The weighted region problem: finding shortest paths through a weighted planar subdivision
Journal of the ACM (JACM)
Hidden Markov map matching through noise and sparseness
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Map-matching for low-sampling-rate GPS trajectories
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Tag configuration matcher for geo-tagging
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Map matching with inverse reinforcement learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.