Prediction of Evolution Results of Urban Rail Transit Emergencies Based on Knowledge Graph

Accurately predicting the evolution process and results of emergencies is of great reference to formulate the emergency response plans of the urban rail transit system and safeguard its secure operation. However, the prediction methods of emergency evolution results are lack of high intelligence, and excessively depend on the feature weighting and retrieval template set subjectively by policymakers, which is complicated, inaccurate, and short of applicability. Based on Knowledge Graph(KG) and Relational-Graph Convolution Neural network(R-GCN), a predicting method of evolution result of urban rail transit emergencies is proposed. A knowledge graph of urban rail transit emergencies is constructed to combine with contextual information related to the emergency for structured processing. Firstly, the knowledge graph of urban rail transit emergencies is constructed to combine with contextual information related to the emergency for structured processing. Then the predicting model of urban rail transit emergencies is constructed based on the relational graph convolution neural network to achieve the result prediction of urban rail transit emergency. Finally, the verification is conducted via case base of urban rail transit emergency. The experimental result demonstrates that the predicting method proposed in this paper is of high accuracy and applicability, which can provide consolidated data and decision support for rail transit emergency management.