An agent-based support system for railway station dispatching

A D-Agent support system is modular designed for railway station dispatching.The D-Agent can behave as a dispatcher by embedding expert dispatching knowledge.MIPL formulations are integrated to optimize the railway traffic control.The D-Agent can keep improving by extending skills and learning.Its ability of communication prepares it to work in a dynamic environment. For those railway stations without being automated, railway traffic dispatching still depends on dispatchers, especially under disturbed circumstances. In this study, an agent-based support system, named D-Agent, is developed to assist human dispatchers to make decisions in station operation. To this end, the common knowledge and possible difficulties concerning a station dispatcher in his/her routine work are firstly studied, and the D-Agent is proposed with the purpose of working out practicable solutions to these challenging tasks as a dispatcher does. Then the general model of the D-Agent is established, containing five basic modules: local database, knowledge base, skill base, reasoning mechanism and communication interfaces. The internal skills of the D-Agent are designed to execute various tasks in different scenarios. Besides, a skill extension of the D-Agent with mathematical formulations is particularly discussed in this paper, to find feasible and optimal traffic control solutions in disturbance situations such as train delays and route conflicts. The D-Agent is designed to learn from its own experimental history in applying different skills, and evaluate the skills by preference weights of alternative solutions in a particular task. This procedure allows the agent to have potential for continuous improvement. To verify the applicability of the proposed support system, a D-Agent for a terminal station of subway is simulated. The numerical example of train delays and route conflicts shows that the D-Agent can generally perform as a station dispatcher in fulfilling the specific tasks, estimate the traffic state in different operation strategies and support the decision-making of favored solutions. Significantly, it indicates that the mathematical methods can also been employed by an intelligent agent.