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, 167, 3-7 [10.1016/j.neucom.2014.08.100].
Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling
Moretti, Fabio;Pizzuti, Stefano;Panzieri, Stefano;
2015-01-01
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.File in questo prodotto:
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