The paper presents a new methodology for knowledge representation and reasoning based on parameterised fuzzy Petri nets. Recently, this net model has been proposed as a natural extension of generalised fuzzy Petri nets. The new class extends the generalised fuzzy Petri nets by introducing two parameterised families of sums and products, which are supposed to provide the suitable t-norms and s-norms. The nature of the fuzzy reasoning realised by a given net model changes variously depending on t-and/or s-norms to be used. However, it is very difficult to select a suitable t-and/or s-norm function for actual applications. Therefore, we proposed to use in the net model parameterised families of sums and products, which nature change variously depending on the values of the parameters. Taking into account this aspect, we can say that the parameterised fuzzy Petri nets are more flexible than the classical fuzzy Petri nets, because they allow to define the parameterised input/output operators. Moreover, the choice of suitable operators for a given reasoning process and the speed of reasoning process are very important, especially in real-time decision support systems. Some advantages of the proposed methodology are shown in its application in train traffic control decision support.