Critical scenarios and their identification in parallel railroad level crossing traffic control systems

Deterministic and stochastic Petri nets (DSPNs) are well utilized as a visual and mathematical formalism to model discrete event systems. This paper proposes to use them to model parallel railroad level crossing (LC) control systems. Their applications to both single- and double-track railroad lines are illustrated. The resulting models allow one to identify and thus avoid critical scenarios in such systems by conditions and events of the model that control the phase of traffic light alternations. Their analysis is performed to demonstrate how the models enforce the phase of traffic transitions by a reachability graph method. Their important properties are verified. To our knowledge, this is the first work that employs DSPNs to model a parallel railroad LC system and identify its critical scenarios for the purpose of their complete avoidance. This helps advance the state of the art in traffic safety related to the intersection of railroads and roadways.