Journal article
COVID-GAN+: Estimating Human Mobility Responses to COVID-19 through Spatio-temporal Generative Adversarial Networks with Enhanced Features
ACM transactions on intelligent systems and technology, Vol.13(2), pp.1-23
01/05/2022
DOI: 10.1145/3481617
Appears in UI Libraries Support Open Access
Abstract
Estimating human mobility responses to the large-scale spreading of the COVID-19 pandemic is crucial, since its significance guides policymakers to give Non-pharmaceutical Interventions, such as closure or reopening of businesses. It is challenging to model due to complex social contexts and limited training data. Recently, we proposed a conditional generative adversarial network (COVID-GAN) to estimate human mobility response under a set of social and policy conditions integrated from multiple data sources. Although COVID-GAN achieves a good average estimation accuracy under real-world conditions, it produces higher errors in certain regions due to the presence of spatial heterogeneity and outliers. To address these issues, in this article, we extend our prior work by introducing a new spatio-temporal deep generative model, namely, COVID-GAN+. COVID-GAN+ deals with the spatial heterogeneity issue by introducing a new spatial feature layer that utilizes the local Moran statistic to model the spatial heterogeneity strength in the data. In addition, we redesign the training objective to learn the estimated mobility changes from historical average levels to mitigate the effects of spatial outliers. We perform comprehensive evaluations using urban mobility data derived from cell phone records and census data. Results show that COVID-GAN+ can better approximate real-world human mobility responses than prior methods, including COVID-GAN.
Details
- Title: Subtitle
- COVID-GAN+: Estimating Human Mobility Responses to COVID-19 through Spatio-temporal Generative Adversarial Networks with Enhanced Features
- Creators
- Han Bao - University of IowaXun Zhou - University of IowaYiqun Xie - University of Maryland, College ParkYingxue Zhang - Worcester Polytechnic InstituteYanhua Li - Worcester Polytechnic Institute
- Resource Type
- Journal article
- Publication Details
- ACM transactions on intelligent systems and technology, Vol.13(2), pp.1-23
- Publisher
- Association for Computing Machinery (ACM)
- DOI
- 10.1145/3481617
- ISSN
- 2157-6904
- eISSN
- 2157-6912
- Grant note
- name: Safety Research using Simulation University Transportation Center; name: U.S. Department of Transportation’s University Transportation Centers Program, award: 69A3551747131; name: NSF, award: 2105133, 2126474, IIS-1942680 (CAREER), CNS-1952085, CMMI-1831140, and DGE-2021871; name: Google’s AI for Social Good Impact Scholars program; name: Dean’s Research Initiative Award at the University of Maryland
- Language
- English
- Date published
- 01/05/2022
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380447402771
Metrics
2 Record Views