A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic
Advanced Engineering Informatics
; : 101678, 2022.
Article
in English
| ScienceDirect | ID: covidwho-1894733
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.
Full text:
Available
Collection:
Databases of international organizations
Database:
ScienceDirect
Type of study:
Prognostic study
Language:
English
Journal:
Advanced Engineering Informatics
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS