Frederic Chazal, Daniel Chen, Leonidas Guibas, Xiaoye Jiang, and Christian Sommer. Data- driven trajectory smoothing. In Proceedings of the 19th SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS ’11, 2011.

Abstract:

Motivated by the increasing availability of large collections of noisy GPS traces, we present a new data-driven frame- work for smoothing trajectory data. The framework, which can be viewed of as a generalization of the classical mov- ing average technique, naturally leads to efficient algorithms for various smoothing objectives. We analyze an algorithm based on this framework and provide connections to pre- vious 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.

Bibtex:

@inproceedings{ccgjs-dts-11,
 author = {Chazal, Frederic and Chen, Daniel and Guibas, Leonidas and Jiang, Xiaoye and Sommer, Christian},
 title = {Data-Driven Trajectory Smoothing},
 booktitle = {Proceedings of the 19th SIGSPATIAL International Conference on Advances in Geographic Information Systems},
 series = {GIS '11},
 year = {2011},
 location = {Chicago, Illinois}
}