Short-term traffic forecasting is driven by an increasing need of new services for user information and new systems for dynamic control. Our research focuses on reproducing anticipated traffic conditions by means of statistical methods traditionally applied in artificial intelligence problems. Although we strongly believe that the effects of specific traffic events can only be predicted through transportation model based simulations in real-time, yet the fluctuations affecting the ordinary traffic conditions of a day-type can well be forecasted without.

A hybrid method for real-time short-term predictions of traffic flows in urban areas / Attanasi, Alessandro; Meschini, Lorenzo; Pezzulla, Marco; Fusco, Gaetano; Gentile, Guido; Isaenko, Natalia. - ELETTRONICO. - (2017), pp. 878-883. (Intervento presentato al convegno 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017 tenutosi a Hotel Royal Continental, Napoli, Italia nel 2017) [10.1109/MTITS.2017.8005637].

A hybrid method for real-time short-term predictions of traffic flows in urban areas

Attanasi, Alessandro;Meschini, Lorenzo;PEZZULLA, MARCO;Fusco, Gaetano;Gentile, Guido;Isaenko, Natalia
2017

Abstract

Short-term traffic forecasting is driven by an increasing need of new services for user information and new systems for dynamic control. Our research focuses on reproducing anticipated traffic conditions by means of statistical methods traditionally applied in artificial intelligence problems. Although we strongly believe that the effects of specific traffic events can only be predicted through transportation model based simulations in real-time, yet the fluctuations affecting the ordinary traffic conditions of a day-type can well be forecasted without.
2017
5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017
Affinity propagation; artificial intelligence; bayesian network; neural network; short-term traffic forecasting; modeling and simulation; transportation; Computer Networks and Communications; Artificial Intelligence
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A hybrid method for real-time short-term predictions of traffic flows in urban areas / Attanasi, Alessandro; Meschini, Lorenzo; Pezzulla, Marco; Fusco, Gaetano; Gentile, Guido; Isaenko, Natalia. - ELETTRONICO. - (2017), pp. 878-883. (Intervento presentato al convegno 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017 tenutosi a Hotel Royal Continental, Napoli, Italia nel 2017) [10.1109/MTITS.2017.8005637].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1092215
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