Compression of GPS Trajectories using Autoencoders
This work addresses storage efficiency for GPS data users, but it is incremental as it applies an existing autoencoder method to a specific domain.
The paper tackles the problem of compressing GPS trajectories to reduce storage needs by proposing an LSTM-autoencoder approach, which significantly outperforms the Douglas-Peucker algorithm in terms of discrete Fréchet distance and dynamic time warping on gaming and real-world datasets.
The ubiquitous availability of mobile devices capable of location tracking led to a significant rise in the collection of GPS data. Several compression methods have been developed in order to reduce the amount of storage needed while keeping the important information. In this paper, we present an lstm-autoencoder based approach in order to compress and reconstruct GPS trajectories, which is evaluated on both a gaming and real-world dataset. We consider various compression ratios and trajectory lengths. The performance is compared to other trajectory compression algorithms, i.e., Douglas-Peucker. Overall, the results indicate that our approach outperforms Douglas-Peucker significantly in terms of the discrete Fréchet distance and dynamic time warping. Furthermore, by reconstructing every point lossy, the proposed methodology offers multiple advantages over traditional methods.