33 lines
2.4 KiB
Markdown
33 lines
2.4 KiB
Markdown
---
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layout: single
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title: "Autoencoder Trajectory Compression"
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categories: research
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tags: deep-learning recurrent-neural-networks trajectory-analysis data-compression geoinformatics
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excerpt: "LSTM autoencoder better DP for trajectory compression (Fréchet/DTW)."
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header:
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teaser: /assets/figures/23_trajectory_model.png
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scholar_link: "https://scholar.google.de/citations?user=NODAd94AAAAJ&hl=en"
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---
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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.
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<center>
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<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%">
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<figcaption>Schematic of the LSTM Decoder Architecture.</figcaption>
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</center>
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<br>
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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.
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{:style="display:block; width:50%" .align-right}
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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**.
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**Key findings:**
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* 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)**.
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* 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.
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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 %}
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