A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic. (August 2022) - Wang, Yue; Lv, Zhiqiang; Sheng, Zhaoyu; Sun, Haokai; Zhao, Aite
Abstract: The COVID-19 pandemic is a major global public health problem that has caused hardship to people's normal production and life. Predicting the traffic revitalization index can provide references for city managers to formulate policies related to traffic and epidemic prevention. Previous methods have struggled to capture the complex and diverse dynamic spatio-temporal correlations during the COVID-19 pandemic. Therefore, we propose a deep spatio-temporal meta-learning model for the prediction of traffic revitalization index (DeepMeta-TRI) using external auxiliary information such as COVID-19 data. We conduct extensive experiments on a real-world dataset, and the results validate the predictive performance of DeepMeta-TRI and its effectiveness in addressing underfitting.