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.

@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} }