dc.creator | Chanfreut Palacio, Paula | es |
dc.creator | Maestre Torreblanca, José María | es |
dc.creator | Camacho, Eduardo F. | es |
dc.date.accessioned | 2021-05-06T15:54:47Z | |
dc.date.available | 2021-05-06T15:54:47Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Chanfreut Palacio, P., Maestre Torreblanca, J.M. y Fernández Camacho, E. (2020). Coalitional Model Predictive Control on Freeways Traffic Networks. IEEE Transactions on Intelligent Transportation Systems, 1-12. | |
dc.identifier.issn | 1524-9050 | es |
dc.identifier.uri | https://hdl.handle.net/11441/108673 | |
dc.description.abstract | This paper discusses the application of coalitional model predictive control (MPC) to freeways traffic networks, where the goal is reducing the time spent by the drivers through a dynamic setting of variable speed limits (VSL) and ramp metering. The prediction model METANET is used to represent the traffic flows evolution. The system behavior and objective function lead to a non-convex and non-linear optimization problem, which can only be solved in a centralized fashion for small networks. The underlying motivation of this paper is the continued advance of clustering methods in the control of large-scale and spatially distributed systems. The global freeway system is partitioned into a set of coupled sub-stretches, which in turn are assigned to the different agents involved in the control problem. These local controllers can dynamically assemble into coalitions to take coordinated measures. In this work, a top-down approach is considered: the bottom layer consists of the set of controllers that compute the VSL and ramp-metering across time; and the supervisory layer changes periodically the information exchange structure to promote coalitions of those controllers that bring greater performance to the global system. In this way, a balance is sought between optimality and efficiency. Finally, the coalitional approach is simulated on a stretch of traffic freeway where cooperation with adjacent sub-stretches is allowed. | es |
dc.format | application/pdf | es |
dc.format.extent | 12 p. | es |
dc.language.iso | eng | es |
dc.publisher | IEEE (Institute of Electrical and Electronics Engineers) | es |
dc.relation.ispartof | IEEE Transactions on Intelligent Transportation Systems, 1-12. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Distributed model predictive control | es |
dc.subject | Coalitional control | es |
dc.subject | Control by clustering | es |
dc.subject | Traffic systems | es |
dc.title | Coalitional Model Predictive Control on Freeways Traffic Networks | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/abstract/document/9102355/keywords#keywords | es |
dc.identifier.doi | 10.1109/TITS.2020.2994772 | es |
dc.journaltitle | IEEE Transactions on Intelligent Transportation Systems | es |
dc.publication.initialPage | 1 | es |
dc.publication.endPage | 12 | es |