Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding

TL;DR

This paper introduces MMTEC, a novel pre-training method for trajectory embeddings that reduces biases, captures travel semantics and spatio-temporal correlations, and handles complex trajectories, improving performance across various tasks.

Contribution

The paper proposes a new pretext task and dual encoder architecture for learning general, unbiased, and comprehensive trajectory embeddings from unlabeled data.

Findings

Outperforms existing trajectory embedding methods on real-world datasets

Effective in capturing travel semantics and spatio-temporal correlations

Reduces biases in pre-trained embeddings for diverse downstream tasks

Abstract

Spatio-temporal trajectories provide valuable information about movement and travel behavior, enabling various downstream tasks that in turn power real-world applications. Learning trajectory embeddings can improve task performance but may incur high computational costs and face limited training data availability. Pre-training learns generic embeddings by means of specially constructed pretext tasks that enable learning from unlabeled data. Existing pre-training methods face (i) difficulties in learning general embeddings due to biases towards certain downstream tasks incurred by the pretext tasks, (ii) limitations in capturing both travel semantics and spatio-temporal correlations, and (iii) the complexity of long, irregularly sampled trajectories. To tackle these challenges, we propose Maximum Multi-view Trajectory Entropy Coding (MMTEC) for learning general and comprehensive trajectory embeddings. We introduce a pretext task that reduces biases in pre-trained trajectory embeddings, yielding embeddings that are useful for a wide variety of downstream tasks. We also propose an attention-based discrete encoder and a NeuralCDE-based continuous encoder that extract and represent travel behavior and continuous spatio-temporal correlations from trajectories in embeddings, respectively. Extensive experiments on two real-world datasets and three downstream tasks offer insight into the design properties of our proposal and indicate that it is capable of outperforming existing trajectory embedding methods.