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A train control system is a system that is geographically and functionally distributed. Its subsystems have a high degree of autonomy. Because of these characteristics, this paper describes a two-layer framework for modeling train control systems. The upper-layer is defined by agents. The lower-layer is the cellular automata (CA) traffic model to simulate the train following dynamic. The CA model delivers the knowledge needed by the agents to make decisions. The interaction between agents can describe the decision-making processes of train control systems to achieve its functions. Its functions are classified into three levels: Service Control Functionality, Signaling Functionalities and Train Operation Functionality. A case study is used to illustrate the applicability of the proposed framework. The study results show that the proposed framework can be successfully used to analyze the influence on traffic flow, which is caused by the train control system. © 2010 WIT Press.

A framework for modeling train control systems based on agent and cellular automata