Spatial-temporal graph ode networks for traffic flow forecasting

Z Fang, Q Long, G Song, K Xie - Proceedings of the 27th ACM SIGKDD …, 2021 - dl.acm.org
Spatial-temporal forecasting has attracted tremendous attention in a wide range of
applications, and traffic flow prediction is a canonical and typical example. The complex and …

Traffic flow forecasting with spatial-temporal graph diffusion network

X Zhang, C Huang, Y Xu, L Xia, P Dai, L Bo… - Proceedings of the …, 2021 - ojs.aaai.org
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of
spatial-temporal mining applications, such as intelligent traffic control and public risk …

Spatial dynamic graph convolutional network for traffic flow forecasting

H Li, S Yang, Y Song, Y Luo, J Li, T Zhou - Applied Intelligence, 2023 - Springer
The complex traffic network spatial correlation and the characteristic of high nonlinear and
dynamic traffic conditions in the time are the challenges to accurate traffic flow forecasting …

Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting

N Hu, D Zhang, K Xie, W Liang, MY Hsieh - Connection Science, 2022 - Taylor & Francis
Traffic forecasting is highly challenging due to its complex spatial and temporal
dependencies in the traffic network. Graph Convolutional Neural Network (GCN) has been …

Spatio-temporal joint graph convolutional networks for traffic forecasting

C Zheng, X Fan, S Pan, H Jin, Z Peng… - … on Knowledge and …, 2023 - ieeexplore.ieee.org
Recent studies have shifted their focus towards formulating traffic forecasting as a spatio-
temporal graph modeling problem. Typically, they constructed a static spatial graph at each …

Dynamic graph convolutional networks based on spatiotemporal data embedding for traffic flow forecasting

W Zhang, K Zhu, S Zhang, Q Chen, J Xu - Knowledge-Based Systems, 2022 - Elsevier
Traffic flow forecasting has always been a challenge owing to its complicated spatiotemporal
dependencies. Few of previous works can exploit the implicit interactions among traffic …

Adaptive spatio-temporal graph neural network for traffic forecasting

X Ta, Z Liu, X Hu, L Yu, L Sun, B Du - Knowledge-based systems, 2022 - Elsevier
Accurate traffic forecasting is of vital importance for the management and decision in
intelligent transportation systems. Indeed, it is a nontrivial endeavor to predict future traffic …

Generic dynamic graph convolutional network for traffic flow forecasting

Y Xu, L Han, T Zhu, L Sun, B Du, W Lv - Information Fusion, 2023 - Elsevier
In the field of traffic forecasting, methods based on Graph Convolutional Network (GCN) are
emerging. But existing methods still have limitations due to insufficient sharing patterns …

STGAT: Spatial-temporal graph attention networks for traffic flow forecasting

X Kong, W Xing, X Wei, P Bao, J Zhang, W Lu - IEEE Access, 2020 - ieeexplore.ieee.org
Traffic flow forecasting is a critical task for urban traffic control and dispatch in the field of
transportation, which is characterized by the high nonlinearity and complexity. In this paper …

Spatial-temporal fusion graph neural networks for traffic flow forecasting

M Li, Z Zhu - Proceedings of the AAAI conference on artificial …, 2021 - ojs.aaai.org
Spatial-temporal data forecasting of traffic flow is a challenging task because of complicated
spatial dependencies and dynamical trends of temporal pattern between different roads …