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.
Barua et al. (Thu,) studied this question.
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