Graph neural network for traffic forecasting: A survey
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 …
[HTML][HTML] Urban traffic flow prediction techniques: A review
B Medina-Salgado, E Sánchez-DelaCruz… - … Informatics and Systems, 2022 - Elsevier
In recent decades, the development of transport infrastructure has had a great development,
although traffic problems continue to spread due to increase due to the increase in the …
although traffic problems continue to spread due to increase due to the increase in the …
Graph neural network-driven traffic forecasting for the connected internet of vehicles
Due to great advances in wireless communication, the connected Internet of vehicles
(CIoVs) has become prevalent. Naturally, internal connections among active vehicles are an …
(CIoVs) has become prevalent. Naturally, internal connections among active vehicles are an …
A multi-factor driven spatiotemporal wind power prediction model based on ensemble deep graph attention reinforcement learning networks
Y Chengqing, Y Guangxi, Y Chengming, Z Yu, M Xiwei - Energy, 2023 - Elsevier
Spatiotemporal wind power prediction technology could provide technical support for wind
farm energy regulation and dynamic planning. In the paper, a novel ensemble deep graph …
farm energy regulation and dynamic planning. In the paper, a novel ensemble deep graph …
A variational Bayesian deep network with data self-screening layer for massive time-series data forecasting
Compared with mechanism-based modeling methods, data-driven modeling based on big
data has become a popular research field in recent years because of its applicability …
data has become a popular research field in recent years because of its applicability …
Denoising aggregation of graph neural networks by using principal component analysis
W Dong, M Woźniak, J Wu, W Li… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
To avoid the overfitting phenomenon that appeared in performing graph neural networks
(GNNs) on test examples, the feature encoding scheme of GNNs usually introduces the …
(GNNs) on test examples, the feature encoding scheme of GNNs usually introduces the …
Deep collaborative intelligence-driven traffic forecasting in green internet of vehicles
Accompanied with the development of green wireless communication, the green Internet of
Vehicles (GIoV) has been a latent solution for future transportation. Among them, intelligent …
Vehicles (GIoV) has been a latent solution for future transportation. Among them, intelligent …
Emerging technologies for smart cities' transportation: geo-information, data analytics and machine learning approaches
KLM Ang, JKP Seng, E Ngharamike… - … International Journal of …, 2022 - mdpi.com
With the recent increase in urban drift, which has led to an unprecedented surge in urban
population, the smart city (SC) transportation industry faces a myriad of challenges …
population, the smart city (SC) transportation industry faces a myriad of challenges …
Predicting traffic propagation flow in urban road network with multi-graph convolutional network
H Yang, Z Li, Y Qi - Complex & Intelligent Systems, 2024 - Springer
Traffic volume propagating from upstream road link to downstream road link is the key
parameter for designing intersection signal timing scheme. Recent works successfully used …
parameter for designing intersection signal timing scheme. Recent works successfully used …
Deep learning for time-series prediction in IIoT: progress, challenges, and prospects
Time-series prediction plays a crucial role in the Industrial Internet of Things (IIoT) to enable
intelligent process control, analysis, and management, such as complex equipment …
intelligent process control, analysis, and management, such as complex equipment …