rnn and gps paper added

_posts/research/2023-02-25-autoencoder-trajectory-compression.md

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+---
+layout: single
+title: "Autoencoder Trajectory Compression"
+categories: research deep-learning recurrent-neural-networks trajectory-analysis data-compression geoinformatics
+excerpt: "Introduced an LSTM autoencoder approach for GPS trajectory compression, demonstrating superior reconstruction accuracy compared to Douglas-Peucker based on Fréchet distance and DTW."
+header:
+  teaser: /assets/figures/23_trajectory_model.png
+scholar_link: "https://scholar.google.de/citations?user=NODAd94AAAAJ&hl=en"
+---
+
+The proliferation of location-aware mobile devices generates vast amounts of GPS trajectory data, necessitating efficient storage solutions. While various compression techniques aim to reduce data volume, preserving essential spatio-temporal information remains crucial.
+
+<center>
+  <img src="/assets/figures/23_trajectory_model.png" alt="Schematic diagram of the LSTM autoencoder model architecture used for trajectory compression" style="display:block; width:60%">
+  <figcaption>Schematic of the LSTM Decoder Architecture.</figcaption>
+</center>
+<br>
+
+This paper introduces a novel approach for **compressing and reconstructing GPS trajectories** using a **Long Short-Term Memory (LSTM) autoencoder**. The autoencoder learns a compressed latent representation of the trajectory sequence, which can then be decoded to reconstruct the original path.
+
+![Graphs showing mean distance errors (e.g., Fréchet, DTW) for different compression ratios on the T-Drive dataset](\assets\figures\23_trajectory_scores.png){:style="display:block; width:50%" .align-right}
+
+Our method was evaluated on two distinct datasets: one from a gaming context and another real-world dataset (T-Drive). We assessed performance across a range of compression ratios and trajectory lengths, comparing it against the widely used traditional **Douglas-Peucker algorithm**.
+
+**Key findings:**
+
+*   The LSTM autoencoder approach significantly **outperforms Douglas-Peucker** in terms of reconstruction accuracy, as measured by both **discrete Fréchet distance** and **Dynamic Time Warping (DTW)**.
+*   Unlike point-reduction techniques like Douglas-Peucker, our method performs a **lossy reconstruction at every point** along the trajectory. This offers potential advantages in maintaining temporal resolution and providing greater flexibility for downstream analysis.
+
+Experimental results demonstrate the effectiveness and potential benefits of using deep learning, specifically LSTM autoencoders, for GPS trajectory compression, offering improved accuracy over conventional geometric algorithms. {% cite kolle2023compression %}
+