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Traffic simulation models have been used extensively because of their ability to model the dynamic stochastic nature of transportation systems. While the parameter calibration is very complex and difficult to get the optimal results, it is time-consuming especially for the large and complex network. Firstly, the procedure of traffic micro-simulation param- eter calibration was put forward. The VISSIM models was selected for parameter calibration on complex-network, then a Simultaneous Perturbation Genetic Algorithm (SPGA) was examined for use in optimization component, an automatic calibration methodology for micro-simulation models was developed in order to select the best parameter set based on ob- served ITS data and it was effective for different networks. Finally, the methodology was applied to calibration the Bei- jing city VISSIM models, and the convergence rate of Genetic Algorithm (GA), Simultaneous Perturbation Stochastic Approximation (SPSA) and SPGA algorithm were compared. The results show that the SPGA has a good performance on effectiveness and superiority.

A Parameters Calibration Method in Simulated Complex Traffic Network