ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 19-23, 2015
https://doi.org/10.5194/isprsannals-II-4-W2-19-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
10 Jul 2015
PROBE VEHICLE TRACK-MATCHING ALGORITHM BASED ON SPATIAL SEMANTIC FEATURES
Y. Luo1,2, X. Song2, L. Zheng3, C. Yang4, M. Yu4, and M. Sun3 1School of Resource and Environmental Sciences, Wuhan University, China
2Wuhan KOTEI Infomatics Co., Ltd. China
3School of Geodesy and Geomatics, Wuhan University, China
4Department of Geography and GeoInformation Sciences, College of Science, George Mason University, Fairfax, VA 22030, USA
Keywords: Spatial Semantic Features, Probe Vehicle Path, Spatial Data Mining, Global Matching Model, Track Abstract. The matching of GPS received locations to roads is challenging. Traditional matching method is based on the position of the GPS receiver, the vehicle position and vehicle behavior near the receiving time. However, for probe vehicle trajectories, the sampling interval is too sparse and there is a poor correlation between adjacent sampling points, so it cannot partition the GPS noise through the historical positions. For the data mining of probe vehicle tracks based on spatial semantics, the matching is learned from the traditional electronic navigation map matching, and it is proposed that the probe vehicle track matching algorithm is based on spatial semantic features. Experimental results show that the proposed global-path matching method gets a good matching results, and restores the true path through the probe vehicle track.
Conference paper (PDF, 2540 KB)


Citation: Luo, Y., Song, X., Zheng, L., Yang, C., Yu, M., and Sun, M.: PROBE VEHICLE TRACK-MATCHING ALGORITHM BASED ON SPATIAL SEMANTIC FEATURES, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 19-23, https://doi.org/10.5194/isprsannals-II-4-W2-19-2015, 2015.

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