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Quantifying travel sequence predictability via a constrained vector-quantized variational autoencoder
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DOI：10.1016/j.trc.2026.105958.png)
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Abstract
En 中文
<ul class="list">
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="p0001">
Extend predictability from discrete states to continuous travel sequences.
</div></span></li>
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="p0002">
A constrained VQ-VAE computes the predictability–distortion curve.
</div></span></li>
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="p0003">
Validate on synthetic data against a theoretical predictability-distortion curve.
</div></span></li>
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="p0004">
Real-world mobility data validation quantifies cross-vehicle predictability heterogeneity.
</div></span></li>
</ul>
Keywords:
Predictability
VQ-VAE
Electric vehicle
Information-theoretic metrics
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