Incident-Guided Spatiotemporal Traffic Forecasting

1. 1Identify external disturbances such as traffic accidents and adverse weather
2. 2Integrate these disturbances into graph neural network models
3. 3Use historical traffic data to train the models
4. 4Evaluate the performance of the models using metrics such as mean absolute error and mean squared error

Data scientists and transportation system engineers can benefit from this research as it provides a novel approach to traffic forecasting, enabling them to make more accurate predictions and informed decisions

💡 Considering external disturbances like accidents and weather can significantly improve traffic forecasting accuracy

🚗💡 Improve traffic forecasting with graph neural networks and incident guidance!