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Human mobility patterns have shown significant applications inpolicy-decision scenarios and economic behavior researches. The human mobilitysimulation task aims to generate human mobility trajectories given a small setof trajectory data, which have aroused much concern due to the scarcity andsparsity of human mobility data. Existing methods mostly rely on the staticrelationships of locations, while largely neglect the dynamic spatiotemporaleffects of locations. On the one hand, spatiotemporal correspondences of visitdistributions reveal the spatial proximity and the functionality similarity oflocations. On the other hand, the varying durations in different locationshinder the iterative generation process of the mobility trajectory. Therefore,we propose a novel framework to model the dynamic spatiotemporal effects oflocations, namely SpatioTemporal-Augmented gRaph neural networks (STAR). TheSTAR framework designs various spatiotemporal graphs to capture thespatiotemporal correspondences and builds a novel dwell branch to simulate thevarying durations in locations, which is finally optimized in an adversarialmanner. The comprehensive experiments over four real datasets for the humanmobility simulation have verified the superiority of STAR to state-of-the-artmethods. Our code will be made publicly available.

Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation