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地理科学进展, 2023, 42(5): 1012-1024 doi: 10.18306/dlkxjz.2023.05.014

研究综述

城市交通韧性研究进展及未来发展趋势

嵇涛,1,2, 姚炎宏1, 黄鲜1, 诸云强,2,*, 邓社军1, 于世军1, 廖华军3

1.扬州大学建筑科学与工程学院，江苏 扬州 225127

2.中国科学院地理科学与资源研究所，北京 100101

3.北京超图软件股份有限公司，北京 100015

Progress and future development trend of urban transportation resilience research

JI Tao,1,2, YAO Yanhong1, HUANG Xian1, ZHU Yunqiang,2,*, DENG Shejun1, YU Shijun1, LIAO Huajun3

1. College of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China

2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

3. SuperMap Software Co., Ltd., Beijing 100015, China

通讯作者: *诸云强(1977— )，男，江西广丰人，博士，研究员，研究方向为分布式数据共享关键技术、地理空间数据本体与应用、地学知识图谱及应用、资源环境信息系统。E-mail: zhuyq@lreis.ac.cn

收稿日期: 2022-07-25   修回日期: 2023-03-12  

基金资助: 江苏省基础研究计划(青年基金项目)(BK20210833)
中国博士后科学基金面上项目(2021M703175)
江苏省双创博士项目(JSSCBS20211025)

Received: 2022-07-25   Revised: 2023-03-12  

Fund supported: Natural Science Foundation of Jiangsu Province(BK20210833)
China Postdoctoral Science Foundation(2021M703175)
Double-Creation Doctoral Program of Jiangsu Province(JSSCBS20211025)

作者简介 About authors

嵇涛(1988— )，男，江苏扬州人，中国科学院博士后，研究方向为交通大数据分析建模、城市灾害应急救援与辅助决策。E-mail: jitao@yzu.edu.cn

摘要

交通韧性是指在极端条件下交通系统能够通过自身抵抗、减缓以及吸收的方式维持其系统基本功能和结构的能力，或者能够在合理的时间和成本内恢复原始平衡或者新平衡状态的能力。受全球增温、海平面上升以及快速城市化的影响，极端事件的风险日益增加，从而导致城市交通运输基础设施运营面临着严峻的挑战。在此背景下，如何衡量极端事件下城市交通韧性强度(包括不同极端天气事件强度对其强度的影响)，如何监测其时空分布特征和演变趋势，以及多长时间交通运输系统能够恢复正常状态?针对这些问题，目前还缺乏有效的监测方法，尤其是缺乏气候变化对交通韧性影响的时空动态变化监测。因此，如何精准识别极端事件下城市交通韧性的状态，提升自然灾害交通防治水平亟待解决。而随着大数据挖掘技术和时空预测深度学习方法的发展，为重建城市交通韧性强度时空数据集，进而揭示历史极端事件影响下城市交通韧性强度时空演变特征、变化趋势以及影响机制提供了可能。论文对国内外近50年来交通韧性研究进行了梳理和概括，结合国内外交通韧性的相关研究成果对已有的研究中存在的不足进行了评述；并指出了气候变暖情况下交通韧性研究的重点领域和方向，旨在为今后开展交通韧性研究提供新的思路。

关键词： 交通韧性; 极端事件; 气候变化; 时空演变; 未来趋势

Abstract

Urban transportation resilience reflects the ability of the transportation system to maintain its basic functions and structure through its resistance, mitigation, and absorption under extreme conditions, or the ability to restore the original equilibrium or reach a new equilibrium state within a reasonable time and with reasonable cost. Global warming, sea-level rise, and rapid urbanization all increase the risk of compound extreme weather events, presenting challenges for the operation of urban-related infrastructure including transportation infrastructure. In this context, some questions become important. For example, how to measure the strength of urban transportation resilience under extreme weather events (including the impact of different extreme weather event intensities on its strength); how to monitor its spatial and temporal features and evolution trends; and how long will it take for the entire system to restore balance? At present, effective monitoring methods for transportation resilience under the influence of extreme events are lacking, especially the monitoring of the temporal and spatial dynamic changes of transportation resilience under climate change, to answer these questions. Therefore, it is urgently needed to solve the problem of accurately identifying the state of urban transportation resilience under extreme weather events and improving the level of prevention and control of transportation system impact of natural hazard-related disasters. The development of big data mining technology and deep learning methods for spatiotemporal prediction made the construction of spatiotemporal datasets for evaluating and predicting urban transportation resilience possible. Such datasets can reveal the spatiotemporal evolution features, changing trends of urban transportation resilience intensity under the influence of extreme weather events, as well as the mechanism of influence. It indicates the key research areas that should be focused on for transportation resilience under climate warming. This article reviewed and summarized the research on transportation resilience in China and internationally in the past 50 years, analyzed the deficiencies in the existing research based on the relevant research results of transportation resilience in China and globally, and identified the key areas and directions of the research on transportation resilience under climate warming in order to provide new ideas for future research on transportation resilience.

Keywords： transportation resilience; compound extreme weather events; climate change; spatiotemporal dynamics; future trends

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本文引用格式

嵇涛, 姚炎宏, 黄鲜, 诸云强, 邓社军, 于世军, 廖华军. 城市交通韧性研究进展及未来发展趋势[J]. 地理科学进展, 2023, 42(5): 1012-1024 doi:10.18306/dlkxjz.2023.05.014

JI Tao, YAO Yanhong, HUANG Xian, ZHU Yunqiang, DENG Shejun, YU Shijun, LIAO Huajun. Progress and future development trend of urban transportation resilience research[J]. Progress in Geography, 2023, 42(5): 1012-1024 doi:10.18306/dlkxjz.2023.05.014

城市的不断扩张给城市交通系统带来了巨大挑战。由于城市化进程中常住人口的流动性需求不断增长，交通需求也会随之大幅增加，同时引发交通拥堵、环境污染和交通事故等一系列负面问题[1]。而交通拥堵直接影响城市交通服务的质量，如人员和货物的流动，从而大大降低城市的可达性和流动水平[2]。此外，它还会增加延误、能源消耗和大气污染，进一步还会导致生产力水平下降以及社会生活成本上升[3]。因此，交通管理部门需要充分规划交通网络，控制交通流动，以确保交通系统顺畅运行和减轻流动压力[1]。

城市交通系统总是受到不同类型事件的干扰，如自然灾害(台风、暴雨、洪水等)或人为事件(恐怖袭击、罢工和由人为错误或管理不善引起的系统故障等)[4⇓-6]。由于城市中极端事件发生的风险不断增加，这种风险与城市不断扩张和人口城镇化进程加快密切相关，并且有研究发现城市交通系统抵御未来极端事件和灾害的脆弱性和临界性风险也在增大[7-8]。因此，韧性的概念也在交通系统中被广大学者所关注。而为应对全球气候变化以及自然灾害等环境因素影响[9]，并提升交通基础设施的高质量发展，国家先后出台了《交通强国建设纲要》《国家综合立体交通网指标框架》以及《“十四五”现代综合交通运输体系发展规划》，要求建设现代化高质量综合立体交通网，建立自然灾害交通防治体系，提高交通防灾抗灾能力，提升交通网络系统的安全性，增强交通运输网络韧性[10]。

交通系统作为国家重要的基础设施和民生工程，为社会物资和居民出行提供运输服务保障。而基础设施在城市可持续性中发挥着至关重要的作用，并且基础设施韧性作为城市韧性的重要组成部分，其中交通系统作为维持城市正常运转的关键要素，其韧性的提升直接关乎城市社会经济发展和人民生活质量保障[11-12]。而目前没有一种可靠的方法能够准确揭示极端事件影响下城市交通韧性时空演变过程，科学阐明全球气候变化(气候变暖造成极端天气事件发生频率及强度变化)对城市交通韧性的影响。因此，本文通过对已有的城市交通韧性相关研究进行整理，综述目前对城市交通韧性的研究进展，对已有的研究中存在的不足进行评述，并提出在气候变化情况下未来极端事件对城市交通韧性研究的具体方向，旨在为今后开展相关研究提供新的思路。

1 城市交通韧性的研究进展

城市交通韧性的研究大约起始于20世纪70年代[13]，从90年代开始交通运输系统韧性研究关注度不断上升。根据Web of Science(WoS)核心合集数据库和中国知网(CNKI)北大中文核心数据库历年国内外发表的交通韧性论文统计发现(图1)，英文期刊发表的数量从2000年以来的零星发表到近年来的爆炸式增长(发文量占比也从不到1%快速跃升到23%左右)，其中2010—2021年交通韧性的研究论文呈现出较快增长趋势，尤其是从2018年之后，有关交通韧性的研究论文呈现出近乎直线式上升；而中文期刊关于交通韧性的研究相对起步较晚(2006年1篇)，并从2017年开始才逐步呈现出缓慢上升的趋势(2020年14篇，2021年6篇)，但总体来看，国内相关交通韧性的研究仍处于初步探索阶段。

图1

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图1   2000—2021年中英文期刊交通韧性相关研究历年发表量时序分布及占比

注：发文量占比指某一年发文量占总发文量的百分数。

Fig.1   Temporal distribution and percentage of transportation resilience-related publications during 2000-2021


从目前交通韧性的研究内容分析，国内外现有的相关研究主要集中在概念框架、指标体系评估方法以及定量评估优化等方面，并在上述相关领域取得了丰硕的研究成果[14]。

1.1 交通韧性概念框架研究现状

韧性(Resilience)起源于拉丁语“Resilio”，又被称为弹性，意思是系统受到扰动后能恢复到“最初状态”。生态学领域最早开始有关韧性研究，随后又陆续被应用到工程学、经济学和社会学等多个学科领域[15]。而近年来，随着交通韧性研究的方兴未艾，韧性一词的内涵在交通系统得到了进一步外延拓展(表1)。

表1   交通系统韧性的定义

Tab.1  Definitions of resilience of transportation systems

交通韧性定义研究领域文献
系统在不发生灾难性事件下吸收冲击的能力，以及在发生破坏或灾难后维持其功能的能力交通系统[16⇓⇓-19]
系统能够吸收中断的能力，以减少中断的影响并保持货运流动性货物运输系统[20⇓-22]
系统面对冲击时的反应，以及继续提供预期服务水平的能力道路运输系统[23]
交通系统在经历潜在的破坏性事件后，在合理的时间内恢复到健康运行状态的速度和能力铁路运输系统[24⇓-26]
交通网络能够有效吸收破坏性事件，并在合理的时间范围内将系统恢复到等于或优于中断前的服务水平交通基础设施[27]
航空网络遭到破坏后维持现状功能及恢复到原先状态的速度航空运输系统[28-29]
海上运输受到事件影响中断后，在一段时间内恢复到正常状态的能力海上运输系统[30]
轨道系统受到事件干扰后恢复到正常状态的速度和服务水平的适应能力城市轨道系统[31-32]

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城市交通运输系统总是受到不同极端事件的干扰，自然灾害(例如台风、风暴潮、暴雨、洪涝)或人为事件(例如恐怖袭击、罢工和人为管理不善导致的系统故障)，其对城市交通运输系统功能的影响可能与对其他城市系统功能影响相同[6,19,33]。由于城市中发生极端事件的风险日益增加，因此，城市交通运输系统的抵御能力成为交通韧性概念关注的焦点[7,34]。交通运输系统的韧性不仅是指防止系统因干扰而发生故障行为的能力，而且还指系统适应和减少影响并避免灾难性局部后果或整个系统发生故障的能力[14]。由此可以看出，交通韧性的内涵理解主要在于对其系统应对事件“抵御”干扰和“恢复”的能力。同时，也有学者从交通韧性变化状态角度进行分析，可以分为静态韧性和动态韧性。其中，静态韧性强调的是交通系统自身对干扰事件的抵抗而能够有效维持其功能的能力；而动态韧性则更加强调交通系统受到事件干扰后系统自身如何通过功能恢复到原始状态或者平衡状态，其恢复的速率受到系统自身能力的大小以及外在事情干扰的程度[35]。

1.2 交通韧性定量评估指标方法研究

交通韧性评估指标方法研究大致可以分为3种：网络拓扑指标评估、韧性特征指标评估以及韧性性能指标评估[36]。

(1) 网络拓扑指标评估

早期学者们通常把交通网络建模成无加权无向的网络，并利用相关拓扑指标评估交通网络中断前后的变化情况，从而衡量交通网络韧性。主要网络拓扑指标有网络规模(节点数、边数、网络直径)、拓扑结构(平均度、平均路径长度、平均聚集系数、网络介数)、网络性能(网络效率、网络连通度、自然连通度)来衡量航空、地铁、道路韧性[32,37⇓⇓⇓-41](图2)。网络拓扑指标评估的是静态交通网络的韧性，能够反映出交通系统在受到干扰前后其网络连通状况。随着研究的深入，陆续有学者考虑评估动态交通网络在中断前后的表现特征，利用交通网络建模叠加时间或者交通流量的加权网络，将上述网络拓扑指标与诸如行程时间、客流等表示交通流量的指标有机结合。后期还有学者运用复杂网络理论分别计算子系统网络的网络规模、拓扑结构和网络性能，并综合计算不同子系统间的耦合度、综合协调指数和耦合协调度等，以定量评估交通系统的耦合协调度[42]。

图2

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图2   网络拓扑评估指标

Fig.2   Network topology evaluation indicators


(2) 韧性特征指标评估

交通韧性特征的指标研究是交通韧性定量评估的必要步骤，相关研究人员采用一个或者多个指标来进行表征，这些指标在测度体系构建过程中起着至关重要的作用，主要包括城市交通运输系统的冗余度(redundancy)、适应性(adaptation)、有效性(efficiency)、鲁棒性(robustness)、依存度(interdependence)、应对性(preparedness)、灵活性(flexibility)以及快捷性(rapidity)等特性。表2统计分析了文献中常用韧性特征指标定义及评估情况。

表2   基于特征的韧性评估指标

Tab.2  Resilience assessment indicators based on features

特征指标定义参考文献
冗余度系统拥有相同功能的可替代的子系统[26,40,43⇓⇓⇓-47]
适应性系统根据外部环境的变化而灵活调节自身的形态、结构或功能，以应对新压力的能力[31,48⇓-50]
有效性系统发生中断时仍能保持服务和连通水平的能力[45,51⇓-53]
鲁棒性系统抵御和应对外界冲击的能力[7,54⇓⇓⇓⇓⇓⇓-61]
依存度各子系统之间的连通性，包括子系统之间关系网络连通性[24]
应对性在系统被破坏之前准备某些措施，并通过减少破坏性事件的潜在负面影响来增强系统韧性的能力[50,62-63]
灵活性系统应对突发事件冲击并在系统中断后通过应急计划适应变化的能力[7,24,40,26,54,56]
快捷性系统按照优先事项及时实现控制损失并避免未来系统中断目标的能力[7,64-65]

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从表2可以看出，多数文献更加关注交通网络的冗余度和鲁棒性，而系统中断后重新恢复到新的平衡状态，有关适应性研究较少，即反映系统的动态韧性(如适应性、灵活性等)，这也是韧性区别于其他概念的重要特征。行程时间和交通流量(行驶速度等)是反映上述各个指标变化的主要性能变量，也是受到外在及内在威胁情况下城市交通韧性变化的重要衡量变量[14,66]。

(3) 韧性性能指标评估

韧性性能指标是通过反映交通系统在受到干扰事件前后系统性能的变化情况。考虑到“韧性三角形”能够反映减缓各种干扰事件的影响而维持自身功能的能力，同时能够反映交通运输系统功能损失的程度、事件的干扰程度，以及交通运输系统恢复正常功能所需的时间。通过构建极端事件影响下城市交通韧性强度测度体系，用韧性三角形定量化表示，即交通韧性损失程度，反映城市道路网络中系统性能的退化，其计算方法是一周中同一天的正常状态下的交通性能与极端天气事件下的交通性能面积差值(图3)[67]。其数学积分公式如下：

R=∫t0t2100-Q(t)dt

图3

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图3   基于韧性三角的韧性损失测度[69]

Fig.3   Measurement of resilience loss based on the resilience triangle


式中：Q(t)为系统的性能，t0为系统受事件干扰时间，t2为重新恢复平衡时间，R为韧性损失。该性能指标测度了交通网络从事件干扰开始到恢复平衡状态的累积性能损失。

Bruneau等[67]的研究成果为道路交通运输网络等城市基础设施韧性量化评价奠定了坚实的基础。随着研究的深入，后续学者们从自身视角出发不断地延伸与拓展这一基础模型，从而得出新的计算思路和方法表示交通韧性变化，如通过时间依赖的恢复损失比来定量分析灾害事件对交通系统全过程性能变化状态，包括系统初始平衡状态、系统不平衡状态、中断状态、恢复状态、重新平衡状态，反映系统随事件的发生发展变化的动态过程，从而可以定量评估灾害对系统性能的损失以及不同时间下的性能状况[68⇓-70]。

相比于上述拓扑指标与韧性特征指标定量评估韧性，性能指标更能刻画出系统在应对干扰事件前后系统性能的整个“抵御”过程，从而能够更好地反映交通网络应对干扰事件系统动态变化的能力，因此被广泛应用于交通韧性定量评估。

1.3 交通韧性定量评估优化方法研究

交通韧性定量评估优化方法，一般来说，大致可以分为以下3种：情景模拟方法、数据驱动方法以及统筹优化方法。

(1) 情景模拟方法

情景模拟方法主要假设不同事件干扰下，模拟交通系统可能的性能变化。如利用网络拓扑指标，通过发生极端天气事件时各交通网络节点可能被破坏的节点数和破坏程度，模拟其韧性的变化情况[71-72]。再如韧性最强为优化指标，模拟海运系统中断情景下如何保障吨位、时间和成本控制前提下，提升海运系统韧性[73]。随着研究的深入，后期学者们通过模拟不同干扰事件情景，并考虑引入动态交通韧性框架、水文模型结合GIS模拟洪涝灾害、交通信号影响等方式[74⇓-76]，以分析不同交通系统(道路、水运、地铁、航空等)在不同情景下的韧性水平，来识别交通网络中的潜在的脆弱节点，从而探究交通系统受到事件攻击后其韧性状况，并用来指导交通网络优化提升工程。

(2) 数据驱动方法

数据驱动分析是研究城市交通韧性的又一重要有效手段，利用交通出行大数据(例如出租车行驶轨迹数据和公共交通出行数据)，通过传统的统计模型、框架测算以及新兴的大数据挖掘技术，构建并评价基于城市交通内部威胁(例如交通事故和人为技术故障)的交通韧性过程，并分析在上述特定事件下交通韧性的形成和发展的历史过程，是目前的普遍做法。如利用浮动车(出租车和公交车等)GPS轨迹数据，结合数据驱动、复杂网络方法以及目前新兴的深度学习技术开展交通系统(航空、道路等)韧性方法研究，分析干扰事件对交通网络性能运营的影响程度及恢复情况[31,43,77⇓-79]。数据驱动方法能够对交通系统受到干扰事件发生前后全过程的韧性进行评估，通过利用多源异构大数据得出更加反映实际情况的交通网络韧性水平，从而有效指导交通网络开展应急救援，以有效提升系统韧性。

(3) 统筹优化方法

统筹优化交通网络韧性缓解灾害事件给其系统带来的潜在风险，是研究交通韧性的最终目标。近年来，学者们着手开展有关提升交通网络韧性的研究，提出有针对性地提升交通韧性最大化措施或方案。目前交通韧性统筹优化研究主要侧重在预防和恢复2个方面。

预防方面主要针对灾害发生前，通过寻找最佳的交通网络灾前损失最小、资源最大化利用或网络拓扑结构调整最合理等措施，以最小的灾害干扰来有效保障交通网络韧性性能。如考虑到气候变化给交通系统带来的影响，未来几十年交通基础设施可能会面临一系列重大挑战：它们很大程度上无法适应外部条件的变化，使用经常远远超过其预期的寿命，并且影响越来越大[80]。目前相关学者已经进行了应对气候变化的交通运输系统鲁棒性的研究，通过加强其对气候变化的适应能力，以提升控制、预防和巩固能力的方式来防止因交通基础设施的破坏损毁而导致交通系统的中断[81⇓-83]。另外，还有少部分学者开展了气候变化影响下的极端天气事件对城市交通韧性影响评价(韧性恢复速率)及预测研究，提出了气候变化影响评估标准指标，阐述极端天气如何影响交通基础设施和运营、气候变化如何改变这些影响极端事件的频率和幅度，以及新型技术进步和社会经济发展如何塑造未来的交通网络，改善或加剧气候变化的影响[84]。

恢复方面主要针对灾害发生后，交通网络遭受严重破坏将发生运营中断甚至瘫痪时，如何采取合适的恢复措施，以尽快恢复系统正常运行。如极端高温事件导致沥青路面老化/氧化、开裂，极端暴雨事件导致道路淹没、冲刷以及引发滑坡、泥石流，海平面上升影响下的台风—洪涝极端事件导致沿海低洼地区洪水频繁、路基/桥梁侵蚀以及地面沉降等[85⇓⇓-88]，运营管理者如何根据上述事件对交通系统的实时状态做出最优优化决策，以最大限度地降低因灾害事件而带来的破坏，确保系统能够顺畅安全地运行，以成本最小、转移路线地点最安全以及应急救援时间最短等选择最佳疏散路线、疏散地点。后续还有相关学者提出了双目标双层优化方法，以保证资源分配有效性最大化，提升系统韧性[89]。

2 当前研究中存在的问题

虽然城市交通韧性相关研究在概念框架、指标体系评估方法以及定量评估优化等方面取得了丰硕的研究成果，但如何通过发展和构建高精度的城市交通韧性测度体系，准确揭示极端事件影响下城市交通韧性时空特征，科学阐明灾害事件对城市交通韧性强度的影响机制等相关问题，尤其是气候变暖给城市交通韧性强度带来的变化分析，仅依靠上述研究进展仍不能够完全实现，各个研究还存在以下不足：

(1) 交通韧性概念框架以及测度体系构建方面。虽然目前国内外学术界尚未有衡量交通韧性强度等级的统一定量指标，普遍认为，冗余度、适应性、鲁棒性、应对性以及快捷性等特性是最能反映交通系统韧性强弱的主要指标[14,90]。但这些特性又用哪些具体指标表示，目前的相关研究还不够明确。再加之近年来，极端事件发生频率及强度增加，并且系统复杂性、连通性及依赖性不断增加、易受灾地区快速城市化等使得极端事件对交通运输系统的影响不断加剧，这就导致目前的交通韧性测度体系很难精准反映其系统韧性强度。因此，如何构建能够反映城市交通运输系统韧性的测度体系，是精确刻画区域交通运输系统在极端事件影响下其韧性强度的关键所在，而目前的研究严重制约了对交通韧性强度特征的整体和全局性认知。

(2) 极端事件影响下交通韧性时空演变特征分析方面。虽然情景模拟方法可以作为一种研究极端事件下的交通运输韧性研究方法，并能够明确分析出极端事件下的交通运输系统可能存在的薄弱环节。但是在面对现实极端天气事件的影响时，这种方法仍然存在困难，即不能够有效反映极端事件发生前后交通韧性的真实变化情况，从而不能根据极端事件预判其对交通系统正常运行的冲击和中断程度，同时其影响因素多且关系复杂[18,91]。数据驱动方面，该方法数据需求量巨大，需要同时考虑多源异构数据在时空尺度上如何融合，并且结果可能会受到数据源、数据质量等因素影响[92]。因此，尝试应用数据驱动的方法来评价在外部威胁下的交通运输网络韧性，需要掌握一定质量的数据和扎实的处理方法[18,93]。统筹优化方法虽然考虑到灾害事件后资源分配与时间成本，能够为灾后的系统恢复提供最优的方案；但是，该方法需要占用大量的计算资源以及依赖高精度的数学模型，并且考虑的约束因素越多，求解就越复杂。因此，目前情景预测方法具有现实模拟的局限性，而数据驱动以及统筹优化方法也存在数据和计算资源保障等问题，都无法完全满足在时空尺度上城市交通韧性演变特征分析及预测的要求。

(3) 全球气候变化对交通韧性强度的影响机制方面。虽然目前道路、堤坝等交通基础设施建造都是满足特定极端事件等级标准设计的(如百年一遇的洪水事件，即交通基础设施能够承受在任何情况下1%几率发生该事件的强度)，在发生这种情况下的极端事件进行鲁棒性研究是切实可行的。然而，交通系统还面临着气候变化产生其他间接的、非物理的等难以量化的影响[7]。例如，当极端事件变得更加频繁激烈时，如何设计和提高它们的韧性?又如，如果一条道路设计为能够承受百年一遇的洪水事件，但驾驶员只有抵御50年的洪水事件的经验，那么如何最有效地避免因交通中断而造成交通事故以及如何提升交通恢复速率?而目前上述情景模拟、数据驱动以及统筹优化方法都不能解决此类问题。随着全球气候变暖，极端事件发生频次以及强度的增加，无疑会进一步加大对城市交通运输系统的压力，从而对城市交通韧性(时间、空间、趋势等的变化)产生了巨大的挑战。因此，“气候变化—极端事件”对其时空演变规律的影响有待进一步厘清，尤其是揭示极端事件对城市交通韧性影响的机理，而目前的有关极端事件对交通韧性的影响研究还仅限在个例条件，并不能充分认知极端事件对交通韧性影响的规律特征。

3 未来城市交通韧性的研究方向

联合国政府间气候变化专门委员会(Intergovernmental Panel on Climate Change，IPCC)第五次评估报告再次指出，过去百年来，全球气候系统正经历着以全球变暖为主要特征的显著变化，并且进入21世纪，全球极端气候事件发生的频率将快速增加且其影响范围呈现扩大趋势[7,94]。而未来全球气候变化背景下，极端事件对交通韧性时空演变规律的影响有待进一步厘清，包括未来交通韧性强度的空间特征如何变化，哪些地区需要加强交通应急管制，以及交通韧性强度随时间变化趋势如何，等等。这些问题目前还没有明确且肯定的答案[84]。因此，气候增温背景下采用何种方法研究极端事件对交通韧性影响的时空特征还没有清晰完整的框架。在此背景下，本文认为可以从以下几个方面考虑。

3.1 交通韧性的测度体系研究

城市交通韧性是在适应演化中与周围环境达到了一定的动态平衡。而无论是暴雨、洪涝、台风、风暴潮，都偏离了交通系统所长期适应的通勤环境，从而抑制交通有序组织甚至导致交通瘫痪，并进一步影响城市社会经济的发展[29,95]。另一方面，交通系统对极端事件也有一定程度的适应和抵抗能力[14,18]。当极端事件的强度和频次没有超出交通系统的忍耐限度时，极端事件通常只是降低整个交通系统运行的效率和能力。当极端事件的强度和频次超出交通系统的忍耐限度时，极端事件会导致交通系统大面积瘫痪。总之，极端事件对交通系统的影响通常是负面的[96]。精确定量描述这一影响，有赖于学者们对极端事件影响交通韧性的理解程度，包括如何提高对交通系统的恢复时间、速率，以及抵抗极端事件机理的准确描述[93]。然而，过去的工作大多侧重于现象描述，对其如何进行定量化测度体系构建，以及深入分析其背后的驱动机理尚较为缺乏。

因此，我们需要探索和提高关于交通系统韧性特征的认知，进一步厘清极端事件对交通韧性的影响，包括如何选择合适的交通韧性指标精确反映极端事件对交通系统的影响；结合极端事件对交通系统中断前准备和中断后恢复行动的模型，创建一个综合交通系统韧性的定量化测度方法，并在交通韧性评估的虚实结合中不断调整评估方案，以能够科学揭示交通系统在极端事件影响下的整个演变过程，即初始平衡—相对不平衡—不平衡—动态平衡—恢复平衡。通过计算历次极端事件影响下如图3所示的韧性三角形，从而重建历次极端事件影响下城市交通韧性强度时空数据集，进而监测历史城市交通韧性时空变化特征，并对比极端事件跟周期性事件(如早晚高峰拥堵)所形成的交通韧性定量测度区别。

3.2 城市交通韧性时空演变特征分析研究

基于构建的交通韧性强度测度体系，重建极端事件影响下的城市高时空分辨率交通韧性强度测度体系时空数据集。在此基础上，采用时空统计分析等方法，分析极端事件影响下的城市交通韧性强度时空尺度演变特征，即统计分析在不同极端事件强度下交通韧性强度的表现特征，如不同暴雨强度(暴雨、大暴雨以及特大暴雨)、不同台风预警等级(蓝色、黄色、橙色以及红色)以及在复合极端事件(如台风—暴雨—洪涝)下交通韧性强度的表现特征，以及分析交通韧性强度在不同暴雨强度、不同台风危险等级和复合极端事件下的时间序列和空间演变特征。

因此，我们需要通过构建极端事件影响下城市交通韧性强度测度，即交通韧性损失强度；并根据韧性三角形面积对交通韧性损失强度等级进行划分(图4)。通过统计历次极端事件影响下交通韧性强度等级(低、中等以及高韧性)，分析其在时间和空间2个维度的演变特征。在上述研究的基础上，可以对一年内所有发生极端事件影响下的城市交通韧性进行统计，从而进行年际交通损失强度面积的累加，将其结果定义为该空间网格单元上极端事件影响下的年度交通韧性“综合强度”。对各个空间网格，进行逐年度的交通韧性“综合强度”统计，即可获得逐网格、逐年度的交通韧性“综合强度”数据时空序列，并对其年际特征进行分析。

图4

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图4   极端天气时间下城市交通韧性强度的可能情景

注：本图参考文献[68]绘制。

Fig.4   Scenarios of urban transportation resilience under extreme weather


3.3 新技术模拟气候变化影响的交通韧性强度趋势变化研究

当前许多气候变化预测工具包的开发都没有考虑到交通运输等相关部门。空间连贯性和真实天气情况的再现也是未来一个非常重要的需求，因为交通运输容易受到极端天气事件的影响，这些天气事件会在空间上同时影响多个交通节点[95]。同时，鉴于大数据挖掘和人工智能技术已被证明是评估交通系统性能影响的一种有价值的方法，交通系统需要其发挥更积极的作用，应用这种技术来指导紧急情况下的“准确规划”[84]。

因此，我们可以通过联合利用多源异构大数据(浮动车GPS、视频监控、手机信令、城市路网、卫星遥感、气象观测及实测潮位观测等数据)，根据IPCC的第六次评估报告(sixth assessment report，AR6)的5种代表性浓度路径(representative concentration pathways，RCPs)情景下，以第六次国际耦合模式比较计划(coupled model intercomparison project phase 6，CMIP6)多模式比较模拟数据预测未来极端事件(台风、风暴潮及暴雨)强度的总体可能变化趋势，构建高精度的城市交通韧性强度评价指标体系，并通过深度学习及大数据挖掘的相关算法，构建极端事件对城市交通韧性强度影响的评价及预测方法。此外，联合深度学习预测模型和GIS技术结合和集成，能够规范化模型计算的数据结构，并综合应用组件技术、数据对象访问技术及动态链接库技术等建立可以与GIS嵌入式紧密集成的交通韧性时空动态模拟模块，将预测模型与GIS、RS和数据库管理系统完全集成，为城市交通韧性空间数据管理、模型运算和空间数据可视化与分析集成为一体，进而科学认知极端事件下城市交通韧性强度时空变化规律，提高城市道路网的管理能力和建立完善的极端事件交通应急响应措施，保障中国城市的可持续发展以及应对气候变化的风险管理创新发展。

4 结论与展望

近年来不同极端事件的频发给交通系统带来了巨大的影响。本文根据已有研究成果，系统总结分析了交通韧性的概念框架、指标体系评估以及定量评估优化等方面的研究进展。目前研究普遍表明，极端事件对交通系统影响明显，不同事件对其影响程度存在时空异质性，但是在气候变化背景下的时空演变特征以及未来的时空变化趋势等还存在较大争议和不确定性。这种不确定性给模型模拟和预测极端事件影响下的交通韧性时空演变特征分析带来了巨大困难。因此，建议未来可以充分利用现有的卫星遥感技术、GIS空间技术并结合模式分析、情景预测等方法加强对极端事件的模拟及预测，以提高极端事件的发生频次、强度、影响范围以及地理分布格局监测能力；同时，通过利用大数据挖掘技术和深度学习的人工智能预测模型对交通韧性强度的时空演变特征进行研究，掌握极端事件影响下交通韧性强度变化的格局与趋势。

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The long-term reliability and functioning of transportation systems will increasingly need to consider and plan for climate change and extreme weather events. Transportation systems have largely been designed and operated for historical climate conditions that are now often exceeded. Emerging knowledge of how to plan for climate change largely embraces risk-based thinking favoring more robust infrastructure designs. However, there remain questions about whether this approach is sufficient given the uncertainty and non-stationarity of the climate, and many other driving factors affecting transportation systems (e.g., funding, rapid technological change, population and utilization shifts, etc.). This paper examines existing research and knowledge related to the vulnerability of the transportation system to climate change and extreme weather events and finds that there are both direct and indirect "pathways of disruption." Direct pathways of disruption consist of both abrupt impacts to physical infrastructure and impacts via non-physical factors such as human health, behavior, and decision making. Similarly, indirect pathways of disruption result from interconnections with other critical infrastructure and social systems. Currently, the direct pathways appear to receive much of the focus in vulnerability and risk assessments, and the predominant approach for addressing these pathways of disruption emphasizes strengthening and armoring infrastructure (robustness) guided by risk analysis. However, our analysis reveals that indirect pathways of disruption can have meaningful impacts, while also being less amenable to robustness-based approaches. As a result, we posit that concepts like flexibility and agility appear to be well suited to complement the status quo of robustness by addressing the indirect and non-physical pathways of disruption that often prove challenging - thereby improving the resilience of transportation systems.

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公共交通系统韧性是交通安全研究的核心内容之一。复杂网络理论作为分析大型复杂系统的有力工具，为研究公共交通系统韧性提供了新的角度和方向。综述了复杂网络在公共交通韧性领域的研究现状，首先结合文献计量分析法对公共交通网络韧性相关文献的发展趋势、出版刊物分布、热点关键词等特征进行分析，以系统梳理公共交通网络韧性发展历程，并归纳总结公共交通网络韧性领域的研究热点。其次，从公共交通网络韧性定义出发，对复杂网络在公共交通韧性评估、韧性优化方面的应用与研究现状进行综述，一方面对公共交通韧性评估的核心内容，包括韧性评估指标、评估方法、中断建模及中断流重分配进行了系统分析，另一方面从灾前预防策略与灾后恢复策略2个方面梳理了公共交通韧性提升相关研究。最后，系统总结现有研究所面临的主要问题与挑战，并从韧性评估方法创新、中断建模改进、恢复模型探索等方面对未来公共交通韧性的发展方向与研究趋势进行分析。

[Yang Qi, Zhang Yani, Zhou Yuqing, et al.

A review of complex network theory and its application in the resilience of public transportation systems

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The resilience of public transportation systems is one of the core contents of traffic safety research. As a powerful tool for analyzing large-scale complex systems, complex network theory provides a new perspective and direction for studying the resilience of public transportation systems. This paper provided a compressive literature review on the application of complex network theory in the field of public transportation resilience. Firstly, this paper analyzed the characteristics of the literature related to public transportation network resilience, such as the trend of the number of documents, the distribution of publications and hot keywords combined with the bibliometric analysis method. This paper sorted out the development process of public transportation network resilience, and summarized the research hotspots in the field of public transportation network resilience. Secondly, based on the definition of public transportation network resilience, this paper reviewed the application and research status of complex network theory in public transportation resilience assessment and resilience optimization. On the one hand, the core content of public transportation resilience assessment including resilience assessment indicators, assessment methods, interruptions modeling and redistribution of interrupted traffic were systematically analyzed. On the other hand, this paper sorted out the related research on the resilience improvement strategy of public transportation from the two aspects of pre-disaster prevention and post-disaster recovery. Finally, the main problems and challenges faced by existing researches were summarized, and the development direction and research trend of future public transportation resilience were analyzed from the aspects of resilience assessment method innovation, interruptions modeling improvement, and recovery model exploration.

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在全球环境变化和快速城市化的背景下,各种不确定风险成为制约城市安全和可持续发展的重要障碍。城市韧性作为一种城市风险治理的新思路,如何提高城市抵御、消解、适应不确定风险的能力,建设有韧性能力的城市,正成为当前地理学及其相关学科领域亟待探索的新课题。论文在概述城市韧性的研究缘起与概念内涵的基础上,从多种要素(人文要素、环境要素、灾害扰动)对城市韧性的影响、城市韧性框架、城市韧性评价及模拟研究等方面出发,对可持续发展视角的城市韧性研究现状进行探讨,并指出当前城市韧性研究在理论框架、作用机理、实证研究、差异性分析等方面仍存在诸多薄弱环节。最后,对城市韧性重点研究方向进行展望,即应以理论框架为引领,推动多目标、多层次、多视角的系统评价研究;以机理解析为支撑,实现城市韧性的动态模拟与决策预警的新突破;以实证研究为导向,继续加强多学科融合和探索城市韧性的应用模式;遵循差异性规律,实现城市规划治理从统一的“多城一策”向灵活的“一城一策”转变。

[Zhao Ruidong, Fang Chuanglin, Liu Haimeng.

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Under the background of global environmental change and urbanization, various risks and uncertainties have posed an important obstacle on urban security and urban sustainable development. As a potential new approach of urban risk management, urban resilience can improve the ability to resist, dissolve, and adapt when facing risks and uncertainties, and expound the adaptive scheme of risks in the process of rapid urbanization. Urban resilience has been a new topic in geography and related disciplines. Based on the origin and concept of urban resilience research, from the perspective of the impact of various factors (human, environmental, disaster) on urban resilience, this study constructed a theoretical framework of urban resilience, including evaluation and scenario simulation, and discussed the status of urban resilience research, pointing out that there are still many weak links in the theoretical framework, mechanism, practical application, and difference analysis of urban resilience research. Finally, the key directions of urban resilience research were also discussed. The theoretical framework should be used as a guide to promote multi-objective, multi-level, and multi-perspective systematic evaluation research. With the analysis of mechanism as support, studies should aim to achieve a new breakthrough in process simulation of urban resilience and decision making and early warning. Oriented by empirical research, studies should continue to strengthen the application model of multidisciplinary integration and exploration of urban resilience. Considering regional differences, further work should try to achieve a change in urban planning from the unified "one policy for multi-city" approach to the flexible "one policy for one city" approach.

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This paper summarizes a broad literature review on system resilience. After these interpretations of resilience are considered, a definition of resilience in the context of freight transportation systems is provided. The definition of resilience offered here captures the interactions between managing organizations—namely, state departments of transportation, the infrastructure, and users—which is critical considering that the freight transportation system exists to support economic activity and production. A list of properties of freight transportation system resilience is outlined. These properties of resilience can contribute to the overall ability of the freight transportation system to recover from disruptions, whether exhibited at the infrastructure, managing organization, or user dimension. This contribution provides a framework that can serve as a starting point for future research, offering a shared language that promotes a more structured conversation about freight transportation resilience.

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The resiliency of infrastructure, particularly as related to transportation networks, is essential to any society. This resiliency is especially vital in the aftermath of disasters. Recent events around the globe, including Hurricane Katrina and significant seismic events in Haiti, Chile, and Japan, have increased the awareness and the importance of resiliency. Transportation systems are key to response and recovery. These systems must withstand stress, maintain baseline service levels, and be stout enough in physical design and operational concept to provide restoration to the system. Analysis of a transportation network's resiliency before a disruptive event will help decision makers identify specific weaknesses within the network so that investments and improvement projects are prioritized appropriately. Previous research in quantification of network resiliency was expanded into a proposed methodology, through which understanding and applying concepts of network resiliency could preclude many devastating effects of destabilizing events and preserve the quality of life and economic stability.

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Ports are an integral part of the transportation system and are often susceptible to a diverse range of risks, including natural disasters, malicious cyber-attacks, technological factors, organizational factors, economic factors, and human error. To address the challenges triggered by these diverse risks, this research identifies the basic factors that could enhance the resilience of the port system. After these factors are identified and expressed as different resilience capacities, they are used to quantify the resilience of the port infrastructure by applying a Bayesian network. Quantification of resilience is further analyzed based on different advanced techniques such as forward propagation, backward propagation, sensitivity analysis, and information theory. The formal interpretation of these analyses indicates that maintenance, alternate routing, and manpower restoration are the leading factors contributing to enhancing the resilience of a port infrastructure system under disruptive conditions.

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Infrastructure systems, especially those including civil infrastructure (e.g., transportation networks, water distribution systems, and power transmission lines) are inarguably critical to the everyday functions of society. The ability of an infrastructure system to withstand, to adapt to, and to recover rapidly from extreme events is paramount in the ability to serve users. The effects of historical climatic events have resulted in a growing concern for preparedness against such hazards, specifically in coastal communities. The continuing function of society and its economy relies heavily on an accessible transportation network. Roads, highways, railroads, ports, and airports make up a complex infrastructure vital for the travel of goods and people. Extreme events of climatic source that disturb the transportation network affect all aspects of daily life, both directly and indirectly. To estimate the resilience of coastal transportation networks to extreme events, topological graph properties are measured as the nodes and links of a network are removed to simulate failures and closures due to extreme climatic events. The transportation network of the New York City metropolitan area, the most populous and crucial urban area in the United States, was chosen as a case study. The presented approach provides a tool for transportation agencies to identify the most critical sections of the network and to establish pre-event hazard mitigation strategies or to plan for postevent recovery actions with the goal of increasing the resiliency and decreasing the down time of transportation networks.

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Modern society is increasingly dependent on the stability of a complex system of interdependent infrastructure sectors. It is imperative to build resilience of large-scale infrastructures like metro systems for addressing the threat of natural disasters and man-made attacks in urban areas. Analysis is needed to ensure that these systems are capable of withstanding and containing unexpected perturbations, and develop heuristic strategies for guiding the design of more resilient networks in the future. We present a comprehensive, multi-pronged framework that analyses information on network topology, spatial organization and passenger flow to understand the resilience of the London metro system. Topology of the London metro system is not fault tolerant in terms of maintaining connectivity at the periphery of the network since it does not exhibit small-world properties. The passenger strength distribution follows a power law, suggesting that while the London metro system is robust to random failures, it is vulnerable to disruptions on a few critical stations. The analysis further identifies particular sources of structural and functional vulnerabilities that need to be mitigated for improving the resilience of the London metro network. The insights from our framework provide useful strategies to build resilience for both existing and upcoming metro systems.

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In this paper we present an integrated approach to the evacuation problem under an emergency situation for transportation systems. The approach is based on a view that a service system has two subsystems: infrastructure and substance. The approach attempts to integrate infrastructure design and substance flow planning to improve the evacuation performance. Without loss of generality, we restrict infrastructure design to reconstruction of a damaged road with two attributes of the road: capacity and travel time, we restrict substance flow planning to the contraflow method, and we consider the evacuation problem with single source and single destination. Further, we apply the discrete variable Particle Swarm Optimization and RelaxIV to solve the problem model. The overall objective function in the problem model is a minimum transportation time. Since recovery of a damaged transportation (damaged road in this case) is implied in our problem, the proposed approach has some significant implication to resilience engineering of a service system as well. An example is studied to show the effectiveness of our approach; in particular it is shown that an integrated solution is significantly better than the solution with only the contraflow method.

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Mapping resilience of Houston freeway network during Hurricane Harvey using extreme travel time metrics

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This paper presents a conceptual framework to define seismic resilience of communities and quantitative measures of resilience that can be useful for a coordinated research effort focusing on enhancing this resilience. This framework relies on the complementary measures of resilience: “Reduced failure probabilities,” “Reduced consequences from failures,” and “Reduced time to recovery.” The framework also includes quantitative measures of the “ends” of robustness and rapidity, and the “means” of resourcefulness and redundancy, and integrates those measures into the four dimensions of community resilience—technical, organizational, social, and economic—all of which can be used to quantify measures of resilience for various types of physical and organizational systems. Systems diagrams then establish the tasks required to achieve these objectives. This framework can be useful in future research to determine the resiliency of different units of analysis and systems, and to develop resiliency targets and detailed analytical procedures to generate these values.

Qiang Y, Xu J.

Empirical assessment of road network resilience in natural hazards using crowdsourced traffic data

[J]. International Journal of Geographical Information Science, 2019, 34(12): 2434-2450.

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Henry D, Ramirez-marquez J E.

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Chen Y, Wang J E, Jin F J.

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高鹏, 胡剑波, 魏高乐.

变权重的城市轨道交通复杂网络鲁棒性分析

[J]. 计算机仿真, 2013, 30(9): 153-156.

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[Gao Peng, Hu Jianbo, Wei Gaole.

Robustness analysis of urban transit network based on complex network with varied weight

Computer Simulation, 2013, 30(9): 153-156.]

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[J]. Transportation Research Part D: Transport and Environment, 2020, 85(10): 102395. doi: 10.1016/j.trd.2020.102395.

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[J]. Travel Behaviour and Society, 2019, 15: 28-43.

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Ganin A A, Mersky A C, Jin A S, et al.

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[J]. Transportation Research Part C: Emerging Technologies, 2019, 100: 318-329.

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Yin J, Ren X, Liu R, et al.

Quantitative analysis for resilience-based urban rail systems: A hybrid knowledge-based and data-driven approach

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Using big data to study resilience of taxi and subway trips for hurricanes Sandy and Irene

[J]. Transportation Research Record: Journal of the Transportation Research Board, 2016, 2599: 70-80.

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Hurricanes Irene and Sandy had a significant impact on New York City; the result was devastating damage to the New York City transportation systems, which took days, even months to recover. This study explored posthurricane recovery patterns of the roadway and subway systems of New York City on the basis of data for taxi trips and for subway turnstile ridership. Both data sets were examples of big data with millions of individual ridership records per month. The spatiotemporal variations of transportation system recovery behavior were investigated by using neighborhood tabulation areas as units of analysis. Recovery curves were estimated for each evacuation zone category to model time-dependent recovery patterns of the roadway and subway systems. The recovery rate for Hurricane Sandy was found to be lower than that for Hurricane Irene. In addition, the results indicate a higher resilience of the road network compared with the subway network. The methodology proposed in this study can be used to evaluate the resilience of transportation systems with respect to natural disasters and the findings can provide government agencies with useful insights into emergency management.

Wong A, Tan S, Chandramouleeswaran K R, et al.

Data-driven analysis of resilience in airline networks

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[J]. Sustainable and Resilient Infrastructure, 2019, 4(4): 173-191.

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Meyer M, Weigel B.

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[J]. Transport Policy, 2015, 41: 159-166.

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Woods D D.

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[J]. Reliability Engineering and System Safety, 2015, 141: 5-9.

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[J]. Progress in Physical Geography: Earth and Environment, 2014, 38(4): 448-463.

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The assessment of the potential impact of climate change on transport is an area of research very much in its infancy, and one that requires input from a multitude of disciplines including geography, engineering and technology, meteorology, climatology and futures studies. This paper investigates the current state of the art for assessments on urban surface transport, where rising populations and increasing dependence on efficient and reliable mobility have increased the importance placed on resilience to weather. The standard structure of climate change impact assessment (CIA) requires understanding in three important areas: how weather currently affects infrastructure and operations; how climate change may alter the frequency and magnitude of these impacts; and how concurrent technological and socio-economic development may shape the transport network of the future, either ameliorating or exacerbating the effects of climate change. The extent to which the requisite knowledge exists for a successful CIA is observed to decrease from the former to the latter. This paper traces a number of developments in the extrapolation of physical and behavioural relationships on to future climates, including a broad move away from previous deterministic methods and towards probabilistic projections which make use of a much broader range of climate change model output, giving a better representation of the uncertainty involved. Studies increasingly demand spatially and temporally downscaled climate projections that can represent realistic sub-daily fluctuations in weather that transport systems are sensitive to. It is recommended that future climate change impact assessments should focus on several key areas, including better representation of sub-daily extremes in climate tools, and recreation of realistic spatially coherent weather. Greater use of the increasing amounts of data created and captured by ‘intelligent infrastructure’ and ‘smart cities’ is also needed to develop behavioural and physical models of the response of transport to weather and to develop a better understanding of how stakeholders respond to probabilistic climate change impact projections.

易嘉伟, 王楠, 千家乐, 等.

基于大数据的极端暴雨事件下城市道路交通及人群活动时空响应

[J]. 地理学报, 2020, 75(3): 497-508.

DOI:10.11821/dlxb202003005      [本文引用: 1]

随着全球气候变化加剧,极端降雨增多,暴雨内涝灾害频发,严重威胁城市的可持续发展。快速掌握暴雨给城市交通及人群的影响,有助于提高灾害应急管理水平和事件响应能力。利用实时动态的交通路况信息和手机定位请求数据,通过一种融合STL时序分解技术与极端学生化偏差统计检验的时间序列异常探测方法,监测和分析暴雨内涝灾害事件中,城市道路交通和人群活动的时空响应特征,并以2018年7月16日发生在北京的极端暴雨事件为例开展实证研究。研究结果显示,在降雨集中的早、晚高峰两个时段（8—9时、18—19时）,市区的拥堵道路数量超出往常水平最高可达150%,异常检测分析显示拥堵道路数量和交通拥堵指数均达到异常甚至极端水平。人群活动的异常响应分析结果显示,暴雨事件引起定位请求量异常升高、异常点增多,且异常点的空间分布与1 h前的降雨量分布存在较高相关性。以上结果不仅证明了大数据及异常检测方法对于快速洞察暴雨事件对城市交通及人群影响的有效性,也为城市暴雨内涝灾害的应急响应与管理提供了新的技术手段。

[Yi Jiawei, Wang Nan, Qian Jiale, et al.

Spatio-temporal responses of urban road traffic and human activities in an extreme rainfall event using big data

Acta Geographica Sinica, 2020, 75(3): 497-508.]

DOI:10.11821/dlxb202003005      [本文引用: 1]

As global climate change intensifies, extreme rainfalls and floods become more frequent and pose a serious threat to urban sustainable development. Fast assessment of the rainfall disaster impact upon urban traffic and population plays an important role in improving disaster emergency management and incident response capabilities. This study adopts a time series anomaly detection method to discover and quantify the impact of rainfall-triggered flood on road traffic and human activities using real-time traffic condition information and mobile phone location request data. The anomaly detection method combines the STL time series decomposition technique and the extreme student deviation statistics to identify the response characteristics of traffic data and location requests during the event. The extreme rainfall event that occurred in Beijing on July 16, 2018 is used as a case study to examine the method effectiveness. The results show that the precipitation peaked in the morning and evening rush hours, during which the number of congested roads exceeded the average level by up to 150%. The anomaly detection analysis indicates that the number of congested roads and the traffic congestion index reached the outlier level. The anomaly analysis of human activity responses shows that the heavy rainfall event also caused an abnormal increase in the number of location requests, and the spatial distribution of the anomalous grids was highly correlated with the rainfall distribution one hour before. The above results not only prove the effectiveness of the big data and the anomaly detection method in understanding the impact of heavy rainfall events on urban traffic and population, but also provide new means for urban emergency response and management against rainfall disasters.

胡文燕, 李梦雅, 王军, 等.

暴雨内涝影响下的城市道路交通拥挤特征识别

[J]. 地理科学进展, 2018, 37(6): 772-780.

DOI:10.18306/dlkxjz.2018.06.004      [本文引用: 1]

近年来,暴雨内涝频繁发生,常引发严重的城市交通拥堵问题。本文利用自主开发的宏观交通模拟工具,模拟了上海市中心城区50年一遇和100年一遇暴雨强度情景下每条路段的小时交通量,通过计算道路饱和度,研究了不同强度暴雨内涝对中心城区高架出入口和重要道路拥挤程度的影响。结果表明：①100年一遇暴雨内涝对上海市中心城区道路交通服务能力影响显著,可导致7个高架道路出入口关闭,部分出入口严重拥堵;②暴雨内涝对道路拥堵状况影响的差异性明显,变拥挤路段占道路总里程的13.35%,其中一级道路的拥挤程度变化最为明显,如：大连路、武宁路,周家嘴路和长寿路等主要路段服务水平下降。

[Hu Wenyan, Li Mengya, Wang Jun, et al.

Feature identification of urban road traffic congestion under the influence of rainstorm and waterlogging

Progress in Geography, 2018, 37(6): 772-780.]

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In recent years, pluvial flash floods (PFF) have caused serious traffic congestion and disruption in many big cities. This study investigated the traffic congestion pattern due to PFFs in an intra-urban area through a macro traffic simulation method. A macro traffic flow model was built based on trip distribution and the characteristics of roads and routing. We incorporated rainfall data and simulated the hourly traffic volume on each road segment under two PFF scenarios of 50-year and 100-year return periods by the macro traffic flow model. Next, the variation of volume/capacity (V/C) on each road was calculated to derive the spatial pattern of traffic condition under different PFF scenarios. The results were contrasted to demonstrate the change of congestion pattern on the main roads and entrances/exits of the expressways in the city center of Shanghai to analyze the influence of different PFF scenarios on traffic congestion. The results indicate that ①PFFs of 50-year return period may have a marginal effect on the traffic system, but PFFs of 100-year return period can pose great threat to the traffic system in the central urban area of Shanghai. Seven exits and entrances of the expressways are closed due to serious inundation and a large portion of the road network becomes more congested; ②the effect of PFFs on V/C also exhibits spatial disparity over the entire network. Overall, 13.35% of the roads become more congested under 100-year return period. Additionally, the most obvious change of congestion pattern is found on the first-class highways (23.31%), such as Dalian Road, Wuning Road, Zhoujiazui Road, and Changshou Road.

尹占娥, 许世远, 殷杰, 等.

基于小尺度的城市暴雨内涝灾害情景模拟与风险评估

[J]. 地理学报, 2010, 65(5): 553-562.

[本文引用: 1]

[Yin Zhan'e, Xu Shiyuan, Yin Jie, et al.

Scenario simulation and risk assessment of urban rainstorm and waterlogging disaster based on small-scale

Acta Geographica Sinica, 2010, 65(5): 553-562.]

[本文引用: 1]

殷杰.

基于高精度地形表面模型的城市雨洪情景模拟与应急响应能力评价

[J]. 地理研究, 2017, 36(6): 1138-1146.

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在气候变化和城市化背景下,日益频发的暴雨洪涝对城市人员、财产及功能造成了严重的灾害影响,突出表现为城市路网被淹导致的交通与公共服务中断。因此,城市暴雨洪涝灾害的应急管理与风险防范已经成为当前自然灾害研究的热点问题之一。选取受暴雨洪涝灾害影响严重的上海市中心城区（南京东路CBD）为研究区,采用高精度暴雨洪涝数值模拟与GIS空间分析相结合的研究方法,评估了不同降水强度情景和积水深度条件下,城市路网的可通行性与城市关键公共服务部门（医疗和公安）的应急响应能力。研究表明,基于高精度地形表面模型的城市雨洪模拟与应急响应能力评估方法,具有较高的实用性和有效性,可为城市洪灾应对与精细化应急管理提供科学支撑。

[Yin Jie.

Urban storm flood scenario simulation and emergency response capability evaluation based on high-precision topographic surface model

Journal of Geography Research, 2017, 36(6): 1138-1146.]

[本文引用: 1]

Zhao T, Zhang Y.

Transportation infrastructure restoration optimization considering mobility and accessibility in resilience measures

[J]. Transportation Research Part C: Emerging Technologies, 2020, 117: 102700. doi:10.1016/j.trc.2020.102700.

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马令勇, 王振好, 梁静, 等.

基于韧性城市理论的大庆市道路交通空间韧性策略研究

[J]. 河南科学, 2018, 36(6): 978-984.

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[Ma Lingyong, Wang Zhenhao, Liang Jing, et al.

Research on spatial resilience strategy of Daqing road traffic based on resilient city theory

Henan Science, 2018, 36(6): 978-984.]

[本文引用: 1]

Wang H W, Peng Z R, Wang D, et al.

Evaluation and prediction of transportation resilience under extreme weather events: A diffusion graph convolutional approach

[J]. Transportation Research Part C: Emerging Technologies, 2020, 115: 102619. doi: 10.1016/j.trc.2020.102619.

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Loo B P Y, Leung K Y K.

Transport resilience: The occupy central movement in Hong Kong from another perspective

[J]. Transportation Research Part A: Policy and Practice, 2017, 106: 100-115.

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Zhu Y, Xie K, Ozbay K, et al.

Data-driven spatial modeling for quantifying networkwide resilience in the aftermath of hurricanes Irene and Sandy

[J]. Transportation Research Record: Journal of the Transportation Research Board, 2017, 2604: 9-18.

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In recent years, the New York City metropolitan area was hit by two major hurricanes, Irene and Sandy. These extreme weather events disrupted and devastated the transportation infrastructure, including road and subway networks. As an extension of the authors’ recent research on this topic, this study explored the spatial patterns of infrastructure resilience in New York City with the use of taxi and subway ridership data. Neighborhood tabulation areas were used as the units of analysis. The recovery curve of each neighborhood tabulation area was modeled with the logistic function to quantify the resilience of road and subway systems. Moran's I tests confirmed the spatial correlation of recovery patterns for taxi and subway ridership. To account for this spatial correlation, citywide spatial models were estimated and found to outperform linear models. Factors such as the percentage of area influenced by storm surges, the distance to the coast, and the average elevation are found to affect the infrastructure resilience. The findings in this study provide insights into the vulnerability of transportation networks and can be used for more efficient emergency planning and management.

Ridder N N, Pitman A J, Westra S, et al.

Global hotspots for the occurrence of compound events

[J]. Nature Communication, 2020, 11(1): 5956. doi: 10.1038/s41467-020-20502-8.

[本文引用: 1]

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Hao Z, Hao F, Sinch V P, et al.

A multivariate approach for statistical assessments of compound extremes

[J]. Journal of Hydrology, 2018, 565: 87-94.

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Sathlaraj D, Punkasem T O, Wang F, et al.

Data-driven analysis on the effects of extreme weather elements on traffic volume in Atlanta, GA, USA

[J]. Computers, Environment and Urban Systems, 2018, 72: 212-220.

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