Compression of GPS Trajectories Using Autoencoders

Kölle, Michael; Illium, Steffen; Hahn, Carsten; Schauer, Lorenz; Hutter, Johannes und Linnhoff-Popien, Claudia (2023): Compression of GPS Trajectories Using Autoencoders. ICAART 2023: International Conference on Agents and Artificial Intelligence, Lissabon, Portugal, 22. - 24. Februar 2023. Rocha, Ana Paula; Steels, Luc und Herik, Jaap van den (Hrsg.): In: Proceedings of the 15th International Conference on Agents and Artificial Intelligence, Bd. 3 Setúbal: SciTePress. S. 829-836

Abstract

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.

• Dokumententyp: Konferenzbeitrag (Paper)
• Keywords: Trajectory Compression ; Autoencoder Model ; LSTM Networks ; Location Data
• Fakultät: Mathematik, Informatik und Statistik > Informatik > Künstliche Intelligenz und Maschinelles Lernen
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISBN: 978-989-758-623-1
• Ort: Setúbal
• Sprache: Englisch