STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19

TL;DR

This paper introduces STORM-GAN, a novel spatio-temporal meta-generative model that effectively estimates human mobility responses to COVID-19 across different cities and time periods, even with limited data.

Contribution

It presents the first deep meta-generative framework for cross-city mobility estimation, incorporating a spatio-temporal task-based graph embedding for rapid adaptation.

Findings

Significantly improves estimation accuracy over baselines.

Effectively captures city similarities to reduce heterogeneity.

Demonstrates robustness with limited training samples.

Abstract

Human mobility estimation is crucial during the COVID-19 pandemic due to its significant guidance for policymakers to make non-pharmaceutical interventions. While deep learning approaches outperform conventional estimation techniques on tasks with abundant training data, the continuously evolving pandemic poses a significant challenge to solving this problem due to data nonstationarity, limited observations, and complex social contexts. Prior works on mobility estimation either focus on a single city or lack the ability to model the spatio-temporal dependencies across cities and time periods. To address these issues, we make the first attempt to tackle the cross-city human mobility estimation problem through a deep meta-generative framework. We propose a Spatio-Temporal Meta-Generative Adversarial Network (STORM-GAN) model that estimates dynamic human mobility responses under a set of social and policy conditions related to COVID-19. Facilitated by a novel spatio-temporal task-based graph (STTG) embedding, STORM-GAN is capable of learning shared knowledge from a spatio-temporal distribution of estimation tasks and quickly adapting to new cities and time periods with limited training samples. The STTG embedding component is designed to capture the similarities among cities to mitigate cross-task heterogeneity. Experimental results on real-world data show that the proposed approach can greatly improve estimation performance and out-perform baselines.