AGC-ODE: Adaptive Graph Controlled Neural ODE for Human Mobility Prediction

Abstract

Despite the substantial progress in predicting human mobility, most existing methods fail to reveal the spatiotemporal patterns under significant interventions such as COVID-19, which disrupt the routine of human mobility. To fill this gap, this paper presents a unified framework for learning human mobility in both regular and intervened scenarios through explicit modeling of the intervention and the intervened system. To be concrete, we design a novel Deep State-Space Model (DSSM) called AGC-ODE: Adaptive Graph Controlled Neural Ordinary Differential Equation for human mobility prediction during COVID-19. The transition equation that describes continuous-time dynamics of human mobility is parameterized with a graph-controlled Neural ODE, and the latent control that guides the equation propagating is inferred through the multi-head gating filters. Additionally, an information capacity constraint is applied to foster the disentanglement of interventions. Lastly, AGC-ODE utilizes a data-driven initialization strategy to improve DSSM’s initial state estimation. We conduct extensive experiments and analysis on two real-world datasets of Beijing and the U.S. to demonstrate the superiority and interpretability of our model. Furthermore, we introduce a deployed system that is based on AGC-ODE and how it helps epidemic prevention during the COVID era and work resumption in the post-COVID era.