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Calibrating a traffic flow model with parallel differential evolution

Strofylas Giorgos, Porfyri Kalliroi, Nikolos Ioannis, Delis Anargyros, Papageorgiou Markos

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/769CF98D-026C-4DBE-920A-774D0DB071C2-
Αναγνωριστικόhttps://www.researchgate.net/publication/318495057_Calibrating_a_traffic_flow_model_with_parallel_differential_evolution-
Αναγνωριστικόhttps://doi.org/10.4203/ccp.111.26-
Γλώσσαen-
Μέγεθος20 pagesen
ΤίτλοςCalibrating a traffic flow model with parallel differential evolutionen
ΔημιουργόςStrofylas Giorgosen
ΔημιουργόςΣτροφυλας Γιωργοςel
ΔημιουργόςPorfyri Kalliroien
ΔημιουργόςΠορφυρη Καλλιρροηel
ΔημιουργόςNikolos Ioannisen
ΔημιουργόςΝικολος Ιωαννηςel
ΔημιουργόςDelis Anargyrosen
ΔημιουργόςΔελης Αναργυροςel
ΔημιουργόςPapageorgiou Markosen
ΔημιουργόςΠαπαγεωργιου Μαρκοςel
ΕκδότηςCivil-Comp Pressen
ΠεριγραφήThe research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 321132, project TRAMAN21. en
ΠερίληψηGiven the importance of the credibility and validity required in macroscopic traffic flow models while performing real-word simulations, the necessity of employing an efficient, computationally fast, and reliable constrained optimization scheme for model calibration appears to be mandatory to ensure that the traffic flow characteristics are accurately represented by such models. To this end, a parallel, metamodel-assisted Differential Evolution (DE) algorithm is employed for the calibration of the second-order macroscopic gas-kinetic traffic flow (GKT) model using real traffic data from Attiki Odos freeway in Athens, Greece. The parallelization of the DE algorithm is performed using the Message Passing Interface (MPI), while artificial neural networks (ANNs) are used as surrogate models. Numerical simulations are performed, which demonstrate that the DE algorithm can be effectively used for the search of the globally optimal model parameters in the GKT model; in fact, the method appears to be promising for the calibration of other similar traffic models as well. en
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by-nc-nd/4.0/en
Ημερομηνία2018-04-16-
Ημερομηνία Δημοσίευσης2017-
Θεματική ΚατηγορίαParallel differential evolutionen
Θεματική ΚατηγορίαSurrogate modelsen
Θεματική ΚατηγορίαArtificial neural networksen
Θεματική ΚατηγορίαMacroscopic traffic flow modelingen
Βιβλιογραφική ΑναφοράG. A. Strofylas, K. N. Porfyri, I. K. Nikolos, A. I. Delis and M. Papageorgiou, "Calibrating a traffic flow model with parallel differential evolution," in Fifth International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering, 2017. doi: 10.4203/ccp.111.26en

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