Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19

Jiang，Renhe1,4; Wang，Zhaonan1; Cai，Zekun1; Yang，Chuang1; Fan，Zipei1,4; Xia，Tianqi2; Matsubara，Go2; Mizuseki，Hiroto3; Song，Xuan1,4; Shibasaki，Ryosuke1

10.1007/978-3-030-86514-6_20

2021

0302-9743

1611-3349

LECT NOTES ARTIF INT

12978

319-334

Modeling and predicting human mobility are of great significance to various application scenarios such as intelligent transportation system, crowd management, and disaster response. In particular, in a severe pandemic situation like COVID-19, human movements among different regions are taken as the most important point for understanding and forecasting the epidemic spread in a country. Thus, in this study, we collect big human GPS trajectory data covering the total 47 prefectures of Japan and model the daily human movements between each pair of prefectures with time-series Origin-Destination (OD) matrix. Then, given the historical observations from past days, we predict the countrywide OD matrices for the future one or more weeks by proposing a novel deep learning model called Origin-Destination Convolutional Recurrent Network (ODCRN). It integrates the recurrent and 2-dimensional graph convolutional components to deal with the highly complex spatiotemporal dependencies in sequential OD matrices. Experiment results over the entire COVID-19 period demonstrate the superiority of our proposed methodology over existing OD prediction models. Last, we apply the predicted countrywide OD matrices to the SEIR model, one of the most classic and widely used epidemic simulation model, to forecast the COVID-19 infection numbers for the entire Japan. The simulation results also demonstrate the high reliability and applicability of our countrywide OD prediction model for a pandemic scenario like COVID-19.

COVID-19 Deep learning Graph convolutional network Human mobility OD matrix Origin-destination

通讯

英语

CPCI-S ; EI

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications

WOS:000713055600020

20213910947934

Convolution ; Intelligent systems ; Matrix algebra ; Recurrent neural networks

Information Theory and Signal Processing:716.1 ; Artificial Intelligence:723.4 ; Algebra:921.1

2-s2.0-85115719006

Scopus

1.The University of Tokyo,Tokyo,Japan
2.LocationMind Inc.,Tokyo,Japan
3.BlogWatcher Inc.,Tokyo,Japan
4.Southern University of Science and Technology,Shenzhen,China

Jiang，Renhe,Wang，Zhaonan,Cai，Zekun,et al. Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19[C],2021:319-334.