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Abstract The ever increasing demand in passenger and freight transportation is leading to the saturation of railway network capacity. Virtual Coupling (VC) has been proposed within the European Horizon 2020 Shift2Rail Joint Undertaking as a potential solution to address this problem. It allows to dynamically connect two or more trains in a single convoy, thus reducing headway between them. In this work, we investigate the main challenges related to the potential deployment of VC in railways. Its feasibility through Reinforcement Learning techniques is explored, discussing about technical implementation and performance issues. A qualitative analysis based on a Deep Deterministic Policy Gradient control algorithm is proposed. The aim is to give a first insight towards the definition of a qualitative and technology roadmap which could lead to the deployment of artificial intelligence applications aiming at enhancing rail safety and automation.

Virtual coupling, which provides great advantages in operational flexibility and line capacity, is an advanced signaling concept for the railway industry. The dynamic disturbances caused by line conditions might change the coupled movements of the train convoy into abnormal states, which means the speed difference and separation distance of the adjacent trains exceed the given thresholds. This paper proposes a method to maintain the coupled states using terminal sliding mode control (SMC) based on second-order nonlinear train dynamics, with a nonlinear observer eliminating the estimation error due to time-varying measurement delay. The controller calculates the optimal unit effective tractive force for the following train in real-time, taking the leader velocity and desired separation as control targets. The simulation of a two-train convoy on a high-speed railway is conducted including different abnormal scenarios. The results demonstrate that the proposed method eliminates the observation errors and achieves synchronous convergence of the tracking errors while guaranteeing passenger comfort, and that it outperforms traditional SMC. [ABSTRACT FROM AUTHOR]

The ever increasing demand in passenger and freight transportation is leading to the saturation of railway network capacity. Virtual Coupling (VC) has been proposed within the European Horizon 2020 Shift2Rail Joint Undertaking as a potential solution to address this problem. It allows to dynamically connect two or more trains in a single convoy, thus reducing headway between them. In this work, we investigate the main challenges related to the potential deployment of VC in railways. Its feasibility through Reinforcement Learning techniques is explored, discussing about technical implementation and performance issues. A qualitative analysis based on a Deep Deterministic Policy Gradient control algorithm is proposed. The aim is to give a first insight towards the definition of a qualitative and technology roadmap which could lead to the deployment of artificial intelligence applications aiming at enhancing rail safety and automation. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach in train control systems based on the concept called virtual coupling or train convoys. This approach follows the recent developments in the field of safe platooning of autonomous vehicles. We use a decentralized model predictive control (MPC) framework for each train participating in a convoy formation. Control designs for the leading and following trains are presented. An optimal control formulation for both controllers is used and its design relies on a numeric solution of a finite horizon optimal control problem. This paper compares the proposed method with alternative control strategies including the well-studied moving block train control concept. A study of a metro line has been chosen as a first analysis for this control approach. The simulation results for this kind of railway lines demonstrate better performance and the benefits of this new concept versus the moving block system. We show that the virtual coupling concept substantially reduces headway and distance between trains while guaranteeing safe separation between two consecutive trains at any instant. [ABSTRACT FROM AUTHOR]