Volume II-4/W2
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.
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

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 Y. Luo et al.
  • 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.