37 lines
2.5 KiB
Plaintext
37 lines
2.5 KiB
Plaintext
---
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title: "Autoencoder Trajectory Compression"
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tags: [deep-learning, recurrent-neural-networks, trajectory-analysis, data-compression, geoinformatics, autoencoders, LSTM, GPS]
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excerpt: "LSTM autoencoder better DP for trajectory compression (Fréchet/DTW)."
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teaser: /figures/23_trajectory_model.png
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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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<CenteredImage
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src="/figures/23_trajectory_model.png"
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alt="Schematic diagram of the LSTM autoencoder model architecture used for trajectory compression"
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width={450}
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height={450}
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caption="Schematic of the LSTM Autoencoder Decoder Architecture."
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/>
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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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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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<FloatingImage
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src="/figures/23_trajectory_scores.png"
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alt="Graphs showing mean distance errors (e.g., Fréchet, DTW) for different compression ratios on the T-Drive dataset"
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width={1000}
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height={800}
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float="right"
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caption="Comparison of mean distance errors for different compression ratios on the T-Drive dataset."
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/>
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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 bibtexKey="kolle2023compression" /> |