城市多模式交通网络韧性评估研究综述

张洁斐; 任刚; 唐磊; 杜建玮; 顾厚煜; 宋建华

doi:10.3963/j.jssn.1674-4861.2024.03.011

城市多模式交通网络韧性评估研究综述

doi: 10.3963/j.jssn.1674-4861.2024.03.011

1.
安徽理工大学矿业工程学院安徽淮南232001
2.
东南大学交通学院南京 211189

基金项目:

安徽省高校自然科学重点科研项目 2023AH051218

国家自然科学基金项目 52072068

安徽理工大学高层次引进人才科研启动基金项目 2023yjrc20

详细信息

作者简介:
张洁斐(1991—)，博士，讲师. 研究方向：韧性交通评估与优化. E-mail: 617098059@qq.com

通讯作者:
任刚(1976—)，博士，教授. 研究方向：应急交通组织、交通仿真、韧性交通分析等. E-mail: rengang@seu.edu.cn

中图分类号: U268.6
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出版历程
- 收稿日期: 2023-10-09
- 网络出版日期: 2024-10-21

A Review about Resilience Evaluation for Urban Multimodal Transportation Networks

1.
School of Mining Engineering, Anhui University of Science and Technology, Huainan 232001, Anhui, China
2.
School of Transportation, Southeast University, Nanjing 211189, China

摘要

摘要: 为促进交通韧性研究的发展，聚焦于城市多模式交通网络，对国内外韧性评估领域的相关文献进行总结。阐述了“韧性”的定义与内涵；梳理了基于网络拓扑、基于供需特性、考虑耦合关系的韧性评估指标体系；总结了模型驱动和数据驱动2类韧性评估方法的成果与优劣；探讨了网络设计、应急疏散、网络修复层面的交通网络韧性提升措施，并归纳了韧性优化的模型和算法；最后总结了现有研究不足和未来发展方向。研究结果表明：①复合网络的韧性评估未能充分考虑网络的耦合特性，韧性评估对可变的交通需求和乘客出行行为的刻画不精确；②模型驱动的韧性评估在指标权重的确定上更多依赖主观性；数据驱动的韧性评估重在数据的分析与结果展示，缺乏韧性演变规律与趋势的深度解析；③旨在提升韧性的优化模型在多目标决策、大型网络中的计算效率、真实场景的还原等方面还有待改进。未来研究的建议和展望如下：①在网络的构建、指标的获取上充分考虑复合网络的相依特性，在评估模型的构建上科学反映各系统间的耦合特性；②协同多部门建立完备共享的数据库，探索数据与模型双驱动的网络韧性评估方法，设计高效算法以支持韧性指数的快速精确计算；③将静态离散的韧性评估转化为动态连续的韧性监测，进而分析网络韧性时空演化规律与趋势，探究交通网络韧性演化机理；④精细化的网络韧性决策优化应在数据的分析和模型的构建上加强对真实事件场景的还原，并进一步探索AI智能算法在大型网络优化中的应用。
- 交通工程 /
- 多模式交通网络 /
- 韧性评估 /
- 指标体系 /
- 评估方法 /
- 韧性提升
Abstract: To improve the development of research about transportation resilience, this paper, focusing on urban multimodal transportation networks, summarizes the relevant studies on resilience evaluation in the literature. The definition and connotation of resilience are introduced. The indicators for resilience evaluation are summarized from the perspectives of network topology, supply-demand characteristics, and coupling relationships. The research of model-driven and data-driven resilience evaluation methods are introduced. The advantages and disadvantages of these methods are summarized as well. Fourth, measures to improve the resilience of transportation network are discussed from the perspectives of network design, emergency evacuation, and network restoration. The resilience optimization models and algorithms are summarized as well. The research deficiencies and future development directions are discussed. The results show that: ① the resilience evaluation of composite networks fails to fully consider the coupling characteristics. Besides, resilience evaluation is imprecise to depict variable traffic demand and travelers' travel behavior. ② The determination of indicator weights depend more on subjective judgement in model-driven resilience evaluation. Data-driven resilience evaluation focus on data analysis and result display, but lacks in-depth analysis of resilience evolution. ③ The optimization models targeting resilience improvement need to be improved in multi-objective decision making, computational efficiency in large-scale networks, and reproduction of real scenes. From these results, the suggestions for the future research are as follows: ① in the development of the network and the selection of indicators, the dependence of the composite network needs to be fully considered. Besides, and the coupling characteristics between the systems need to be scientifically reflected in evaluation models. ② It is suggested to cooperate with multiple departments to establish a complete and shared database, to explore the network resilience evaluation methods which are driven by both data and model, and to design high-efficient algorithms to support the rapid and accurate calculation of the resilience indicators. ③ The static discrete resilience evaluation should be developed into dynamic continuous resilience monitor, based on which the temporal-spatial evolution of network resilience and the evolution mechanism of traffic network resilience must be analyzed. ④ The refined network resilience decision optimization should be strengthen to reproduce the real event scenarios in data analysis and model development. Besides, it is necessary to further explore the application of AI algorithm to deal with the application of large-scale network optimization.
- traffic engineering /
- multimodal transportation networks /
- resilience evaluation /
- indicators system /
- evaluation method /
- resilience improvement

HTML全文

图 1 2006—2021年多模式交通韧性评估载文量分布图

Figure 1. Distribution of research papers of multimodal transportation resilience evaluation from 2006-2021

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图 2 WOS关键词共现图谱分析

Figure 2. WOS keywords co-occurrence pattern analyze

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图 3 韧性视角下交通网络性能变化示意图

Figure 3. Performance change of transportation network in resilience perspective

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图 4 网络拓扑指标频次图

Figure 4. Distribution of network topological indicators

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图 5 网络供需特性指标频次图

Figure 5. Distribution of network supply-demand indicators

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图 6 交通网络性能改变图(忽略扰动降级和扰动后的稳态)

Figure 6. Performance change of transportation network(Ignoring disturbance degradation and steady state after disturbance)

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表 1 韧性评估数据输入

Table 1. Resilience evaluation data input

对象	数据输入
基于网络拓扑的韧性评估	①网络结构：有向图、无向图；将社会、经济、人口数据、最短路径长度等作为节点或路段的权重
基于网络拓扑的韧性评估	②外部数据：人口、社会、经济数据、地理空间、气象数据等
基于供需特性的韧性评估	①网络结构: 有向加权图
	②路段属性：通行能力、长度、自由流下的速度及时间
	③节点属性：节点容量
	④固定或可变的OD需求矩阵
	⑤网络实际的运行状态：被满足的OD需求总量、各路段的流量、速度、延误等
	⑥出行成本：出行时间、票价等
	⑦外部数据：人口、社会、经济数据、地理空间、气象数据等
考虑耦合关系的韧性评估	①交通网络特性：网络供需特性的部分指标
	②其他特性：响应时间、恢复时间、修复资源充足性等
	③外部数据：人口、社会、经济数据、地理空间、气象数据等

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表 2 韧性优化模型与算法

Table 2. Resilience optimization model and algorithm

层面	韧性指数形式	优化目标所选取的性能参数	算法
网络设计	离散型	可替代的不相关路径最大化^[11]、出行时间最小化^[11]	NSGA-II算法^[11]
应急疏散	离散型	出行成本^[52]、资源利用率^[53]、被满足的出行需求^[53]	禁忌搜素算法^[52]、NSGA-II算法^[53]
	离散型	出行时间与路段流量乘积之和^[45]	整数L-shaped分解算法^[45]
网络修复	积分型	网络效率^[4]、网络可达性^[54]	枚举法^[4]、Lingo软件^[54]
网络修复	组合型	平均速度^[39]、可替代的不相关路径^[55]、OD需求满足率^{[35, 56]}、恢复速度^{[35, 56-57]}、出行时间^[57]	GA算法^{[35, 55-56]}、NSGA-II^[39]、禁忌搜索算法^[57]

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