| Título |
A Model Predictive Control scheme for freeway traffic systems based on the Classification and Regression Trees methodology |
| Autores |
Oleari, Alberto Nai , DOMÍNGUEZ FREJO, JOSÉ RAMÓN, Camacho, Eduardo F. , Ferrara, Antonella , IEEE |
| Publicación externa |
No |
| Alcance |
Conference Paper |
| Naturaleza |
Científica |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963808545&doi=10.1109%2fECC.2015.7331069&partnerID=40&md5=2a0c444601c98200d1af3d1de0fd545f |
| Fecha de publicacion |
01/01/2015 |
| ISI |
000380485400553 |
| Scopus Id |
2-s2.0-84963808545 |
| DOI |
10.1109/ECC.2015.7331069 |
| Abstract |
Traffic control algorithms based on optimization may not always be applicable on-line, since they are computationally demanding and may not always comply with the typical sampling times adopted for traffic systems. In this paper, we propose a new approach to freeway traffic control based on the Classification And Regression Trees (CART) methodology. In our approach, a standard centralized receding horizon model predictive controller is replaced with a controller based on a set of regression trees, trained in order to reproduce the behaviour of the original controller. The result is a controller which does not need to solve an optimization problem at each time step. This makes it adequate for the on-line usage. The effectiveness of the proposed control approach, designed relying on a macroscopic model, is evaluated in simulation, on a microscopic model of the Grenoble South Ring developed on the basis of real data. |
| Palabras clave |
Algorithms; Forestry; Model predictive control; Optimization; Regression analysis; Traffic control; Classification and regression tree; Control approach; Freeway traffic controls; Macroscopic model; M |
| Miembros de la Universidad Loyola |
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