Virtually Coupled Train Set (VCTS) is an advanced train control technology enabling multiple trains to operate closely through wireless communication, enhancing capacity and operational flexibility. Traditional VCTS control algorithms struggle with complex dynamic models and local optimality, hindering real-time, long-term optimization. This paper proposes an Edge Intelligence (EI) enhanced Monte Carlo Tree Search (MCTS) framework for VCTS Optimal Control (M-VOC). MCTS is a heuristic search algorithm that identifies optimal operational solutions efficiently, focusing on long-term stability over local optimums. EI supports MCTS for real-time decision-making, and we introduce a model-based reinforcement learning algorithm to manage VCTS's complex dynamics. Our framework addresses VCTS control issues in real-time while optimizing long-term benefits. To meet computational and real-time demands, we propose a train-to-edge cooperative computing strategy using multi-intelligence reinforcement learning. Simulations demonstrate that our EI-enhanced MCTS strategy effectively provides cooperative control, ensuring virtually coupled trains operate safely, stably, and punctually with reduced intervals.

The virtual coupling approach has been proposed to improve transportation efficiency, passenger satisfaction and operational excellence for communication based train control (CBTC) systems, and a train formation is built and named virtual coupled train sets (VCTS). In order to ensure the stability of the VCTS, train-to-train (T2T) wireless communications are critical to provide the continuous information exchange channel for adjacent trains. Due to unreliability of wireless communications, jamming attacks are inevitable, which could bring packets loss and time delay and disturb the control performance of VCTS. As the distance between adjacent trains is much less than the traditional headway under the moving block (MB) mode in CBTC systems, the jamming attacks could lead to safety events, which are unacceptable. Therefore, in the paper, an event trigger control (ETC) method is developed to mitigate effects of jamming attacks on VCTS by designing a stability range for VCTS formation and reducing the communications cycle of CBTC systems. This ETC-based cooperative control approach can ensure the stable headway between adjacent trains and improve the transportation efficiency. Simulation results demonstrate availability of the proposed scheme.