Location Referencing is a well-known methodology to transfer geoobjects from one digital map to another and typically used to share traffic information. Here, especially the dynamic methods play a major role, as they are developed to transfer Location References between different maps in such cases where no common databases and/or common structures are available. The key issue in dynamic Location Referencing is to find the correct geoobject in the target map which corresponds to the geoobject in the source map. So far, in nearly all methods a deterministic algorithm is implemented to perform this. Given the fact that geodata as well as the matching procedure for geodata has some uncertainty, it is obvious to research uncertainty-based algorithms. This paper presents a probability-based decision system by formulating the decision algorithm/functions and evaluating them. The evaluation is done with real traffic information and benchmarked against a deterministic decision system.