  Efficient and accurate incident prediction in spatio-temporal systems is
critical to minimize service downtime and optimize performance. This work aims
to utilize historic data to predict and diagnose incidents using
spatio-temporal forecasting. We consider the specific use case of road traffic
systems where incidents take the form of anomalous events, such as accidents or
broken-down vehicles. To tackle this, we develop a neural model, called RadNet,
which forecasts system parameters such as average vehicle speeds for a future
timestep. As such systems largely follow daily or weekly periodicity, we
compare RadNet's predictions against historical averages to label incidents.
Unlike prior work, RadNet infers spatial and temporal trends in both
permutations, 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 dataset
demonstrate that the proposed model gives up to 8% higher prediction F1 scores
compared to the state-of-the-art methods.
