Traffic Forecasting with Graph Neural Networks
Machine Learning · Smart Mobility
An A3T-GCN graph neural network that forecasts traffic speed 60 minutes ahead across 207 real Los Angeles sensors — learning from both the time series and the road network itself, so it anticipates spillover before it shows up locally. A coupled SUMO simulation then used those forecasts to drive adaptive signal control.
View repository ↗