Calibrating a microscopic traffic simulation model for roundabouts using genetic algorithms

Abstract

The paper introduces a methodological approach based on genetic algorithms to calibrate microscopic traffic simulation models. The specific objective is to test an automated procedure utilizing genetic algorithms for assigning the most appropriate values to driver and vehicle parameters in AIMSUN. The genetic algorithm tool in MATLAB® and AIMSUN micro-simulation software were used. A subroutine in Python implemented the automatic interaction of AIMSUN with MATLAB®. Focus was made on two roundabouts selected as case studies. Empirical capacity functions based on summary random-effects estimates of critical headway and follow up headway derived from meta-analysis were used as reference for calibration purposes. Objective functions were defined and the difference between the empirical capacity functions and simulated data were minimized. Some model parameters in AIMSUN, which can significantly affect the simulation outputs, were selected. A better match to the empirical capacity functions was reached with the genetic algorithm-based approach compared with that obtained using the default parameters of AIMSUN. Overall, GA performs well and can be recommended for calibrating microscopic simulation models and solving further traffic management applications that practioners usually face using traffic microsimulation in their professional activities