Using synchronous and asynchronous parallel Differential Evolution for calibrating a second-order traffic flow model. (November 2018) - Strofylas, G.A.; Porfyri, K.N.; Nikolos, I.K.; Delis, A.I.; Papageorgiou, M.
Highlights: A synchronous and asynchronous parallel Differential Evolution algorithm is presented. Two Artificial Neural Networks are used as surrogate models. The algorithm is used to calibrate a second-order traffic flow model. The synchronous and asynchronous versions are compared. The results demonstrate the accuracy and efficiency of the procedure. Abstract: Given the importance of the credibility and validity required by macroscopic traffic flow models in performing real-word simulations, the necessity of including an accurate, computationally fast, and reliable constrained optimization scheme appears to be mandatory to ensure that the traffic flow characteristics are accurately represented by such models. To this end, a parallel, synchronous or asynchronous, metamodel-assisted Differential Evolution (DE) algorithm is employed for the calibration of a second-order macroscopic gas-kinetic traffic flow (GKT) model using real traffic data from Attiki Odos freeway in Athens, Greece. Two Artificial Neural Networks, a Multi-layer Perceptron and a Radial Basis Function network, are used as surrogate models to decrease the computation time of the evaluation phase of the DE optimizer. The parallelization of the DE algorithm is performed using the Message Passing Interface (MPI). Numerical simulations are performed, which demonstrate that the DE algorithm can be effectively used for the search of the global optimal model parameters in the GKT model, while appears to be a promising method for the calibration of other similar traffic models.