Graph Neural Networks for Traffic Flow Forecasting
From ISLAB/CAISR
| Title | Graph Neural Networks for Traffic Flow Forecasting |
|---|---|
| Summary | The main goal of this project is to explore GNN for traffic flow forecasting |
| Keywords | Graph neural networks (GNN), Traffic flow forecasting |
| TimeFrame | Fall 2022 |
| References | Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2008). The graph neural network model. IEEE transactions on neural networks, 20(1), 61-80. |
| Prerequisites | |
| Author | |
| Supervisor | Prayag Tiwari, Sławomir Nowaczyk |
| Level | Master |
| Status | Open |
Traffic flow forecasting is one of the most critical topics in the Intelligent transportation system. Traffic flow forecasting focuses on predicting future traffic flow using past information. It is a challenging task due to the dynamic nature of the traffic network. Graph neural networks (GNN) have shown to be effective in modeling the dynamic nature of traffic data. The main goal of this project is to explore GNN for traffic flow forecasting.
