城市轨道交通运营突发事件风险动态演化模型

范博松; 邵春福; 赵丹; 马社强

doi:10.3963/j.jssn.1674-4861.2024.03.013

城市轨道交通运营突发事件风险动态演化模型

doi: 10.3963/j.jssn.1674-4861.2024.03.013

1.
中国人民公安大学交通管理学院北京 100038
2.
新疆大学交通运输工程学院乌鲁木齐 830046

基金项目:

国家自然科学基金项目 52072025

中央高校基本科研业务费项目 2024JKF02ZK12

详细信息

通讯作者:
范博松(1996—)，博士，讲师. 研究方向：交通安全. E-mail: fanbosong@ppsuc.edu.cn

中图分类号: U268.6
计量
- 文章访问数: 107
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- 被引次数: 0
出版历程
- 收稿日期: 2023-12-28
- 网络出版日期: 2024-10-21

Modelling on the Risk Dynamic Evolution of Urban Rail Transit Operation Emergency

1.
School of Traffic Management, People's Public Security University of China, Beijing 100038, China
2.
School of Transportation Engineering, Xinjiang University, Urumqi 830046, China

摘要

摘要: 为了分析城市轨道交通运营突发事件的动态演化特征，探究影响城市轨道交通正常运营的风险致因，研究了城市轨道交通运营突发事件风险动态演化模型。采用bow-tie模型将运营突发事件的风险致因、预估时间裕度和事件严重程度有机组合，构建了风险动态模态，能够反映不同时刻城市轨道交通系统运营的风险状态。基于复杂网络模型，引入连边权重和结构洞理论改进节点的度分布，提出风险动态演化模型，表征风险动态模态及其演化过程。依托北京市轨道交通运营突发事件数据，探究运营突发事件的演化规律和重要风险致因。结果表明：北京市城市轨道交通运营突发事件网络的风险动态演化模型属于无标度网络，19.90%的风险动态模态承担了整个系统77.76%的动态演化过程；风险动态演化模型具有鲁棒且脆弱性，“较严重”“严重”模态对应的风险致因分别为“列车兑现率”“正点率”，这些风险致因给系统的动态演化造成了严重后果。因此，需要重点关注可能带来严重后果的风险致因，并根据系统的动态演化特征开展精准化的风险防控与韧性提升工作。
- 城市交通 /
- 运营突发事件 /
- 风险动态演化模型 /
- bow-tie模型 /
- 复杂网络 /
- 风险动态模态
Abstract: In order to analyze the dynamic evolution characteristics of urban rail transit operation emergencies, and to explore risk factors affecting normal operations, this paper investigates a dynamic evolution model for such emergencies. The bow-tie model is used to integrate the causes of operational risks, estimated time margins and the severity of the emergencies, developing a risk dynamic model which reflects the operational risk status of urban rail transit systems at different moments. Based on a complex network model, the degree distribution of nodes is improved by introducing connected edge weights and structural hole theory, leading to the development of a risk dynamic evolution model which characterizes risk dynamic modes and their evolution process. Relying on the data from Beijing urban rail transit emergency operations, the research explores the evolution patterns of operation emergencies and identifies significant risk factors. The results show that the risk dynamic evolution model for Beijing urban rail transit operation emergency network exhibits the characteristics of a scale-free network, where 19.90% of risk dynamic modes account for 77.76% of the dynamic evolution process of the whole system. The risk dynamic evolution model demonstrates both robustness and fragility, with"train fulfillment"and"punctuality"identified as risk factors for the"more severe"and"severe"modes, respectively. These few but critical risk factors have significant consequences for the dynamic evolution of the system. Therefore, it is necessary to focus on risk factors that may bring serious consequences, and carry out targeted risk prevention, control and resilience enhancement, according to the dynamic evolution characteristics of the system.
- urban traffic /
- emergency /
- risk dynamic evolution model /
- bow-tie model /
- complex network /
- risk dynamic mode

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图 1 风险模态信息的组合

Figure 1. Combination of risk mode information

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图 2 风险动态模态到复杂网络的映射

Figure 2. Mapping risk dynamic modes to complex network

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图 3 模态演化概率散点图

Figure 3. Mode evolution probability scatter plot

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图 4 模态演化概率拟合结果

Figure 4. Mode evolution probability fitting results

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图 5 模态演化概率幂律分布线性拟合图

Figure 5. Linear fitting plot of power law distribution of mode evolution probability

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图 6 模态演化概率与结构洞等级比率关系图

Figure 6. Relationship between mode evolution probability and structural hole rank ratio

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图 7 模态演化概率与结构洞等级比率拟合结果

Figure 7. Mode evolution probability and structural hole rank ratio fitting results

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图 8 模态演化概率与结构洞等级比率分布线性拟合图

Figure 8. Linear fitting plot of mode evolution probability and structural hole rank ratio

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表 1 27个风险致因描述

Table 1. 27 risk causes and their descriptions

类别	变量	名称	变量描述
列车计划	x₁	实际开行列数/列	每日轨道交通实际开行列车数
完成情况	x₂	列车兑现率/%	每日路网实际与计划开行列车比值
列车晚点情况	x₃	正点率/%	每日路网实际开行列车正点到达比率
列车晚点情况	x₄	2 min晚点列车数/列	每日路网晚点超过2 min的列车数
环境因素	x₅	工作日	工作日、非工作日取值：0, 1
	x₆	天气	恶劣天气、非恶劣天气取值：0, 1
	x₇	线路	该线路是否发生突发事件取值：0, 1
路网客流情况	x₈	日路网客运量/(万人·次)	每日轨道交通路网客运量
	x₉	1号线断面满载率/%	每日高峰时段各条线路断面满载率平均值
	x₁₀ ~ x₂₆
	x₂₇	机场线断面满载率/%

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表 2 突发事件严重程度等级取值表

Table 2. Severity levels of emergencies

严重程度等级	严重程度取值
特别严重(Extremely High，EH)	10
严重(High，H)	5
较严重(Medium，M)	2
不严重(Low，L)	1

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表 3 某日路网及各线路人员伤亡、列车调整及列车延误情况

Table 3. Casualties, train adjustments and delays to the network and lines on a given day

	运营企业及线路	人员伤亡	列车调整					列车延误
	运营企业及线路	人数/人	停运/列	通过/列	清人/列	掉线/列	中折/列	5 min及以上延误事件/次
北京地铁	1号线
	2号线	1
	5号线		1			2		1
	6号线		7	1	6		6
	7号线
	8号线
	9号线
	10号线				2	2		1
	13号线
	15号线
	昌平线
	房山线
	亦庄线
	八通线
	机场线
	S1线
	小计	1	8	1	8	4	6	2
京港地铁	4-大兴线
	14号线(西段)
	14号线(东段)
	16号线(北段)
	小计	0	0	0	0	0	0	0
路网		1	8	1	8	4	6	2

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表 4 重要风险动态模态统计分析(前80个)

Table 4. Statistical analysis of important RDM (Top 80)

风险动态模态	节点强度	演化概率/%	风险动态模态	节点强度	演化概率/%
M={x₈, L₂, C₁}	83	11.46	M={x₆, L₁, C₁}	3	0.41
M={x₃, L₃, C₁}	51	7.04	M={x₁₃, L₂, C₁}	3	0.41
M={x₁₀, L₄, C₁}	36	4.97	M={x₁₆, L₃, C₁}	3	0.41
M={x₁₂, L₄, C₁}	34	4.70	M={x₂, L₂, C₂}	3	0.41
M={x₂₈, L₁, C₁}	33	4.56	M={x₁₂, L₁, C₁}	3	0.41
M={x₁₃, L₅, C₁}	29	4.01	M={x₁₁, L₁, C₁}	3	0.41
M={x₁₄, L₅, C₁}	25	3.45	M={x₄, L₂, C₁}	3	0.41
M={x₂, L₃, C₁}	24	3.31	M={x₆, L₂, C₁}	3	0.41
M={x₁₁, L₂, C₁}	24	3.31	M={x₁₀, L₄, C₁}	3	0.41
M={x₁₇, L₄, C₁}	13	1.80	M={x₁₀, L₂, C₂}	3	0.41
M={x₂₀, L₁, C₁}	13	1.80	M={x₂₆, L₄, C₁}	3	0.41
M={x₂₀, L₃, C₁}	13	1.80	M={x₂₁, L₄, C₁}	3	0.41
M={x₅, L₂, C₁}	12	1.66	M={x₅, L₄, C₁}	2	0.28
M={x₆, L₁, C₁}	11	1.52	M={x₁₂, L₅, C₁}	2	0.28
M={x₁, L₁, C₁}	11	1.52	M={x₆, L₃, C₁}	2	0.28
M={x₅, L₃, C₁}	10	1.38	M={x₁₅, L₃, C₁}	2	0.28
M={x₁₀, L₅, C₁}	9	1.24	M={x₁₈, L₃, C₁}	2	0.28
M={x₁₆, L₄, C₁}	9	1.24	M={x₅, L₅, C₂}	2	0.28
M={x₁₀, L₃, C₁}	9	1.24	M={x₁₀, L₁, C₁}	2	0.28
M={x₁₀, L₂, C₁}	8	1.10	M={x₂₄, L₅, C₁}	2	0.28
M={x₄, L₅, C₁}	8	1.10	M={x₆, L₄, C₁}	2	0.28
M={x₉, L₁, C₁}	7	0.97	M={x₇, L₁, C₂}	2	0.28
M={x₇, L₅, C₁}	7	0.97	M={x₂₀, L₄, C₁}	2	0.28
M={x₂, L₁, C₁}	7	0.97	M={x₁₃, L₅, C₂}	2	0.28
M={x₄, L₃, C₁}	6	0.83	M={x₁₈, L₅, C₁}	2	0.28
M={x₁, L₂, C₁}	6	0.83	M={x₉, L₃, C₁}	2	0.28
M={x₁₂, L₃, C₂}	6	0.83	M={x₁₈, L₁, C₁}	2	0.28
M={x₅, L₁, C₁}	6	0.83	M={x₁, L₁, C₂}	2	0.28
M={x₅, L₅, C₁}	6	0.83	M={x₁₄, L₂, C₁}	2	0.28
M={x₂₀, L₂, C₁}	6	0.83	M={x₂₁, L₁, C₁}	2	0.28
M={x₁, L₅, C₁}	6	0.83	M={x₁₉, L₁, C₂}	2	0.28
M={x₃, L₂, C₂}	5	0.69	M={x₆, L₄, C₁}	2	0.28
M={x₇, L₃, C₁}	5	0.69	M={x₁₆, L₁, C₁}	2	0.28
M={x₇, L₄, C₁}	4	0.55	M={x₁₀, L₄, C₂}	2	0.28
M={x₂₀, L₅, C₁}	4	0.55	M={x₂₃, L₅, C₂}	1	0.14
M={x₁₇, L₂, C₂}	4	0.55	M={x₂, L₅, C₁}	1	0.14
M={x₅, L₄, C₁}	4	0.55	M={x₂₂, L₅, C₂}	1	0.14
M={x₃, L₂, C₁}	3	0.41	M={x₂₆, L₂, C₁}	1	0.14
M={x₃, L₂, C₃}	3	0.41	M={x₂₂, L₃, C₁}	1	0.14
M={x₈, L₃, C₁}	3	0.41	M={x₁₆, L₃, C₁}	1	0.14
合计：节点强度和563(前40个)，演化概率和77.76% 节点强度和649(前80个)，演化概率和89.64%

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表 5 高结构洞等级比率风险模态对应的演化路径

Table 5. Evolution path corresponding to high structural hole ratio risk mode

路径	路径信息
路径片段9~18	{x₂₄, L₃, C₁}→{x₂₄, L₃, C₁}→
	{x₇, L₄, C₁}→{x₁₃, L₃, C₁}→
	{x₁₃, L₃, C₁}→{x₁₄, L₁, C₁}→
	{x₅, L₁, C₁}→{x₅, L₄, C₂}→
	{x₇, L₅, C₁}→{x₇, L₂, C₁}
路径片段97~107	{x₈, L₄, C₁}→{x₁₀, L₂, C₂}→
	{x₂₄, L₂, C₁}→{x₁₅, L₅, C₁}→
	{x₁₅, L₅, C₁}→{x₁₂, L₃, C₁}→
	{x₁₇, L₄, C₁}→{x₁₆, L₃, C₁}→
	{x₁₄, L₂, C₁}→{x₂₁, L₂, C₂}

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