A Parameter Calibration Method of Micro Traffic Simulation Based on Index Coupling

A method is proposed by considering multiple calibration indexes to optimize the parameter calibration of a traffic simulation model, improve the accuracy of the simulation model, and restore real road environments. Guided by simulation results, the calibration parameters for application-specific requirements are determined by sensitivity analysis. Considering the mutual influence of different calibration indexes, a simulation model is calibrated by simultaneously considering multiple calibration metrics which takes the variability of errors across distinct time intervals as the weights, and the objective function of the model calibration is developed based on six velocities. The model is implemented through secondary development of VISSIM software with MATLAB language. 143 groups of parameters are determined by two-stage entropy weight assignment and adaptive adjustment based on immune genetic algorithm. The effectiveness of the proposed method is validated by comparing with several baseline methods in three ways: uniform value, recursive value, and index coupling value approaches. The simulation results indicate a 50% decrease of squared errors exceeding 0.01 for the main line speed, and a 60% reduction for large trucks across various time periods. Regarding speed, the existing errors are 5% and 1.5% for main passenger cars and large trucks, reduced by 7% and 5.2%, respectively. The errors of estimated speeds for small trucks and ramps remains at approximately 6.5%; The speeds of main passenger cars and main trucks have smaller weights, ranging from 0.15 to 0.2, which indicates smaller variabilities of errors and smaller effects on the objective function. The results show that the proposed calibration method based on index coupling effectively takes into account both the in teraction of multiple indexes and the error of individual index, which mitigates the shortcoming of single-index calibration methods that leads to excessive errors of other indexes.