A new ensemble deep graph reinforcement learning network for spatio-temporal traffic volume forecasting in a freeway network
Spatio-temporal traffic volume forecasting technologies can effectively improve freeway
traffic efficiency and the travel comfort of humans. To construct a high-precision traffic …
traffic efficiency and the travel comfort of humans. To construct a high-precision traffic …
A3t-gcn: Attention temporal graph convolutional network for traffic forecasting
J Bai, J Zhu, Y Song, L Zhao, Z Hou, R Du… - … International Journal of …, 2021 - mdpi.com
Accurate real-time traffic forecasting is a core technological problem against the
implementation of the intelligent transportation system. However, it remains challenging …
implementation of the intelligent transportation system. However, it remains challenging …
T-GCN: A temporal graph convolutional network for traffic prediction
Accurate and real-time traffic forecasting plays an important role in the intelligent traffic
system and is of great significance for urban traffic planning, traffic management, and traffic …
system and is of great significance for urban traffic planning, traffic management, and traffic …
AST-GCN: Attribute-augmented spatiotemporal graph convolutional network for traffic forecasting
J Zhu, Q Wang, C Tao, H Deng, L Zhao, H Li - Ieee Access, 2021 - ieeexplore.ieee.org
Traffic forecasting is a fundamental and challenging task in the field of intelligent
transportation. Accurate forecasting not only depends on the historical traffic flow information …
transportation. Accurate forecasting not only depends on the historical traffic flow information …
Spatiotemporal residual graph attention network for traffic flow forecasting
Q Zhang, C Li, F Su, Y Li - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
Accurate spatiotemporal traffic flow forecasting is significant for the modern traffic
management and control. In order to capture the spatiotemporal characteristics of the traffic …
management and control. In order to capture the spatiotemporal characteristics of the traffic …
Short-term traffic speed forecasting based on graph attention temporal convolutional networks
G Guo, W Yuan - Neurocomputing, 2020 - Elsevier
Accurate and timely traffic forecasting is significant for intelligent transportation
management. However, existing approaches model the temporal and spatial features of …
management. However, existing approaches model the temporal and spatial features of …
Spatiotemporal graph convolutional network for multi-scale traffic forecasting
Benefiting from the rapid development of geospatial big data-related technologies,
intelligent transportation systems (ITS) have become a part of people's daily life. Traffic …
intelligent transportation systems (ITS) have become a part of people's daily life. Traffic …
Multiple information spatial–temporal attention based graph convolution network for traffic prediction
S Tao, H Zhang, F Yang, Y Wu, C Li - Applied Soft Computing, 2023 - Elsevier
Traffic prediction (forecasting) is a key problem in intelligent transportation. It helps
engineers to obtain traffic trends in advance so that they can make favorable decisions …
engineers to obtain traffic trends in advance so that they can make favorable decisions …
TransGAT: A dynamic graph attention residual networks for traffic flow forecasting
T Wang, S Ni, T Qin, D Cao - Sustainable Computing: Informatics and …, 2022 - Elsevier
Artificial intelligence has attracted great attentions in the field of traffic flow forecasting.
Graph neural network (GNN), as one of the most popular methods, capable of processing …
Graph neural network (GNN), as one of the most popular methods, capable of processing …
Hierarchical graph convolution network for traffic forecasting
Traffic forecasting is attracting considerable interest due to its widespread application in
intelligent transportation systems. Given the complex and dynamic traffic data, many …
intelligent transportation systems. Given the complex and dynamic traffic data, many …