基于知识图谱的城市轨道交通突发事件演化结果预测

准确预测突发事件的演化结果，对城市轨道交通系统制定应急方案、保障安全运营，具有重要的参考意义。目前突发事件演化结果预测方法智能化程度不高，过分依赖决策者主观设定的特征权重、检索模板，复杂、准确性低且应用性较弱。该文基于知识图谱(KG)和关系图卷积神经网络(R-GCN)模型提出一种城市轨道交通突发事件演化结果预测方法。首先，构建城市轨道交通突发事件知识图谱，将与事件相关的场景信息进行结构化处理；其次，基于关系图卷积神经网络模型构建城市轨道交通突发事件结果的预测模型；最后，利用城市轨道交通突发事件案例库进行验证。实验结果表明，所提预测方法具有较好的准确率、较强的普适性，可为轨道交通应急管理提供方法和技术支持。

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.