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The Effects of Navigation Sensors and Spatial Road Network Data Quality on the Performance of Map Matching Algorithms

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Abstract

Map matching algorithms are utilised to support the navigation module of advanced transport telematics systems. The objective of this paper is to develop a framework to quantify the effects of spatial road network data and navigation sensor data on the performance of map matching algorithms. Three map matching algorithms are tested with different spatial road network data (map scale 1:1,250; 1:2,500 and 1:50,000) and navigation sensor data (global positioning system (GPS) and GPS augmented with deduced reckoning) in order to quantify their performance. The algorithms are applied to different road networks of varying complexity. The performance of the algorithms is then assessed for a suburban road network using high precision positioning data obtained from GPS carrier phase observables. The results show that there are considerable effects of spatial road network data on the performance of map matching algorithms. For an urban road network, the results suggest that both the quality of spatial road network data and the type of navigation system affect the link identification performance of map matching algorithms.

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Notes

  1. Map scale is defined simply as the ratio of distance on a map over the corresponding distance on the ground, represented as 1:M where M is the scale denominator. Map scale is an issue because as scale becomes larger the amount of detail that can be presented in a map is also increased. The ability to measure the length of linear features on the ground (road centreline), the position of point features (junctions and roundabouts), and the areas of polygons with a high level of accuracy are also increased. Clearly there is a dependence between quality of systems/sensors used to create a map and map accuracy. Therefore, scale should reflect this.

  2. Vendor supplied accuracy data would give a further improvement, but was unavailable.

References

  1. J.B. Bullock, and E.J. Krakiwsky. “Analysis of the use of digital road maps in vehicle navigation,” IEEE Symposium on Position Location and Navigation, Las Vegas, NV, 11–15 April, pp. 494–501, 1994.

  2. W. Chen, M. Yu, Z.-L. Li and Y.-Q. Chen. “Integrated vehicle navigation system for urban applications,” in Proceedings of the 7th International Conference on Global Navigation Satellite Systems (GNSS), European Space Agency, Graz, Austria, April 22–24, 2003, pp. 15–22, 2003.

  3. N.R. Chrismans. “Modelling error in overlaid categorical maps, Section II,” in M.F. Goodchild and S. Gopel (Ed.), The Accuracy of Spatial Database, 21–34, 1994.

  4. J.A. Farrell and M. Barth. The Global Positioning System and Inertial Navigation. McGraw-Hill: New York, 1999.

    Google Scholar 

  5. C. Goodwin and J. Lau. “Vehicle navigation and map quality,” IEEE-IEE Vehicle Navigation & Information Systems Conference, Ottawa, , pp. 17–20, 1993.

  6. J.S. Greenfeld. “Matching GPS observations to locations on a digital map,” in Proceedings of the 81st Annual Meeting of the Transportation Research Board, January, Washington D.C., 2002.

  7. C. Hide, T. Moore, C. Hill, and D. Park. “Low cost, high accuracy positioning in urban areas,” Journal of Navigation, Vol. 59(3):365–379, 2006.

    Article  Google Scholar 

  8. B. Hoffmann-Wellenhof, J. Collins, and H. Lichtenegger. GPS Theory and Practice. Springer: New York, 1997.

    Google Scholar 

  9. N.J. Hotchkiss. A Comprehensive Guide to Land Navigation with GPS. 3rd edition, Alexis Pub: Herndon, 1999.

    Google Scholar 

  10. E.D. Kaplan. Understanding GPS: Principles and Applications. Artech House: London, 1996.

    Google Scholar 

  11. J.S. Kim, J.H. Lee, T.H. Kang, W.Y. Lee and Y.G. Kim. “Node based map matching algorithm for car navigation system,” in Proceeding of the 29th ISATA Symposium, Florence, 10, pp 121–126, 1996.

  12. W. Kim, G.-I. Jee and J. Lee. “Efficient use of digital road map in various positioning for ITS,” in IEEE Symposium on Position Location and Navigation, San Diego, CA, , pp. 170–176, March, 2000.

  13. M.E. El Najjar and P. Bonnifait. “A Road-matching method for precise vehicle localization using Kalman filtering and belief theory”, Journal of Autonomous Robots, Vol. 19(2):173–191, 2005 S.I. on Robotics Technologies for Intelligent Vehicles, Kluwer Academic Publishers.

    Article  Google Scholar 

  14. V. Noronha and M.F. Goodchild. “Map accuracy and location expression in transportation-reality and prospects ”, Transportation Research C, Vol. 8:53–69, 2000.

    Article  Google Scholar 

  15. NRC, National Research Council. “Collecting, processing and integrating GPS data with GIS,” NCHRP Synthesis 301, National Academy Press; Washington D.C., 2002.

  16. W.Y. Ochieng, M.A. Quddus, and R.B. Noland. “Map-matching in complex urban road networks”, Brazilian Journal of Cartography (Revista Brasileira de Cartografia), Vol. 55(2):1–18, 2003.

    Google Scholar 

  17. J. Pyo, D. Shin and T. Sung. “Development of a map matching method using the multiple hypothesis technique,” in IEEE Proceedings on Intelligent Transportation Systems, pp. 23–27, 2001.

  18. M.A. Quddus. “High integrity map matching algorithms for advanced transport telematics applications,” Ph.D. Thesis, Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College London, 2006.

  19. M.A. Quddus, W.Y. Ochieng, L. Zhao, and R.B. Noland. “A general map matching algorithm for transport telematics applications”, GPS Solutions, Vol. 7(3):157–167, 2003.

    Article  Google Scholar 

  20. M.A. Quddus, R.B. Noland, and W.Y. Ochieng. “Validation of map matching algorithms using high precision positioning with GPS”, The Journal of Navigation, Royal Institute of Navigation, Vol. 58(2):257–271, 2005.

    Google Scholar 

  21. M.A. Quddus, R.B. Noland and W.Y. Ochieng. “A fuzzy logic map matching algorithm for road transport,” Accepted for publication in the Journal of Intelligent Transportation Systems: Technology, Planning, Operations, 2006.

  22. J. Stephen and G. Lachapelle. “Development of a GNSS-based multi-sensor vehicle navigation system”, The Journal of Navigation, Royal Institute of Navigation, Vol. 54(2):297–319, 2001.

    Google Scholar 

  23. S. Syed and M.E. Cannon. “Fuzzy logic-based map matching algorithm for vehicle navigation system in urban canyons,” in Proceedings of the Institute of Navigation (ION) national technical meeting, California, USA, 26–28 January, 2004.

  24. G. Taylor, C. Brunsdon, J. Li, A. Olden, D. Setup and M. Winter. “A test-bed simulator for GPS and GIS integrated navigation and positioning research: bus positioning using GPS observations, odometer readings and map matching,” in Proceedings of the 12th International Conference on Geoinformatics, June 7–9, pp. 31–38, 2004.

  25. C.E. White, D. Bernstein and A.L. Kornhauser. “Some map matching algorithms for personal navigation assistants,” Transportation Research Part C: Emerging Technologies, Vol. 8:91–108, 2000.

    Article  Google Scholar 

  26. D. Yang, B. Cai, and Y. Yuan. “An improved map-matching algorithm used in vehicle navigation system”, IEEE Proceedings on Intelligent Transportation Systems, Vol. 2:1246–1250, 2003.

    Google Scholar 

  27. X. Zhang, Q. Wang and D. Wan. “The relationship among vehicle positioning performance, map quality, and sensitivities and feasibilities of map-matching algorithms,” in Proceedings of IEEE on Intelligent Vehicles Symposium, 9–11 June, pp. 468–473, 2003.

  28. Y. Zhao. Vehicle Location and Navigation Systems. Artech House: Norwood, 1997.

    Google Scholar 

  29. L. Zhao, W.Y. Ochieng, M.A. Quddus, and R.B. Noland. “An extended Kalman filter algorithm for integrating GPS and low-cost dead reckoning system data for vehicle performance and emissions monitoring”, The Journal of Navigation, Royal Institute of Navigation, Vol. 56(2):257–275, 2003.

    Google Scholar 

  30. US DoD. Global Positioning System Standard Positioning Service Performance Standard. Assistant Secretary of Defense for Command, Control, Communications, and Intelligence: Washington, DC, 2001.

    Google Scholar 

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Acknowledgements

The authors would like to thank Robin North and Shaojun Feng (both of the Centre for Transport Studies at Imperial College London) for their assistance during the data collection process.

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Correspondence to Mohammed A. Quddus.

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Quddus, M.A., Noland, R.B. & Ochieng, W.Y. The Effects of Navigation Sensors and Spatial Road Network Data Quality on the Performance of Map Matching Algorithms. Geoinformatica 13, 85–108 (2009). https://doi.org/10.1007/s10707-007-0044-x

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Keywords

Navigation