Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling
| Autor: | Fabio Moretti, Stefano Pizzuti, Stefano Panzieri, Mauro Annunziato |
|---|---|
| Přispěvatelé: | Moretti, F. Annunziato, M., Pizzuti, S., Moretti, F, Pizzuti, S, Panzieri, Stefano, Annunziato, M. |
| Rok vydání: | 2015 |
| Předmět: |
Artificial neural network
neural network business.industry Computer science Cognitive Neuroscience Ensembling Traffic flow Machine learning computer.software_genre Neural network Computer Science Applications traffic flow Artificial Intelligence Bagging Neural networks Traffic flow forecasting Artificial intelligence Data mining business computer |
| Zdroj: | Neurocomputing. 167:3-7 |
| ISSN: | 0925-2312 |
| Popis: | 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. © 2015 Elsevier B.V. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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