Graph neural network for traffic forecasting: A survey
W Jiang, J Luo - Expert Systems with Applications, 2022 - Elsevier
Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …
learning models, including convolution neural networks and recurrent neural networks, have …
Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models
We present PyTorch Geometric Temporal, a deep learning framework combining state-of-the-
art machine learning algorithms for neural spatiotemporal signal processing. The main goal …
art machine learning algorithms for neural spatiotemporal signal processing. The main goal …
A comprehensive review on deep learning approaches in wind forecasting applications
The effective use of wind energy is an essential part of the sustainable development of
human society, in particular, at the recent unprecedented pressure in shaping a low carbon …
human society, in particular, at the recent unprecedented pressure in shaping a low carbon …
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 …
Citywide traffic speed prediction: A geometric deep learning approach
JQ James - Knowledge-Based Systems, 2021 - Elsevier
Accurate traffic speed prediction is critical to modern internet of things-based intelligent
transportation systems. It serves as the foundation of advanced traffic management systems …
transportation systems. It serves as the foundation of advanced traffic management systems …
Bearing remaining useful life prediction based on regression shapalet and graph neural network
X Yang, Y Zheng, Y Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Remaining useful life (RUL) prediction of bearing is essential to guarantee its safe
operation. In recent years, deep learning (DL)-based methods attract a lot of research …
operation. In recent years, deep learning (DL)-based methods attract a lot of research …
A GAN-based short-term link traffic prediction approach for urban road networks under a parallel learning framework
Road link speed is often employed as an essential measure of traffic state in the operation of
an urban traffic network. Not only real-time traffic demand but also signal timings and other …
an urban traffic network. Not only real-time traffic demand but also signal timings and other …
A digital twin of a water distribution system by using graph convolutional networks for pump speed-based state estimation
Water distribution system monitoring is currently carried out using advanced real-time
control technologies to achieve a higher operational efficiency. Data analysis techniques …
control technologies to achieve a higher operational efficiency. Data analysis techniques …
Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions
Geospatial artificial intelligence (GeoAI) has emerged as a subfield of GIScience that uses
artificial intelligence approaches and machine learning techniques for geographic …
artificial intelligence approaches and machine learning techniques for geographic …
[HTML][HTML] CasSeqGCN: Combining network structure and temporal sequence to predict information cascades
Y Wang, X Wang, Y Ran, R Michalski, T Jia - Expert Systems with …, 2022 - Elsevier
One important task in the study of information cascade is to predict the future recipients of a
message given its past spreading trajectory. While the network structure serves as the …
message given its past spreading trajectory. While the network structure serves as the …