Macroscopic Traffic Flow Model Calibration Using Different Optimization Algorithms

This study tests and compares different optimization algorithms employed for the calibration of a macroscopic traffic flow model. In particular, the deterministic Nelder-Mead algorithm, a stochastic genetic algorithm and the stochastic cross-entropy method are utilized to estimate the parameter values of the METANET model for a particular freeway site, using real traffic data. The resulting models are validated using various traffic data sets and the optimization algorithms are evaluated and compared with respect to the accuracy of the produced models as well as the convergence speed and the required computation time.