Status and Prospects of Studies on Urban Rail Transit Resilience
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摘要: 具有良好韧性水平的城市轨道交通系统能够有效应对因自然灾害、人为失误、设施设备故障等因素导致的突发事件。为充分把握国内外城市轨道交通系统韧性相关的研究动态，运用文献计量分析法分析了相关研究文献的关键词和研究热点，发现早期的研究重点为轨道硬件结构韧性，运营服务韧性在近年来逐渐得到关注。结合韧性概念在物理学领域、生态学领域和城市管理领域的发展过程，阐释了城市轨道交通韧性的内涵。面向典型的社会事件和自然灾害场景，韧性评估方法的研究对象由车站扩展至线路再到线网，但在当前技术条件下评估对象的规模与颗粒度存在博弈关系，尚未完全挖掘宏微观、动静态对象间的联动影响机理。在韧性评估指标方面，对基于拓扑结构、运输能力、综合性能和业务环节的指标体系进行梳理，发现还可进一步从空间布局、工程条件、设施设备、人员配置、管理手段、社会力量等方面丰富现有指标体系。在指标度量方面，梳理了基于性能曲线的韧性建模、大数据分析、仿真模拟和数值分析共4种典型方法，发现基于单一方法的度量结果易受数据量、假设条件、指标权重分配等因素的影响，应综合使用多种方法度量不同类型和评估阶段的指标。在韧性提升和恢复方面，总结了包含事前预防、事中适应和事后恢复阶段的策略，发现既有研究多从运营管理角度出发，城市轨道交通基础设施灾后恢复的相关研究处于初步探索阶段。最后从①提升突发场景建模真实性；②考虑沿线城市空间和功能的影响进行动态细粒度分析；③探究突发事件影响传播机理以刻画系统内部变化；④研究韧性评估与提升效果验证方法共4个方面展望未来轨道交通系统韧性研究的发展方向。Abstract: An urban rail transportation system with high-level resilience is able to effectively respond to emergencies caused by natural disasters, human error, equipment failure, and other factors. In order to fully grasp the research trends related to the resilience of urban rail transit, the keywords and hotspots are analyzed with bibliometric analysis. Early studies focuses on the resilience of track structure, while transportation service resilience has gradually gained attention in recent years. Based on the development process of resilience concept in the fields of physics, ecology, and urban management, the connotation of urban rail transit resilience is explained. Oriented to typical social events and natural disaster scenarios, the scope of resilience evaluation is extended from stations to lines and then to networks. However, under current technological conditions, there is a trade-off relationship between the scale and granularity of resilience evaluation. The linkage mechanisms between macro/micro and dynamic/static objects are not fully explored. In terms of resilience evaluation indicators, the indicators system based on topology, transportation capacity, comprehensive performance and operation flow are summarized. The existing indicator system is able to be further enriched in terms of spatial layout, engineering conditions, facilities, equipment, staffing, management and social forces. Four typical indicator measurement methods are sorted out, which include performance curve based on resilience modeling, big data analysis, simulation and numerical analysis. The measurement results based on a single method are easily affected by factors such as the amount of data, hypothetical conditions, and indicator weights. Multiple methods should be comprehensively used to measure indicators in different types and evaluation stages. Resilience enhancement strategies for pre-prevention, in-process adaptation and post-disaster recovery are also discussed. Most existing research primarily approaches from a perspective of operational management, and related studies on post-disaster recovery of infrastructure are still in an initial exploratory stage. Future research on urban rail transit resilience is expected from four aspects: ①Improving the authenticity of emergency scenarios modeling. ②Dynamic fine-grained analysis integrating spatial and functional division of the city. ③Exploring the propagation mechanism of emergencies to characterize system internal changes. ④Verifying effectiveness of resilience evaluation and improvement methods.
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图 2 2018—2023年国内外轨道交通韧性研究年发文量
Figure 2. Annual publication volume of research on urban rail transit resilience from 2018 to 2023
图 3 城市轨道交通韧性研究关键词分布
Figure 3. Distribution of keywords of international research on urban rail transit resilience
图 4 突发事件下城市轨道交通线网运输能力变化图
Figure 4. Changes in transportation capacity of urban rail transit network under emergency
表 2 常用韧性度量模型
Table 2. Resilience evaluation models
模型分类 模型函数 模型特征 基础模型1：系统韧性三角形模型[9] $R=\int_{t_0}^{t_1}[100-\phi(t)] \mathrm{d} t $
式中：ϕ(t)为系统性能；t0为扰动开始时间；t1为恢复到正常状态时间衡量性能损失累积值，但难以反应性能的变化过程特征 基础模型2：实际性能曲线与时间轴围成的面积和时间的比值[35] $ R=\frac{\int_{t_0}^{t_1} \phi(t) \mathrm{d} t}{t_1-t_0}$
式中：ϕ(t)为系统性能；t0为扰动开始时间；t1为恢复到正常状态时间反应突发事件过程中系统的平均剩余性能，但系统性能极值对结果影响较大 拓展模型1：实际性能曲线与时间轴围成的面积和目标性能曲线与时间轴围成的面积之比值[36] $ R=\frac{\int_{t_0}^{t_1} P(t) \mathrm{d} t}{\int_{t_0}^{t_1} T P(t) \mathrm{d} t}$
式中：P(t)为实际系统性能；TP(t)为目标系统性能量化性能下降的百分比程度，但目标性能测算条件的界定会对结果产生较大影响 拓展模型2：系统恢复性能与损失性能比值[37] $\begin{gathered} R_\phi\left(t \mid e^j\right)=\frac{\phi\left(t \mid \mathrm{e}^j\right)-\phi\left(t_d \mid \mathrm{e}^j\right)}{\phi\left(t_0\right)-\phi\left(t_d \mid \mathrm{e}^j\right)}, \\ t \in\left(t_s, t_f\right) \end{gathered} $
式中：ϕ(t|ej)为系统遭受突发事件ej在时刻t下的性能；ϕ(td|ej)为系统最低性能；ϕ(t0)为突发事件前的系统功能；ts为系统性能开始恢复的时刻；tf为系统性能恢复阶段结束的时刻度量某1个时刻的性能下降百分比，且该模型对系统理想性能的界定更为清楚 
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表 3 城市轨道交通常用韧性提升策略
Table 3. Resilience enhancement strategies for urban rail transit
阶段 韧性提升策略 事前 应急设施设备选址与配置、网络结构及衔接优化等 事中 运营调整：赶点运行、扣车、变换交路、退车等 < br > 接驳服务：邻线协同调整、城市公交接驳等 事后 车站及区间设施维修恢复、运营恢复等
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