Intelligent Railway Traffic Management Using AI Based Decision Support

In this paper, an AI-based multi-path railway dispatching scheme was introduced. It combines simulation, conflict detection, and decision making based on reinforcement learning to efficiently manage railway traffic. This framework is modeled on railway systems using a simulation-based method, thereby allowing train movements to be dynamically tracked and operational conflicts to be detected. The intelligent decision support mechanism makes use of the agent based on Q-learning, which is used as the adaptive dispatcher controller. The agent learns from interaction with the environment and acquires a strategy to minimize delays and overcome conflicts. The main advantage of the agent based on artificial intelligence technology is that its decisions are proactive and context-sensitive. The experiment performed under varied conditions proved the viability of the suggested approach. While the mean time of delay was 12.5 minutes, which got reduced to 6.35 minutes, indicating a drop of approximately 40-45%. Besides, there were fewer conflicts between the platform and the tracks as compared to other approaches, signifying the ability of the model to manage resources.                As opposed to conventional approaches, such as static planning, dispatching algorithms, and heuristics-based optimizations, the newly designed one enjoys certain advantages over the former in relation to flexibility and efficiency.  In light of real-time simulation and optimization in accordance with a learning paradigm, the system is capable of coping with dynamic situations arising in railway transport. Finally, it can be seen that the results have shown that the proposed model has been more efficient, minimized any delays, and also made decision-making much easier in railway operations. The model, which includes simulation, artificial intelligence, and adaptive control, would be suitable for managing the railways in the future.

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