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Efficient and accurate incident prediction in spatio-temporal systems iscritical to minimize service downtime and optimize performance. This work aimsto utilize historic data to predict and diagnose incidents usingspatio-temporal forecasting. We consider the specific use case of road trafficsystems where incidents take the form of anomalous events, such as accidents orbroken-down vehicles. To tackle this, we develop a neural model, called RadNet,which forecasts system parameters such as average vehicle speeds for a futuretimestep. As such systems largely follow daily or weekly periodicity, wecompare RadNet's predictions against historical averages to label incidents.Unlike prior work, RadNet infers spatial and temporal trends in bothpermutations, finally combining the dense representations before forecasting.This facilitates informed inference and more accurate incident detection.Experiments with two publicly available and a new road traffic datasetdemonstrate that the proposed model gives up to 8% higher prediction F1 scorescompared to the state-of-the-art methods.

RadNet: Incident Prediction in Spatio-Temporal Road Graph Networks Using Traffic Forecasting