Time-variant road network-based bridgelets

Location measurements from people are very often sparsely sampled due to power constraints or as an attempt at location privacy. However, we would still like to reason about location changes between samples in order to infer visits or understand moving behavior. In this work, we present a method for...

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Bibliographic Details
Published in2023 24th IEEE International Conference on Mobile Data Management (MDM) pp. 265 - 273
Main Authors Anastasiou, Chrysovalantis, Krumm, John, Shahabi, Cyrus
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2023
Subjects
bridge
gps
Heuristic algorithms
Image color analysis
interpolation
Power measurement
Privacy
Roads
spatiotemporal
trajectories
Uncertainty
Visualization
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Summary:Location measurements from people are very often sparsely sampled due to power constraints or as an attempt at location privacy. However, we would still like to reason about location changes between samples in order to infer visits or understand moving behavior. In this work, we present a method for representing this location uncertainty while constraining the moving object to the road network, which is more realistic and precise for human mobility. Unlike the most straightforward method, fastest path, our method explicitly represents the location uncertainty between location measurements with probabilities. We introduce road network-based bridgelets, which are spatiotemporal probability clouds that model the location uncertainty between two endpoints, and we propose an algorithm, APD*, to generate bridgelets efficiently. In our experimental section, we evaluate the performance of APD* and provide visual examples to compare its output with other baseline methods.
ISSN:2375-0324
DOI:10.1109/MDM58254.2023.00050