Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation

TL;DR

This paper introduces STAR, a novel graph neural network framework that models dynamic spatiotemporal effects and location durations to improve human mobility trajectory simulation, outperforming existing methods.

Contribution

The paper proposes a new framework called STAR that captures dynamic spatiotemporal effects and location durations for more accurate human mobility simulation.

Findings

STAR outperforms state-of-the-art methods on four real datasets.

The framework effectively models spatiotemporal correspondences.

The dwell branch improves the simulation of location durations.

Abstract

Human mobility patterns have shown significant applications in policy-decision scenarios and economic behavior researches. The human mobility simulation task aims to generate human mobility trajectories given a small set of trajectory data, which have aroused much concern due to the scarcity and sparsity of human mobility data. Existing methods mostly rely on the static relationships of locations, while largely neglect the dynamic spatiotemporal effects of locations. On the one hand, spatiotemporal correspondences of visit distributions reveal the spatial proximity and the functionality similarity of locations. On the other hand, the varying durations in different locations hinder the iterative generation process of the mobility trajectory. Therefore, we propose a novel framework to model the dynamic spatiotemporal effects of locations, namely SpatioTemporal-Augmented gRaph neural networks (STAR). The STAR framework designs various spatiotemporal graphs to capture the spatiotemporal correspondences and builds a novel dwell branch to simulate the varying durations in locations, which is finally optimized in an adversarial manner. The comprehensive experiments over four real datasets for the human mobility simulation have verified the superiority of STAR to state-of-the-art methods. Our code is available at https://github.com/Star607/STAR-TKDE.