In this paper we show a hybrid modeling approach which combines Artificial Neural Networks and a simple statistical approach in order to provide a one hour forecast of urban traffic flow rates. Experimentation has been carried out on three different classes of real streets and results show that the proposed approach outperforms the best of the methods it puts together.

Moretti F, Pizzuti S, Panzieri S, & Annunziato M (2015). Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling. NEUROCOMPUTING [10.1016/j.neucom.2014.08.100].

Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling

PANZIERI, Stefano;
2015

Abstract

In this paper we show a hybrid modeling approach which combines Artificial Neural Networks and a simple statistical approach in order to provide a one hour forecast of urban traffic flow rates. Experimentation has been carried out on three different classes of real streets and results show that the proposed approach outperforms the best of the methods it puts together.
Moretti F, Pizzuti S, Panzieri S, & Annunziato M (2015). Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling. NEUROCOMPUTING [10.1016/j.neucom.2014.08.100].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/138085
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 181
  • ???jsp.display-item.citation.isi??? 150
social impact