Conference proceeding
STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID
2022 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), pp.1-10
IEEE International Conference on Data Mining
01/01/2022
DOI: 10.1109/ICDM54844.2022.00010
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 outperform baselines.
Details
- Title: Subtitle
- STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID
- Creators
- Han Bao - University of IowaXun Zhou - University of IowaYiqun Xie - University of Maryland, College ParkYanhua Li - Worcester Polytechnic InstituteXiaowei Jia - University of Pittsburgh
- Contributors
- Xingquan Zhu (Editor)Sanjay Ranka (Editor)My T Thai (Editor)Takashi Washio (Editor)Xindong Wu (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- 2022 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), pp.1-10
- Publisher
- IEEE
- Series
- IEEE International Conference on Data Mining
- DOI
- 10.1109/ICDM54844.2022.00010
- ISSN
- 1550-4786
- eISSN
- 2374-8486
- Number of pages
- 10
- Grant note
- DRI award at the University of Maryland Safety Research using Simulation University Transportation Center (SAFER-SIM) Google's AI for Social Good Impact Scholars program CRC at the University of Pittsburgh 69A3551747131 / U.S. Department of Transportation's University Transportation Centers Program IIS-1942680; CNS-1952085; CMMI-1831140; DGE-2021871; 2105133; 2126474; 2147195 / NSF; National Science Foundation (NSF) Pitt Momentum Funds award G21AC10207 / USGS award
- Language
- English
- Date published
- 01/01/2022
- Academic Unit
- Business Analytics
- Record Identifier
- 9984419650502771
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