On Modelling and Analysing Rail Moving-block Signalling by Means of Hybrid Petri Nets

The railway industry is facing the growing demands to improve safety, capacity, and efficiency while adopting advanced digital and automation technologies. This paper presents a Hybrid Petri Nets (HPNs) model to simulate train-following dynamics under a moving-block signalling system. The main purpose is to maintain the safe headway between trains in real-time even during disruptions like unexpected speed reductions. The model allows follower trains to adjust speed adaptively based on the leader’s position, ensuring safety without relying on emergency braking. A numerical simulation is performed to evaluate performance under various minimum headway conditions. The results show how train speed and headway vary under different scenarios, and improve line capacity under moving-block operation. Future work will focus on integrating further real-world features, including station stops, heterogeneous trains, and uncertainties modelling to better reflect operational conditions.