Title A Model Predictive Control scheme for freeway traffic systems based on the Classification and Regression Trees methodology
Authors Oleari, Alberto Nai , DOMÍNGUEZ FREJO, JOSÉ RAMÓN, Camacho, Eduardo F. , Ferrara, Antonella , IEEE
External publication No
Scope Conference Paper
Nature Científica
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963808545&doi=10.1109%2fECC.2015.7331069&partnerID=40&md5=2a0c444601c98200d1af3d1de0fd545f
Publication date 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.
Keywords Algorithms; Forestry; Model predictive control; Optimization; Regression analysis; Traffic control; Classification and regression tree; Control approach; Freeway traffic controls; Macroscopic model; M
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