FRIGATE: Frugal Spatio-temporal Forecasting on Road Networks

Understand

Modelling spatio-temporal processes on road networks is a task of growing importance.

• While significant progress has been made on developing spatio-temporal graph neural networks (Gnns), existing works are built upon three assumptions that are not practical on real-world road networks.
• First, they assume sensing on every node of a road network.
• In reality, due to budget-constraints or sensor failures, all locations (nodes) may not be equipped with sensors.