Co-Move
Covid-19 and Inter-Region Human Mobility Analysis and Prediction
Bibliographic Data
| ID | 22108249 |
|---|---|
| Authors | Sandip Kumar Burnwal (0009-0009-3664-512X, Indian Institute of Technology Jodhpur), Pragati Sinha (0009-0003-0269-6232, Indian Institute of Technology Jodhpur), Bhumika Bhumika (0000-0002-4391-8634, Indian Institute of Technology Jodhpur), Jayant Vyas (0000-0002-9718-3338, Indian Institute of Technology Jodhpur), Debasis Das (0000-0003-0474-0842, Indian Institute of Technology Jodhpur) |
| Year | 2024 |
| Volume | 11 |
| Issue | 5 |
| Pages | 6843-6853 |
| Publication date | 2024-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2024.3406512 |
| OpenAlex | W4400525312 |
| Language | EN |
| References cited | 46 |
Humans relocate for a variety of reasons, including employment, study, tourism, family, and health. However, in COVID-19, the government imposed restrictions such as lockdowns, travel bans, and quarantine regulations, preventing many people from traveling for work, study, or leisure; thus, human mobility exhibits distinct patterns than ordinary movements. In this article, we analyze the effect of COVID-19 on interregion human mobility using curated Twitter data and propose a framework named Co-Move for human mobility prediction. There were three challenges in predicting mobility: 1) heterogenous data; 2) short and long-term periodic patterns; and 3) complex intercorrelation. To address these challenges, the framework comprises parallel multiscale convolution and long short-term memory components. Extensive experiments on real-life mobility datasets show the mean square error (MSE) of 0.0179, RMSE of 0.129, mean absolute error (MAE) of 0.1075, and outperform baseline models
2019-20 coronavirus outbreak · Biology · Outbreak · Pandemic · Computer Science · Human Mobility and Location-Based Analysis · Medicine · Virology
| Citation velocity | historical |
|---|---|
| Highly cited | No |