Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
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| URI | http://purl.tuc.gr/dl/dias/769CF98D-026C-4DBE-920A-774D0DB071C2 | - |
| Αναγνωριστικό | https://www.researchgate.net/publication/318495057_Calibrating_a_traffic_flow_model_with_parallel_differential_evolution | - |
| Αναγνωριστικό | https://doi.org/10.4203/ccp.111.26 | - |
| Γλώσσα | en | - |
| Μέγεθος | 20 pages | en |
| Τίτλος | Calibrating a traffic flow model with parallel differential evolution | en |
| Δημιουργός | Strofylas Giorgos | en |
| Δημιουργός | Στροφυλας Γιωργος | el |
| Δημιουργός | Porfyri Kalliroi | en |
| Δημιουργός | Πορφυρη Καλλιρροη | el |
| Δημιουργός | Nikolos Ioannis | en |
| Δημιουργός | Νικολος Ιωαννης | el |
| Δημιουργός | Delis Anargyros | en |
| Δημιουργός | Δελης Αναργυρος | el |
| Δημιουργός | Papageorgiou Markos | en |
| Δημιουργός | Παπαγεωργιου Μαρκος | el |
| Εκδότης | Civil-Comp Press | en |
| Περιγραφή | The 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 |
| Περίληψη | Given 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 |
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Full Paper | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
| Ημερομηνία | 2018-04-16 | - |
| Ημερομηνία Δημοσίευσης | 2017 | - |
| Θεματική Κατηγορία | Parallel differential evolution | en |
| Θεματική Κατηγορία | Surrogate models | en |
| Θεματική Κατηγορία | Artificial neural networks | en |
| Θεματική Κατηγορία | Macroscopic traffic flow modeling | en |
| Βιβλιογραφική Αναφορά | G. 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.111.26 | en |
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