Spatiotemporal attention-based graph convolution network for segment-level traffic prediction
Traffic prediction, as a core component of intelligent transportation systems (ITS), has been
investigated thoroughly in the literature. Nevertheless, timely accurate traffic prediction still …
investigated thoroughly in the literature. Nevertheless, timely accurate traffic prediction still …
Spatial-temporal graph attention networks: A deep learning approach for traffic forecasting
Traffic speed prediction, as one of the most important topics in Intelligent Transport Systems
(ITS), has been investigated thoroughly in the literature. Nonetheless, traditional methods …
(ITS), has been investigated thoroughly in the literature. Nonetheless, traditional methods …
Modeling global spatial–temporal graph attention network for traffic prediction
B Sun, D Zhao, X Shi, Y He - IEEE Access, 2021 - ieeexplore.ieee.org
Accurate and efficient traffic prediction is the key to the realization of intelligent transportation
system (ITS), which helps to alleviate traffic congestion and reduce traffic accidents. Due to …
system (ITS), which helps to alleviate traffic congestion and reduce traffic accidents. Due to …
Dual dynamic spatial-temporal graph convolution network for traffic prediction
Recently, Graph Convolution Network (GCN) and Temporal Convolution Network (TCN) are
introduced into traffic prediction and achieve state-of-the-art performance due to their good …
introduced into traffic prediction and achieve state-of-the-art performance due to their good …
A general traffic flow prediction approach based on spatial-temporal graph attention
C Tang, J Sun, Y Sun, M Peng, N Gan - IEEE Access, 2020 - ieeexplore.ieee.org
Accurate and reliable traffic flow prediction is critical to the safe and stable deployment of
intelligent transportation systems. However, it is very challenging since the complex spatial …
intelligent transportation systems. However, it is very challenging since the complex spatial …
Dynamic traffic correlations based spatio-temporal graph convolutional network for urban traffic prediction
Accurate urban traffic prediction is a critical issue in Intelligent Transportation Systems (ITS).
It is challenging since urban traffic usually indicates high dynamic spatio-temporal …
It is challenging since urban traffic usually indicates high dynamic spatio-temporal …
PGCN: Progressive graph convolutional networks for spatial–temporal traffic forecasting
The complex spatial-temporal correlations in transportation networks make the traffic
forecasting problem challenging. Since transportation system inherently possesses graph …
forecasting problem challenging. Since transportation system inherently possesses graph …
[PDF][PDF] LSGCN: Long short-term traffic prediction with graph convolutional networks.
Traffic prediction is a classical spatial-temporal prediction problem with many real-world
applications such as intelligent route planning, dynamic traffic management, and smart …
applications such as intelligent route planning, dynamic traffic management, and smart …
Spatial-temporal convolutional graph attention networks for citywide traffic flow forecasting
Traffic flow prediction plays an important role in many spatial-temporal data applications, eg,
traffic management and urban planning. Various deep learning techniques are developed to …
traffic management and urban planning. Various deep learning techniques are developed to …
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 …