Intelligent Processing of Spatio-temporal signals

Intelligent Processing of Spatio-temporal signals


  • Clustering, that is discovery of groups of “similar” trajectories. As an example, the cluster of trajectories they can bring to light the presence of paths not adequately covered from the public transit service.

  • Frequent pattern, that is the discovery of frequent paths. These information could be useful for the city planning, as an example, evidencing frequently covered paths followed by vehicles, that could be the result of planning of the devoid traffic.

  • Classification, that is the discovery of behaviour rules, aiming to explain the behaviour of the running customers and to foretell that one of the future customers. An application could be the pre-allocation of resources.


From the methodological standpoint, the research activity investigates machine learning approaches and specifically neuro-fuzzy models. \