A decomposition dynamic graph convolutional recurrent network for traffic forecasting
Our daily lives are greatly impacted by traffic conditions, making it essential to have accurate
predictions of traffic flow within a road network. Traffic signals used for forecasting are …
predictions of traffic flow within a road network. Traffic signals used for forecasting are …
GE-GAN: A novel deep learning framework for road traffic state estimation
D Xu, C Wei, P Peng, Q Xuan, H Guo - Transportation Research Part C …, 2020 - Elsevier
Traffic state estimation is a crucial elemental function in Intelligent Transportation Systems
(ITS). However, the collected traffic state data are often incomplete in the real world. In this …
(ITS). However, the collected traffic state data are often incomplete in the real world. In this …
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 …
TFGAN: Traffic forecasting using generative adversarial network with multi-graph convolutional network
Traffic forecasting constitutes a task of great importance in intelligent transport systems.
Owing to the non-Euclidean structure of traffic data, the complicated spatial correlations, and …
Owing to the non-Euclidean structure of traffic data, the complicated spatial correlations, and …
Topological graph convolutional network-based urban traffic flow and density prediction
With the development of modern Intelligent Transportation System (ITS), reliable and
efficient transportation information sharing becomes more and more important. Although …
efficient transportation information sharing becomes more and more important. Although …
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 …
Spatial–temporal complex graph convolution network for traffic flow prediction
Traffic flow prediction remains an ongoing hot topic in the field of Intelligent Transportation
System. The state-of-the-art traffic flow prediction models can effectively extract both spatial …
System. The state-of-the-art traffic flow prediction models can effectively extract both spatial …
Graph neural network for traffic forecasting: The research progress
Traffic forecasting has been regarded as the basis for many intelligent transportation system
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …
Transfer learning with graph neural networks for short-term highway traffic forecasting
T Mallick, P Balaprakash, E Rask… - … Conference on Pattern …, 2021 - ieeexplore.ieee.org
Large-scale highway traffic forecasting approaches are critical for intelligent transportation
systems. Recently, deep-learning-based traffic forecasting methods have emerged as …
systems. Recently, deep-learning-based traffic forecasting methods have emerged as …
FDSA-STG: Fully dynamic self-attention spatio-temporal graph networks for intelligent traffic flow prediction
Y Duan, N Chen, S Shen, P Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
With the development of transportation and the ever-improving of vehicular technology,
Artificial Intelligence (AI) has been popularized in Intelligent Transportation Systems (ITS) …
Artificial Intelligence (AI) has been popularized in Intelligent Transportation Systems (ITS) …