Calibration of Microscopic Traffic Flow Simulation Models Using a Memetic Algorithm with Solis and Wets Local Search Chaining (MA-SW-Chains)
Traffic models require calibration to provide an adequate representation of the actual field conditions. This study presents the adaptation of a memetic algorithm (MA-SW-Chains) based on Solis and Wets local search chains, for the calibration of microscopic traffic flow simulation models. The effectiveness of the proposed MA-SW-Chains approach was tested using two vehicular traffic flow models (McTrans and Reno). The results were superior compared to two state-of-the-art approaches found in the literature: (i) a single-objective genetic algorithm that uses simulated annealing (GASA), and (ii) a stochastic approximation simultaneous perturbation algorithm (SPSA). The comparison was based on tuning time, runtime and the quality of the calibration, measured by the GEH statistic (which calculates the difference between the counts of real and simulated links) .
KeywordsCalibration Local search chaining Solis and wets Traffic flow simulation Single-objective optimization Memetic algorithm
The work in this research study was supported by the University of Cauca (Popayan, Colombia) and the University of Nevada Las Vegas, United States. We are grateful to Mr. Colin McLachlan for his help translating the first version of this document.
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