of the Transportation Research Board, Washington, D.C.Google Scholar
  9. Draijer, G., Kalfs, N. and Perdok, J. (2000) Global Positioning System as Data Collection Method for Travel Research. In Transportation Research Record: Journal of the Transportation Research Board, No. 1719, TRB, National Research Council, Washington, D.C., pp. 147–153.Google Scholar
  10. Greenfeld, J. S. (2002) Matching GPS observations to locations on a digital map. Papers presented at the 81th Annual Meeting of the Transportation Research Board. CD-ROM. January 2002, Washington, DC.Google Scholar
  11. Harvey, F. (1994) Defining unmoveable nodes/segments as part of vector overlay: The alignment overlay. In Advances in GIS Research, T. C. Waugh and R. C. Healey. London, Taylor and Francis, 1, pp. 159-176.Google Scholar
  12. Harvey, F. (2005) Aligning or Matching: Cartographic Perspectives on Geographic Integration. AutoCarto 2005, Las Vegas, NV, ACSM.Google Scholar
  13. Harvey, F. and Vauglin, F. (1996a) Geometric match processing: Applying Multiple Tolerances. The Seventh International Symposium on Spatial Data Handling (SDH'96), Delft, Holland, International Geographical Union (IGU).Google Scholar
  14. Harvey, F. and Vauglin, F. (1996b) Geometric match processing: Applying Multiple Tolerances. In Advances in GIS Research, Proceedings of the Seventh International Symposium on Spatial Data Handling, M. J. Krakk and M. Molenaar. London, Taylor & Francis, 1, pp. 155-171.Google Scholar
  15. Lemarié, C. and Raynal, L. (1996) Geographic data matching: First investigations for a generic tool. GIS/LIS '96, Denver, Co, ASPRS/AAG/URISA/AM-FM.Google Scholar
  16. Lou, Y., Xie, X., Zhang, C., Wang, W., Zheng, Y. and Huang, Y. (2009) Mapmatching for low-sampling-rate GPS trajectories. In Proceedings of the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS), pp. 544-545.Google Scholar
  17. Marchal, F., Hackney, J. and Axhausen, K.W. (2005) Efficient map matching of large global positioning system data sets. In Transportation Research Record: Journal of the Transportation Research Board, No. 1935, Transportation Research Board of the National Academies, Washington, D.C., pp. 93-100.Google Scholar
  18. Murakami, E., and Wagner, D.P. (1999) Can Using Global Positioning System (GPS) Improve Trip Reporting? Transportation Research Part C, Vol. 7, pp. 149–165.CrossRefGoogle Scholar
  19. Ogle, J., Guensler, R., Bachman, W., Koutsak, M. and Wolf, J. (2002) Accuracy of Global Positioning System for Determining Driver Performance Parameters. In Transportation Research Record: Journal of the Transportation Research Board: No. 1818, Transportation Research Board of the National Academies, Washington, D.C., pp. 12–24.Google Scholar
  20. Pearson, D. (2001) Global Positioning System (GPS) and Travel Surveys: Results from the 1997 Austin Household Survey. Presented at 8th Conference on the Application of Transportation Planning Methods, April 2001, Corpus Christi, Texas.Google Scholar
  21. Quddus, M. A., Ochieng, W.Y. and Noland, R.B. (2007) Current map-matching algorithms for transport application: State-of-the-art and future research directions. Transportation Research Part C, Vol. 15, pp. 312-328.CrossRefGoogle Scholar
  22. Quddus, M. A., Ochieng, W.Y., Zhao, L. and Noland, R.B. (2003) A general map matching algorithm for transport telematics applications. GPS Solutions, Vol. 7, pp. 157-167.CrossRefGoogle Scholar
  23. Schuessler, N. and Axhausen, K.W. (2009) Processing raw data from Global Positioning Systems without Additional Information. In Transportation Research Record: Journal of the Transportation Research Board, No. 2105, Transportation Research Board of the National Academies, Washington, D.C., pp. 28-36.Google Scholar
  24. Taylor, G., Brunsdon, C., Li, J., Olden, A., Steup, D. and Winter, M. (2006) GPS accuracy estimation using map matching techniques: Applied to vehicle positioning and odometer calibration. Computers, Environment and Urban Systems, 30, pp. 757-772.CrossRefGoogle Scholar
  25. Vauglin, F. and Bel Hadj Ali, A. (1998) Geometric matching of polygonal surfaces in GISs. ASPRS Annual Meeting, Tampa, FL, ASPRS.Google Scholar
  26. Wagner, D. P. (1997) Lexington Area Travel Data Collection Test: GPS for Personal Travel Surveys. Final Report. Office of Highway Policy Information and Office of Technology Applications, Battelle Transport Division, FHWA, Sept. 1997, Columbus, Ohio.Google Scholar
  27. Walter, V. and Fritsch, D. (1999) Matching spatial data sets: a statistical approach. International Journal of Geographic Information Science, 13(5), pp. 445-473.CrossRefGoogle Scholar
  28. White, C. E., Berstein, D. and Kornhauser, A.L. (2000) Some map matching algorithms for personal navigation assistants. Transportation Research Part C, 8, pp. 91-108.CrossRefGoogle Scholar
  29. Wolf, J., Hallmark, S., Oliveira, M., Guensler, R. and Sarasua, W. (1999) Accuracy Issues with Route Choice Data Collection by Using Global Positioning System. In Transportation Research Record: Journal of the Transportation Research Board, No. 1660, TRB, National Research Council, Washington, D.C., pp. 66–74.Google Scholar
  30. Yalamanchili, L., Pendyala, R.M., Prabaharan, N. and Chakravarty, P. (1999) Analysis of Global Positioning System-Based Data Collection Methods for Capturing Multistop Trip-Chaining Behavior. In Transportation Research Record: Journal of the Transportation Research Board, No. 1660, TRB, National Research Council, Washington, D.C., pp. 58–65.Google Scholar
  31. Zhou, J. (2005) A three-step general map matching method in the GIS environment: Travel/transportation study perspective. UCGIS Summer Assembly 2005. Wyoming. http://www.ucgis.org/summer2005/studentpapers.htm. Last date accessed 07.2010.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.TransLAB: Transportation Research Lab, School of Geography & Earth SciencesMcMaster UniversityHamiltonCanada

Personalised recommendations