基于知识图谱的城市轨道交通突发事件演化结果预测

朱广宇; 张萌; 裔扬

doi:10.11999/JEIT211594

基于知识图谱的城市轨道交通突发事件演化结果预测

doi: 10.11999/JEIT211594

朱广宇^{1, 2, ,},
张萌^{1, 2},
裔扬³

1.
北京交通大学北京市城市交通信息智能感知与服务工程技术研究中心北京 100044
2.
北京交通大学综合交通运输大数据应用技术交通运输行业重点实验室北京 100044
3.
扬州大学信息工程学院扬州 225127

基金项目: 国家自然科学基金(61872037, 62132003, 62272036)，中央高校基本科研业务费(2021YJS309)

详细信息

作者简介:
朱广宇：男，教授，博士生导师，研究方向为交通运输智能自动化、交通运输大数据分析

张萌：女，硕士生，研究方向为城市轨道交通安全评价与控制

裔扬：男，教授，博士生导师，研究方向为智能控制、随机控制和数据建模

通讯作者:
朱广宇　gyzhu@bjtu.edu.cn

中图分类号: TP391; U298
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- 被引次数: 0
出版历程
- 收稿日期: 2021-12-29
- 修回日期: 2022-04-11
- 网络出版日期: 2022-04-17
- 刊出日期: 2023-03-10

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

ZHU Guangyu^{1, 2
, ,},
ZHANG Meng^{1, 2},
YI Yang³

1.
Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing 100044, China
2.
Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China
3.
School of Information Engineering, Yangzhou University, Yangzhou 225127, China

Funds: The National Natural Science Foundation of China (61872037, 62132003, 62272036), The Fundamental Research Funds for the Central Universities (2021YJS309)

摘要

摘要: 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。
- 城市轨道交通 /
- 突发事件 /
- 演化结果预测 /
- 知识图谱 /
- 关系图卷积神经网络
Abstract: 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.
- Urban rail transit /
- Emergency /
- Evolution result prediction /
- Knowledge Graph(KG) /
- Relation-Graph Convolution Neural network(R-GCN)

HTML全文

图 1 城轨突发事件案例知识图谱构建流程

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图 2 单个城轨突发事件案例知识图谱示例

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图 3 R-GCN模型节点更新示意图

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图 4 基于R-GCN的城轨突发事件结果预测方法

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图 5 城轨突发事件案例知识图谱示例(部分)

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图 6 去除“事故地点”因素后与原实验预测结果正确率的对比

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图 7 去除“事故类型”因素后与原实验预测结果正确率的对比

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表 1 城轨突发事件特征信息定义及描述

特征信息	描述
事故原因	人员因素、设备因素、管理因素、环境因素、其他
事故地点	区间正线、车站正线、车站内
事故类型	火灾、脱轨、停电、踩踏、自然灾害、相撞、恐怖袭击、设备故障、其他事故

下载: 导出CSV

表 2 城轨突发事件结果信息定义及描述

结果信息	描述
交通影响	停运(特长时间停运、长时间停运、暂时停运、短时停运)、延误(特长时间延误、长时间延误、暂时延误、短时延误)、运营秩序正常
人员滞留情况	将导致乘客滞留、未导致乘客滞留
衍生灾害	无衍生灾害、车站内踩踏、车站内火灾、隧道内毒气、隧道内火灾、隧道内浓烟、隧道内踩踏、车站内浓烟、列车相撞、车厢内浓烟、乘客滞留隧道、踩踏等
事故等级	重大事故、一般事故、险性事故、特大事故、大事故
运营等级	1,2,3,4,5
轨道交通运营状态	紧急运营状态、正常运营状态、非正常运营状态
伤亡等级	0,1,2,3,4,5

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表 3 城轨突发事件预测结果(%)

结果信息	正确率	结果信息	正确率
交通影响	20.83	衍生灾害	62.50
人员滞留情况	66.67	运营等级	45.83
事故等级	50.00	轨道交通运营状态	66.67
伤亡等级	45.83

下载: 导出CSV

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