Space–time collision avoidance and train dynamics constraints-driven optimal control for heterogeneous heavy-haul trains platoon operation considering air brake delay

Abstract

The potential of heavy-haul train platoon operation technology in improving railway freight capacity is increasingly recognised, with extensive trial research currently underway. This study firstly utilising longitudinal train dynamics devised a space–time collaborative collision avoidance model for trainsets, accounting for air brake delay. Secondly, an air brake application strategy was proposed, focusing on collision avoidance and managing speeds on steep ramps. Subsequently, a constraint-driven optimal control scheme tailored for heavy-haul train platooning operations within an open railway environment was developed, which simulates train platooning amidst real-world dynamic traffic flow scenarios, offering a global perspective on railway network operations. Finally, we comprehensively evaluate the impact of line conditions on key platoon control parameters based on realistic railway operation scenarios. Findings show that constraint-driven optimal control, integrating train dynamics and space–time collaborative collision avoidance, effectively replicates the dynamic traits of train platoons. The consideration of air braking delays is crucial due to heterogeneous train formations and steep ramps, which can easily cause collisions. Moreover, on real-world railway lines, the average headway of heavy-haul trains weighing 10,000 tons and below can be compressed to 99.82 s when operating in platoons, significantly enhancing transportation capacity compared to existing semi-automatic blocking systems.