PURPOSES : The purpose of this study is to build an optimization model using the capacity and initial travel speed of the volume delay functions for network calibration performed in the traffic demand analysis process. METHODS : The optimization model contains an error term between the observed traffic volume and estimated traffic volume, based on the user equilibrium principle, and was constructed as a bi-level model by applying range constraints on capacity and travel time. In addition, we searched the split section to apply the method of adjusting the section instead of adjusting the single link. The optimization model is constructed by applying the warm-start method using the bush of the origin-based model so that parameter adjustment and traffic assignment are repeatedly executed within the model and the convergence of the model configured %RSSE. RESULTS : As a result of analysis using the toy network, the optimization model is that the observed traffic volume is estimated when there are no restrictions and, when the constraint conditions were set, the error with the observed traffic volume and error rate was significantly reduced. As a result of the comparative analysis of the trial-and-error methods, KTDB optimum values, and optimization models in empirical analysis using a large-scale network, the evaluation indexes (e.g., RMSE and %RMSE) were significantly improved by applying the optimization model. CONCLUSIONS : Based on the empirical analysis, the optimization model of this study can be applied to large-scale networks and it is expected that the efficiency and reliability of road network calibration will be improved by repeatedly performing parameter adjustment and traffic assignment within the model.