Decision Support in the Railway Accident Rescue by Hybrid Reasoning

In the paper, we employ a hybrid reasoning model to aid the decision making in railway accident rescue. A coarse-to-fine model is proposed which combines the Rule-Based-Reasoning (RBR) and Cased-Based-Reasoning (CBR). In section 2, we present the framework of our method. Further, we investigate on the detail of the implementation for RBR and CBR in decision support for railway accident rescue in Section 3. Specifically, Self-Organizing Feature Map (SOFM) is applied for the case matching, which is the key part of CBR. At last, we validate our method in the experiment and give a typical way to apply the method for practical use. The contribution of this article is proposing an automatic decision making method for railway accident rescue, which combines the theoretical and empirical knowledge. The method proposed in the article can solve complicated railway rescue problem and help people to respond to accident faster and more effectively.