![]() ![]() We evaluate the effectiveness of our techniques for reconstructing tracks under several different configurations of sampling error and sampling rate. 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 also propose a boosting technique which may be applied to either approach to improve the accuracy of the estimated paths. 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. 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. 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 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. Previous work has focused on the scenario where the location data is linearly ordered and consists of fairly dense and regular samples. We study algorithms for matching user tracks, consisting of time-ordered location points, to paths in the road network.
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