Jointly spatial-temporal representation learning for individual trajectories

TL;DR

This paper introduces ST-GraphRL, a novel deep learning method that captures implicit spatial-temporal dependencies in individual trajectories, improving geospatial modeling and understanding of human mobility patterns.

Contribution

It formalizes learnable spatial-temporal dependencies into trajectory representations using a graph-based encoder-decoder framework, advancing deep learning for geospatial data.

Findings

Outperforms baseline models in predicting movement distributions

Effectively preserves trajectory similarity with high spatial-temporal correlations

Validates understanding of spatial-temporal patterns in latent space

Abstract

Individual trajectories, rich in human-environment interaction information across space and time, serve as vital inputs for geospatial foundation models (GeoFMs). However, existing attempts at learning trajectory representations have overlooked the implicit spatial-temporal dependency within trajectories, failing to encode such dependency in a deep learning-friendly format. That poses a challenge in obtaining general-purpose trajectory representations. Therefore, this paper proposes a spatial-temporal joint representation learning method (ST-GraphRL) to formalize learnable spatial-temporal dependencies into trajectory representations. The proposed ST-GraphRL consists of three compositions: (i) a weighted directed spatial-temporal graph to explicitly construct mobility interactions in both space and time dimensions; (ii) a two-stage jointly encoder (i.e., decoupling and fusion), to learn entangled spatial-temporal dependencies by independently decomposing and jointly aggregating space and time information; (iii) a decoder guides ST-GraphRL to learn explicit mobility regularities by simulating the spatial-temporal distributions of trajectories. Tested on three real-world human mobility datasets, the proposed ST-GraphRL outperformed all the baseline models in predicting movement spatial-temporal distributions and preserving trajectory similarity with high spatial-temporal correlations. Analyzing spatial-temporal features presented in latent space validates that ST-GraphRL understands spatial-temporal patterns. This study may also benefit representation learnings of other geospatial data to achieve general-purpose data representations and advance GeoFMs development.