Map matching is a common task when analysing GPS tracks, such as vehicle trajectories. The goal is to match a recorded noisy polygonal curve to a path on the map, usually represented as a geometric graph. The Fréchet distance is a commonly used metric for curves, making it a natural fit. The map-matching problem is well-studied, yet until recently no-one tackled the data structure question: preprocess a given graph so that one can query the minimum Fréchet distance between all graph paths and a polygonal curve. Recently, Gudmundsson, Seybold, and Wong [Gudmundsson et al., 2023] studied this problem for arbitrary query polygonal curves and c-packed graphs. In this paper, we instead require the graphs to be λ-low-density t-spanners, which is significantly more representative of real-world networks. We also show how to report a path that minimises the distance efficiently rather than only returning the minimal distance, which was stated as an open problem in their paper.
@InProceedings{buchin_et_al:LIPIcs.SoCG.2024.27, author = {Buchin, Kevin and Buchin, Maike and Gudmundsson, Joachim and Popov, Aleksandr and Wong, Sampson}, title = {{Map-Matching Queries Under Fr\'{e}chet Distance on Low-Density Spanners}}, booktitle = {40th International Symposium on Computational Geometry (SoCG 2024)}, pages = {27:1--27:15}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-316-4}, ISSN = {1868-8969}, year = {2024}, volume = {293}, editor = {Mulzer, Wolfgang and Phillips, Jeff M.}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2024.27}, URN = {urn:nbn:de:0030-drops-199723}, doi = {10.4230/LIPIcs.SoCG.2024.27}, annote = {Keywords: Map Matching, Fr\'{e}chet Distance, Data Structures} }
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