Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation

• URL: http://arxiv.org/abs/2306.09381v3
• Date: Thu, 6 Jun 2024 05:56:08 GMT
• Title: Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation
• Authors: Yu Wang, Tongya Zheng, Shunyu Liu, Zunlei Feng, Kaixuan Chen, Yunzhi Hao, Mingli Song,
• Abstract summary: We propose a novel framework to model the dynamictemporal effects of locations, namely SRpatio-Augmented gaph neural networks. The STAR framework designs varioustemporal graphs to capture the behaviors correspondence and builds a novel branch to simulate the varying dwells in locations, which duration is finally optimized in an adversarial manner.
• Score: 35.89805766554052
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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