Constructing and Refining Large-Scale Railway Models Represented by Petri Nets

A new method for rapid construction of large-scale executable railway models is presented. Computer systems for railway systems suffer from poor integration and lack of explicit understanding of the large amount of static and dynamic information in the railway. In this paper, we give solutions to both problems. It is shown how a component-oriented approach makes it easy to construct and refine basic railway models by effective methods, such that a variety of models with important properties can be maintained within the same framework. Basic railway nets are refined into several new kinds: nets that are safe, permit collision detection, include time, and are sensitive to its surroundings. Since the underlying implementation language is Petri nets, large expressibility is combined with simplicity, and in addition, the analysis of the behavior of railway models comes gently.