Map Matching Algorithms (MMA) are developed to solve spatial ambiguities that arise in the process of assigning GPS measurements onto a digital roadway network. There is a lack of systematic parameter tuning approaches for optimizing the MMA performance. Thus, a novel integrated framework is proposed for a systematic calibration of the parameters of a post-processing MMA. The calibration approach consists of an Instance-specific Parameter Tuning Strategy (IPTS) that employs Fuzzy Logic principles. The proposed fuzzy IPTS tool determines the best algorithm parameter values by using instance-specific information a priori to the execution of the MMA. A preliminary prototype of an IPTS system is designed based on real-world data, which identifies the explanatory variables that condition the MMA performance. The implementation of the fuzzy IPTS tool on real-word data yields an enhanced MMA performance in the solution quality and computational time compared to the results of the execution of the MMA with constant algorithm settings.