This paper proposes an innovative multi-thread stochastic optimization approach for the calibration of microscopic traffic simulation models. Combining Quasi-Monte Carlo (QMC) sampling and the Particle Swarm Optimization (PSO) algorithm, the proposed approach, namely the Quasi-Monte Carlo Particle Swarm (QPS) calibration method, is designed to boost the searching process without prejudice to the calibration accuracy. Given the search space constructed by the combinations of simulation parameters, the QMC sampling technique filters the searching space, followed by the multi-thread optimization through the PSO algorithm. A systematic framework for the implementation of the QPS QMC-initialized PSO method is developed and applied for a case study dealing with a large-scale simulation model covering a 6-mile stretch of Interstate Highway 66 (I-66) in Fairfax, Virginia. The case study results prove that the proposed QPS method outperforms other methods utilizing Genetic Algorithm and Latin Hypercube Sampling in achieving faster convergence to obtain an optimal calibration parameter set.
A Study on Climbing Lane in Freeway Uphill Segment by Developing a Microscopic Traffic Simulation Model