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 …
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 …
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 …
GraphSAGE-based dynamic spatial–temporal graph convolutional network for traffic prediction
T Liu, A Jiang, J Zhou, M Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Traffic networks exhibit complex spatial-temporal dependencies, and accurately capturing
such dependencies is critical to improving prediction accuracy. Recently, many deep …
such dependencies is critical to improving prediction accuracy. Recently, many deep …
Optimized graph convolution recurrent neural network for traffic prediction
Traffic prediction is a core problem in the intelligent transportation system and has broad
applications in the transportation management and planning, and the main challenge of this …
applications in the transportation management and planning, and the main challenge of this …
3d graph convolutional networks with temporal graphs: A spatial information free framework for traffic forecasting
Spatio-temporal prediction plays an important role in many application areas especially in
traffic domain. However, due to complicated spatio-temporal dependency and high non …
traffic domain. However, due to complicated spatio-temporal dependency and high non …
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 …
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 …
Dstgcn: Dynamic spatial-temporal graph convolutional network for traffic prediction
J Hu, X Lin, C Wang - IEEE Sensors Journal, 2022 - ieeexplore.ieee.org
Traffic prediction is an important part of building a smart city. Reasonable traffic prediction
can help the relevant departments to make important decisions and help people to plan their …
can help the relevant departments to make important decisions and help people to plan their …
Multi-mode dynamic residual graph convolution network for traffic flow prediction
Urban traffic congestion is not only an important cause of traffic accidents, but also a major
hinder to urban development. By learning the historical traffic flow data, we can forecast the …
hinder to urban development. By learning the historical traffic flow data, we can forecast the …