TrajDistLearn: Learning to compute distance between trajectories

Abstract

Discovering and clustering similar trajectories is a cornerstone task for movement pattern analysis and location prediction in applications like ride-sharing, supply-chain, maps and autonomous driving. However, the existing distance computation is computationally expensive and is hard to parallelize, which makes the large-scale computation prohibitive. We propose TrajDistLearn, a unified learning-based approach for trajectory distance computation, in which the traditional point-based trajectories are converted into rasterized images, and the distance function is learned via Siamese Networks in an end-to-end way. The framework accurately learns various distance metrics for the trajectory similarity computation, including the widely used Fréchet distance, which is a computationally expensive distance metric. The efficiency gain with neural network approximation is significant. Our approach achieves at least a 3000x speed-up on GPU and a 40x speed-up on CPU in comparison with naive Fréchet distance computation. In addition, our approach's computational overhead is independent of the sampling rate of the trajectories. Extensive experiments on real-world trajectory datasets demonstrate the effectiveness and efficiency of TrajDistLearn.