# Data-Driven Trajectory Smoothing

Frédéric Chazal, Daniel Chen, Leonidas J. Guibas, Xiaoye Jiang, and Christian Sommer
GIS 2011 - 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 251-260)

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 performs well on both synthetic data and massive collections of GPS traces.

@inproceedings{CCGJS11,
author    = {Fr{\'e}d{\'e}ric Chazal
and Daniel Chen
and Leonidas J. Guibas
and Xiaoye Jiang
and Christian Sommer},
title     = {Data-driven trajectory smoothing},
booktitle = {19th ACM SIGSPATIAL International Symposium on
Advances in Geographic Information Systems (GIS)},
year      = {2011},
pages     = {251--260},
url       = {http://dx.doi.org/10.1145/2093973.2094007},
doi       = {10.1145/2093973.2094007},
}


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