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Mathematical Problems in Engineering
Volume 2015, Article ID 717095, 13 pages
Research Article

Map Matching Based on Conditional Random Fields and Route Preference Mining for Uncertain Trajectories

1School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
2School of Civil Engineering, Tsinghua University, Beijing 100084, China

Received 14 January 2015; Revised 26 March 2015; Accepted 26 March 2015

Academic Editor: Jurgita Antucheviciene

Copyright © 2015 Ming Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In order to improve offline map matching accuracy of uncertain GPS trajectories, a map matching algorithm based on conditional random fields (CRF) and route preference mining is proposed. In this algorithm, road offset distance and the temporal-spatial relationship between the sampling points are used as features of GPS trajectory in a CRF model, which integrates the temporal-spatial context information flexibly. The driver route preference is also used to bolster the temporal-spatial context when a low GPS sampling rate impairs the resolving power of temporal-spatial context in CRF, allowing the map matching accuracy of uncertain GPS trajectories to get improved significantly. The experimental results show that our proposed algorithm is more accurate than existing methods, especially in the case of a low-sampling-rate.