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 routing. Various forecasting methods have been proposed in the literature, including statistical models, sha...
Main Authors: | Jiang, Weiwei, Luo, Jiayun, He, Miao, Gu, Weixi |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/169720 |
Similar Items
-
Graph Neural Network for Traffic Forecasting: The Research Progress
by: Weiwei Jiang, et al.
Published: (2023-02-01) -
Traffic forecasting with graph spatial-temporal position recurrent network
by: Chen, Yibi, et al.
Published: (2023) -
A Spatiotemporal Graph Neural Network with Graph Adaptive and Attention Mechanisms for Traffic Flow Prediction
by: Yanqiang Huo, et al.
Published: (2024-01-01) -
STGAT: Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting
by: Xiangyuan Kong, et al.
Published: (2020-01-01) -
Graph-Based Neural Networks’ Framework Using Microcontrollers for Energy-Efficient Traffic Forecasting
by: Sorin Zoican, et al.
Published: (2024-01-01)