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A Virtual Differential Map-Matching Algorithm with Improved Accuracy and Computational Efficiency

Published online by Cambridge University Press:  26 June 2008

Hongchao Liu ,
Hao Xu ,
H. Scott Norville  and
Yuanlu Bao
Hongchao Liu*
Affiliation:
(Texas Tech University, Lubbock)
Hao Xu
Affiliation:
(Texas Tech University, Lubbock)
H. Scott Norville
Affiliation:
(Texas Tech University, Lubbock)
Yuanlu Bao
Affiliation:
(University of Technology and Science of China)
*
(Email: hongchao.liu@ttu.edu)
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Abstract

This paper presents development and application of a real-time virtual differential map-matching approach which makes use of the slow drifting property of the GPS errors and the continuous and gradual evolving characteristic of map errors to improve the accuracy and computational efficiency. A differential vector is created to approximate the real-time deviation, which is corrected continuously along with the vehicle movement during the map-matching process. Real-life application of the algorithm to the City of Hefei, a metropolis of China, shows that it corrects both GPS errors and digital map errors reasonably well with improved computational efficiency.

Keywords

Map-matchingGPSVirtual Differential Correction

Information

Type
Research Article
Information
The Journal of Navigation , Volume 61 , Issue 3 , July 2008 , pp. 421 - 434
Copyright
Copyright © The Royal Institute of Navigation 2008

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References

REFERENCES

Bao, Y., Liu, Z. (2006). GPS, traffic monitoring and digital map. China National Defense Industry Press.Google Scholar
Bao, Y., Wang, S.-G., Liu, Y. (2004). GIS-based real-time correction of deviation on vehicle tracks. Proceedings of International Conference on Dynamics, Instrumentation and Control, Nanjing, China, pp. 336–344.CrossRefGoogle Scholar
Blackwell, E. G. (1986). Overview of differential GPS methods. Global Positioning Systems, the Institute of Navigation, 3, pp. 89–100.Google Scholar
Chen, W., Li, Z., Yu, M., and Chen, Y. (2005) Effects of sensor errors on the performance of map-matching. The Journal of Navigation, 58, pp. 273–282.CrossRefGoogle Scholar
Greenfeld, J. S. (2002). Matching GPS observations to locations on a digital map. 81st Transportation Research Board Annual Meeting, Washington, D.C.Google Scholar
Hide, C., Moore, T., and Smith, M. (2001). Adaptive Kalman filtering algorithms for Low-cost INS/GPS. The Journal of Navigation, 56, pp. 143–152.CrossRefGoogle Scholar
Honey, S. K., Zavoli, W. B., Milnes, K. A., Phillips, A. C., White, M. S., Loughmiller, G. E. (1989). Vehicle navigational system and method, United States Patent No., 4796191.Google Scholar
Jagadeesh, G. R., Srikanthan, T., and Zhang, X. (2004). A map-matching method for GPS based real-time vehicle location. The Journal of Navigation, 57, pp. 429–440.CrossRefGoogle Scholar
Jo, T., Haseyamai, M., Kitajima, H. (1996). A map-matching method with the innovation of the Kalman filtering. IEICE Trans. Fund. Electron. Comm. Comput. Sci. E79-A, pp. 1853–1855.Google Scholar
Kim, J. S., Lee, J. H., Kang, T. H., Lee, W. Y., Kim, Y. G. (1996). Node based map-matching algorithm for car navigation system: Proceedings of the 29th International Symposium on Automotive Technology and Automation, Florence, Italy, 10, pp. 121–126.Google Scholar
Kim, S., Kim, J. (2001). Adaptive fuzzy-network based C-measure map-matching algorithm for car navigation system, IEEE Transactions on industrial electronics, 48 (2), 432–440.Google Scholar
Najjar, M. E., Bonnifait, P. (2003). A roadmap-matching method for precise vehicle Localization using belief theory and Kalman filtering. The 11th International Conference in Advanced Robotics, Coimbra, Portugal, June 30–July 3.Google Scholar
Quddus, M. A., Noland, R. B., and Ochieng, W. Y. (2006). A high accuracy fuzzy logic based map-matching algorithm for road transport. Journal of Intelligent Transportation Systems, 10(3), pp. 103–115.CrossRefGoogle Scholar
Quddus, M. A., Ochieng, W. Y., and Noland, R. B. (2007). Current map-matching algorithms for transport applications: state-of-the art and future research directions. Proceedings of Transportation Research Board Annual Meeting, Washington, D.C.CrossRefGoogle Scholar
Ochieng, W. Y., Sauer, K. (2001). Urban road transport navigation requirements: performance of the Global Positioning System after selective availability. Transportation Research Part C, 10, pp. 171–187.CrossRefGoogle Scholar
Rajashri, R. Joshi. (2001). A new approach to map-matching for in-vehicle navigation systems: the rotational variation metric. Proceedings of IEEE Intelligent Transportation System Conference, Oakland, USA, pp. 33–38.Google Scholar
Shi, W. and Zhu, C. (2002). The line segment match method for extracting road network from high-resolution satellite images. IEEE Trans. Geoscience and Remote Sensing, 40, pp. 511–514.Google Scholar
Syed, S., Cannon, M. E. (2004). Fuzzy logic-based map-matching algorithm for vehicle navigation system in urban canyons. Proceedings of the Institute of Navigation (ION) national technical meeting, January 26–28, California, USA.Google Scholar
Taylor, G., Blewitt, G., Steup, D., Corbett, S., Car, A. (2001). Road reduction filtering for GPS-GIS navigation, Transactions in GIS, 5(3), pp. 193–207.CrossRefGoogle Scholar
White, C. E., Bernstein, D., Kornhauser, A. L. (2000). Some map-matching algorithms for personal navigation assistants. Transportation Research Part C, 8, pp. 91–108.CrossRefGoogle Scholar
Yang, D., Cai, B., Yuan, Y. (2003). An improved map-matching algorithm used in vehicle navigation system. IEEE Proceedings on Intelligent Transportation Systems, 2, 1246–1250.Google Scholar