Data-Driven Trajectory Smoothing

Abstract : Motivated by the increasing availability of large collections of noisy GPS traces, we present a new data-driven framework for smoothing trajectory data. The framework, which can be viewed of as a generalization of the classical moving average technique, naturally leads to efficient algorithms for various smoothing objectives. We analyze an algorithm based on this framework and provide connections to previous smoothing techniques. We implement a variation of the algorithm to smooth an entire collection of trajectories and show that it perform well on both synthetic data and massive collections of GPS traces.
Type de document :
Communication dans un congrès
19th SIGSPATIAL International Conference on Advances in Geographic Information Systems, Nov 2011, Chicago, United States. ACM, 2011, Proceedings of the 19th SIGSPATIAL International Conference on Advances in Geographic Information Systems
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https://hal.inria.fr/inria-00636144
Contributeur : Frédéric Chazal <>
Soumis le : mercredi 26 octobre 2011 - 19:33:44
Dernière modification le : vendredi 23 février 2018 - 14:20:08

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  • HAL Id : inria-00636144, version 1

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Frédéric Chazal, Daniel Chen, Leonidas J. Guibas, Xiaoye Jiang, Christian Sommer. Data-Driven Trajectory Smoothing. 19th SIGSPATIAL International Conference on Advances in Geographic Information Systems, Nov 2011, Chicago, United States. ACM, 2011, Proceedings of the 19th SIGSPATIAL International Conference on Advances in Geographic Information Systems. 〈inria-00636144〉

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