Seminario di fisica matematica
ore
16:00
presso - Aula Da Stabilire -
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.