STORM-GAN+: spatio-temporal meta-GAN for cross-city estimation of heterogeneous human mobility responses to COVID-19

Han Bao, Xun Zhou, Yiqun Xie, Yanhua Li, Xiaowei Jia

Abstract: Estimating human mobility is essential during the COVID-19 pandemic because it provides policymakers with important information for non-pharmaceutical actions. Deep learning methods perform better on tasks with enough training data than traditional estimating techniques. However, estimating human mobility during the rapidly developing pandemic is challenging because of data non-stationarity, a lack of observations, and complicated social situations. Prior studies on estimating mobility either concentrate on a single city or cannot represent the spatio-temporal relationships across cities and time periods. To address these issues, we solve the cross-city human mobility estimation problem using a deep meta-generative framework. Recently, we proposed the Spatio-Temporal Meta-Generative Adversarial Network (STORM-GAN) model, which estimates dynamic human mobility responses under social and policy conditions relevant to COVID-19 and is facilitated by a novel spatio-temporal task-based graph (STTG) embedding. Although STORM-GAN achieves a good average estimation accuracy, it creates higher errors and exhibits over-fitting in particular cities due to spatial heterogeneity. To address these issues, in this paper, we extend our prior work by introducing an improved spatio-temporal deep generative model, namely STORM-GAN+. STORM-GAN+ deals with the difficulties by including a distance-based weighted training technique into the STTG embedding component to better represent the variety of knowledge transfer across cities. Furthermore, to mitigate the issue of overfitting, we modify the meta-learning training objective to teach estimated mobility. Finally, we propose a conditional meta-learning algorithm that explicitly tailors transferable knowledge to various task clusters. We perform comprehensive evaluations, and STORM-GAN+ approximates real-world human mobility responses more accurately than previous methods, including STORM-GAN.