Focusing Belief Propagation: traffic slowdowns prediction.

Belief Propagation (BP) is an iterative message passing algorithm that can be used to derive marginal probabilities on a system within the Bethe-Peierls approximation. It is not well understood how this deep learning method is able to learn and how it doesn't get trapped in configurations with low computational performance. Since we aim to classify the congestion situations, we analyze the fundamental diagram of traffic which gives a relation between the traffic flow and the traffic density. A traffic congestion occurs when the density of the road grows up and the flow decreases. In order to predict congestion situations, we train the BP neural network using binarized vectors obtained by the processing of the fundamental diagram. We apply our method to real data which have been recorded by traffic detectors provided by Emilia Romagna region.