This research addresses the challenge of OD matrix calibration in transport modeling, where an initial OD matrix is available alongside traffic data collected from sensors on specific road segments. We propose a refined Simulation-Optimization algorithm that improves the accuracy of transport simulations while preserving the consistency of historical demand patterns. A key aspect of the calibration process is determining the calibration parameter α, which governs the trade-off between the historical OD matrix and the observed traffic count data. The approach identifies the value of α that better maintains both the integrity of historical OD matrices and the accuracy of simulated traffic flows. This approach, employing the PTV Visum simulation tool and heuristic techniques, is tested on a real-world urban network and is designed to be robust, scalable, and applicable to a variety of transport networks.