Deep Spatially and Temporally Aware Similarity Computation for Road Network Constrained Trajectories

TL;DR

This paper introduces ST2Vec, a deep learning framework that effectively computes trajectory similarity by considering both spatial and temporal aspects within road networks, outperforming existing methods.

Contribution

The paper presents a novel deep learning approach that incorporates both spatial and temporal information for trajectory similarity, addressing limitations of prior spatial-only methods.

Findings

ST2Vec significantly outperforms state-of-the-art methods in real datasets.

The framework effectively captures both spatial and temporal trajectory features.

Experimental results demonstrate improved accuracy and efficiency.

Abstract

Trajectory similarity computation has drawn massive attention, as it is core functionality in a wide range of applications such as ride-sharing, traffic analysis, and social recommendation. Motivated by the recent success of deep learning technologies, researchers start devoting efforts to learning-based similarity analyses to overcome the limitations (i.e., high cost and poor adaptability) of traditional methods. Specifically, deep trajectory similarity computation aims to learn a distance function that can evaluate how similar two trajectories are via neural networks. However, existing learning-based methods focus on spatial similarity but ignore the time dimension of trajectories, which is suboptimal for time-aware applications. Besides, they tend to disregard the embedding of trajectories into road networks, restricting their applicability in real scenarios. In this paper, we propose an effective learning-based framework, called ST2Vec, to perform efficient spatially and temporally aware trajectory similarity computation in road networks. Finally, extensive experimental evaluation using three real trajectory data sets shows that ST2Vec outperforms all the state-of-the-art approaches substantially.