STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human
Mobility Responses to COVID-19
- URL: http://arxiv.org/abs/2301.08648v1
- Date: Fri, 20 Jan 2023 15:55:41 GMT
- Title: STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human
Mobility Responses to COVID-19
- Authors: Han Bao, Xun Zhou, Yiqun Xie, Yanhua Li, Xiaowei Jia
- Abstract summary: We make the first attempt to tackle the cross-city human mobility estimation problem through a deep meta-generative framework.
We propose a S-Temporal Meta-Generative Adrial Network (STORM-GAN) model that estimates dynamic human mobility responses.
We show that the proposed approach can greatly improve estimation performance and out-perform baselines.
- Score: 17.611056163940404
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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