An efficient soft computing-based calibration method for microscopic simulation models

Abstract

ABSTRACTIn recent years, vehicular microscopic simulation models have become one of the main tools used by transportation professionals to analyze transportation policies and projects. Effective use of the existing simulation packages is limited by the calibration of specific parameters based on observed real-life conditions. However, because the calibration of the packages is a resource-intensive process, one might resort to using the default parameter values. In this study, a soft-computing based methodology is proposed that considerably reduces the computation time in comparison to other commonly used methods. The proposed methodology is based on a synergistic combination of artificial neural networks (ANN) and genetic algorithms (GA). First, a Latin hypercube sampling method is used to select representative sets of values for the simulation model's calibration parameters. Second, the effect of each set of parameter values on the simulated traffic stream speed is evaluated. Third, an ANN is trained to determine the relationship between the input parameter values and the output vehicular speed. Finally, a genetic algorithm uses the trained ANN to determine the calibration parameters. Applications of the proposed methodology shows that it allows for less time-consuming calibration of microscopic traffic models compared to other commonly used methods.