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Fast Map Matching with Vertex-Monotone Fréchet Distance

Authors Daniel Chen, Christian Sommer, Daniel Wolleb

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Daniel Chen
  • Apple, Cupertino, CA, USA
Christian Sommer
  • Apple, Cupertino, CA, USA
Daniel Wolleb
  • Apple, Cupertino, CA, USA

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Daniel Chen, Christian Sommer, and Daniel Wolleb. Fast Map Matching with Vertex-Monotone Fréchet Distance. In 21st Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2021). Open Access Series in Informatics (OASIcs), Volume 96, pp. 10:1-10:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/OASIcs.ATMOS.2021.10

Abstract

We study a generalization for map matching algorithms that includes both geometric approaches such as the Fréchet distance and global weight approaches such as those typically used by Hidden Markov Models. Through this perspective, we discovered an efficient map matching algorithm with respect to the vertex-monotone Fréchet distance while using a heuristic tie-breaker inspired by global weight methods. While the classical Fréchet distance requires parameterizations to be monotone, the vertex-monotone Fréchet distance allows backtracking within edges. Our analysis and experimental evaluations show that relaxing the monotonicity constraint enables significantly faster algorithms without significantly altering the resulting map matched paths.

Subject Classification

ACM Subject Classification
  • Theory of computation → Shortest paths
Keywords
  • Fréchet distance
  • map matching
  • minimum bottleneck path

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