StarTrack: a framework for enabling track-based applications
Proceedings of the 7th international conference on Mobile systems, applications, and services
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
Contraction hierarchies: faster and simpler hierarchical routing in road networks
WEA'08 Proceedings of the 7th international conference on Experimental algorithms
StarTrack next generation: a scalable infrastructure for track-based applications
OSDI'10 Proceedings of the 9th USENIX conference on Operating systems design and implementation
Accurate, low-energy trajectory mapping for mobile devices
Proceedings of the 8th USENIX conference on Networked systems design and implementation
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Large-scale joint map matching of GPS traces
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 study algorithms for matching user tracks, consisting of time-ordered location points, to paths in the road network. Previous work has focused on the scenario where the location data is linearly ordered and consists of fairly dense and regular samples. In this work, we consider the multi-track map matching, where the location data comes from different trips on the same route, each with very sparse samples. This captures the realistic scenario where users repeatedly travel on regular routes and samples are sparsely collected, either due to energy consumption constraints or because samples are only collected when the user actively uses a service. In the multi-track problem, the total set of combined locations is only partially ordered, rather than globally ordered as required by previous map-matching algorithms. We propose two methods, the iterative projection scheme and the graph Laplacian scheme, to solve the multi-track problem by using a single-track map-matching subroutine. We also propose a boosting technique which may be applied to either approach to improve the accuracy of the estimated paths. In addition, in order to deal with variable sampling rates in single-track map matching, we propose a method based on a particular regularized cost function that can be adapted for different sampling rates and measurement errors. We evaluate the effectiveness of our techniques for reconstructing tracks under several different configurations of sampling error and sampling rate.