A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic
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 …
to people's normal production and life. Predicting the traffic revitalization index can provide …
A new approach to COVID-19 data mining: A deep spatial–temporal prediction model based on tree structure for traffic revitalization index
The outbreak of the COVID-19 epidemic has had a huge impact on a global scale and its
impact has covered almost all human industries. The Chinese government enacted a series …
impact has covered almost all human industries. The Chinese government enacted a series …
Deep learning in the COVID-19 epidemic: A deep model for urban traffic revitalization index
The research of traffic revitalization index can provide support for the formulation and
adjustment of policies related to urban management, epidemic prevention and resumption of …
adjustment of policies related to urban management, epidemic prevention and resumption of …
Urban traffic prediction from spatio-temporal data using deep meta learning
Predicting urban traffic is of great importance to intelligent transportation systems and public
safety, yet is very challenging because of two aspects: 1) complex spatio-temporal …
safety, yet is very challenging because of two aspects: 1) complex spatio-temporal …
Spatio-temporal meta learning for urban traffic prediction
Predicting urban traffic is of great importance to intelligent transportation systems and public
safety, yet is very challenging in three aspects: 1) complex spatio-temporal correlations of …
safety, yet is very challenging in three aspects: 1) complex spatio-temporal correlations of …
Urban traffic dynamics prediction—a continuous spatial-temporal meta-learning approach
Urban traffic status (eg, traffic speed and volume) is highly dynamic in nature, namely,
varying across space and evolving over time. Thus, predicting such traffic dynamics is of …
varying across space and evolving over time. Thus, predicting such traffic dynamics is of …
Traffic prediction with transfer learning: A mutual information-based approach
In modern traffic management, one of the most essential yet challenging tasks is accurately
and timely predicting traffic. It has been well investigated and examined that deep learning …
and timely predicting traffic. It has been well investigated and examined that deep learning …
Dl-traff: Survey and benchmark of deep learning models for urban traffic prediction
Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical
Systems) technologies, big spatiotemporal data are being generated from mobile phones …
Systems) technologies, big spatiotemporal data are being generated from mobile phones …
DeepTrend 2.0: A light-weighted multi-scale traffic prediction model using detrending
In this paper, we propose a detrending based and deep learning based many-to-many traffic
prediction model called DeepTrend 2.0 that accepts information collected from multiple …
prediction model called DeepTrend 2.0 that accepts information collected from multiple …
MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction
The passenger flow prediction of the public metro system is a core and critical part of the
intelligent transportation system, and is essential for traffic management, metro planning …
intelligent transportation system, and is essential for traffic management, metro planning …