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An algorithm for map matching given incomplete road data

Published: 06 November 2012 Publication History

Abstract

We consider the problem of matching a GPS trajectory with a road data set in which some roads are missing. To solve this problem, we extend a map-matching algorithm by Newson and Krumm (Proc. ACM GIS 2009, pp. 336--343) that is based on a hidden Markov model and a discrete set of candidate matches for each point of the trajectory. We introduce an additional off-road candidate for each point of the trajectory. The output path becomes determined by selecting one candidate for each point of the trajectory and connecting the selected candidates via shortest paths, which preferably lie in the road network but, if off-road candidates become selected, may also include off-road sections. We discuss experiments with GPS tracks of pedestrians.

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Y. Lou, C. Zhang, Y. Zheng, X. Xie, W. Wang, and Y. Huang. Map-matching for low-sampling-rate GPS trajectories. In Proc. 17th ACM SIGSPATIAL Internat. Conf. Advances in Geographic Information Systems (GIS '09), pages 352--361. ACM, 2009.
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P. Newson and J. Krumm. Hidden markov map matching through noise and sparseness. In Proc. 17th ACM SIGSPATIAL Internat. Conf. Advances in Geographic Information Systems (GIS '09), pages 336--343. ACM, 2009.
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cover image ACM Conferences
SIGSPATIAL '12: Proceedings of the 20th International Conference on Advances in Geographic Information Systems
November 2012
642 pages
ISBN:9781450316910
DOI:10.1145/2424321

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2012

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Author Tags

  1. GIS
  2. dynamic programming
  3. hidden Markov model
  4. map matching

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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  • (2019)Road Segment Interpolation for Incomplete Road Data2019 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BIGCOMP.2019.8679461(1-8)Online publication date: Feb-2019
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