Calibrating a traffic flow model with parallel differential evolutionCalibrating a traffic flow model with parallel differential evolution Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2018-04-162017enGiven 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. 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. http://creativecommons.org/licenses/by-nc-nd/4.0/Fifth International Conference on Parallel, Distributed, Grid and Cloud Computing for EngineeringProceedings of the Fifth International Conference on Parallel, Distributed, Grid and Cloud Computing for EngineeringStrofylas_et_al_PARENG_2017.pdfChania [Greece]Library of TUC2018-04-16application/pdf2.1 MBfree Strofylas Giorgos Στροφυλας Γιωργος Porfyri Kalliroi Πορφυρη Καλλιρροη Nikolos Ioannis Νικολος Ιωαννης Delis Anargyros Δελης Αναργυρος Papageorgiou Markos Παπαγεωργιου Μαρκος Civil-Comp Press Parallel differential evolution Surrogate models Artificial neural networks Macroscopic traffic flow modeling