多级洪涝灾害中上海市通勤时空结构韧性测度与 优化研究
Resilience of Shanghai's Urban Spatiotemporal Structure under Varying Flood Scenarios
沈 尧
同济大学建筑与城市规划学院 同济大学中英联合城市科学实验室 副教授，博士生导师，eshenyao@tongji.edu.cn
徐子寒
同济大学建筑与城市规划学院 硕士研究生
冯韵洁
同济大学建筑与城市规划学院 硕士研究生
摘要: 城市空间与功能高度耦合形成复杂的时空城市系统。时空城市系统不仅承载并适应各类城市挑战，也是面向城市韧性提升的 规划干预对象。理解真实城市系统的韧性演化机制及其复杂的社会经济效应，是韧性城市建设与规划的基础。融合手机信令、 道路路网、房价等多源数据，构建城市通勤时空结构模拟框架，并测度其在不同等级洪涝情境下的动态韧性特征及空间、社会 分异规律。通过识别韧性瓶颈，提出相应的规划干预策略，并将方法应用于上海市，揭示不同地区的韧性差异与多种韧性组合 模式。结果表明，城市通勤时空结构韧性呈现出多维复杂性，社会经济条件下的韧性空间分异高于性别等因素，并表现出基于 风险分布的不均衡社会排斥效应。此外，通勤效率瓶颈的修复存在显著的时效性和空间分异，并可实现较精准的定位。研究结 果验证了本方法的有效性，揭示了上海市通勤时空结构韧性的洪涝灾害响应机制，并为韧性优化与精细化规划提供了参考。
Abstract: Urban space and function are highly coupled, forming a complex spatiotemporal system that not only accommodates and responds to various urban challenges but also serves as a critical target for enhancing urban resilience and informing planning interventions. Understanding the resilience dynamics of real-world urban systems and their intricate socio-economic effects is fundamental to the development of resilient cities. This study integrates multi-source data, including mobile signalling, road networks, and housing prices, to construct a simulation framework for urban commuting spatiotemporal structures. It further assesses their dynamic resilience characteristics and spatial-social disparities under varying levels of flood scenarios. By identifying resilience bottlenecks, the study proposes targeted planning interventions and applies the framework to Shanghai to unveil regional resilience variations and diverse resilience typologies. The findings indicate that the resilience of urban commuting spatiotemporal structures exhibits multidimensional complexity, with disparities among social strata exceeding those related to gender and manifesting as uneven socio-spatial exclusion effects driven by risk distribution. Moreover, the mitigation of commuting efficiency bottlenecks demonstrates significant temporal sensitivity and spatial heterogeneity, enabling precise intervention targeting. The results validate the effectiveness of the proposed approach, elucidate the flood response mechanisms of Shanghai's commuting spatiotemporal resilience, and offer insights for resilience optimisation and refined urban planning.
关键词：韧性城市；时空大数据；洪涝灾害；复杂网络；社会公平；时空模拟
Keyword: urban resilience; spatiotemporal big data; flood disasters; complex networks; social equity; spatiotemporal simulation
中图分类号：TU984
文献标识码: A
BATTY M. The new science of cities[M]. Cambridge:
MIT Press, 2013.
龙瀛，张宇，崔承印. 利用公交刷卡数据分析北
京职住关系和通勤出行[J]. 地理学报，2012，
67（10）：1339-1352.
LONG Ying, ZHANG Yu, CUI Chengyin. Identi-
fying commuting pattern of Beijing using bus smart card data[J]. Acta Geographica Sinica, 2012,
67(10): 1339-1352.
柴彦威，张雪，孙道胜. 基于时空间行为的城市
生活圈规划研究——以北京市为例[J]. 城市规
划学刊，2015（3）：61-69.
CHAI Yanwei, ZHANG Xue, SUN Daosheng. A study on life circle planning based on space time
behavioural analysis: a case study of Beijing[J].
Urban Planning Forum, 2015(3): 61-69.
KULP S A, STRAUSS B H. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding[J]. Nature Communications,
2019, 10: 4844.
甄峰，李哲睿，谢智敏. 基于人口流动的城市内
部空间结构特征及其影响因素分析——以南
京市为例[J]. 地理研究，2022，41（6）：1525-
1539.
ZHEN Feng, LI Zherui, XIE Zhimin. Analysis of urban internal spatial structure characteristics and its influencing factors based on population flow: a
case study of Nanjing[J]. Geographical Research,
2022, 41(6): 1525-1539.
沈尧. 协频城市：时空数据增强设计中的频度协
同[J]. 上海城市规划，2022（3）：8-13.
SHEN Yao. Synergised city: frequency synergy in spatiotemporal data-augmented-design[J].
Shanghai Urban Planning Review, 2022(3): 8-13.
BASSOLAS A, BARBOSA-FILHO H, DICKINSON B, et al. Hierarchical organization of urban mobility and its connection with city livability[J]. Nature Communications 2019, 10: 4817.
MA Z, ZHU D. Collective flow-evolutionary patterns reveal the mesoscopic structure between snapshots of spatial network[J]. International
Journal of Geographical Information Science,
2024, 39(1): 86-117.
SHEN Y, KARIMI K. Urban function connec-
tivity: characterisation of functional urban streets with social media check-in data[J]. Cities, 2016, 55: 9-21.
吴志强，郑迪，邓弘. 大都市战略空间制胜要素
的迭代[J]. 城市规划学刊，2020（5）：9-17.
WU Zhiqiang, ZHENG Di, DENG Hong. Iteration
of winning elements in the metropolitan strategic
space[J]. Urban Planning Forum, 2020(5): 9-17.
CHEN Z, GONG Z, YANG S, et al. Impact of extreme weather events on urban human flow: a
perspective from location-based service data[J]. Computers, Environment and Urban Systems,
2020, 83: 101520.
COLEMAN N, LIU C, ZHAO Y, et al. Lifestyle pattern analysis unveils recovery trajectories of
communities impacted by disasters[J]. Humanities
and Social Sciences Communications, 2023, 10:
803.
赵瑞东，方创琳，刘海猛. 城市韧性研究进展与
展望[J]. 地理科学进展，2020，39（10）：1717-
1731.
ZHAO Ruidong, FANG Chuanglin, LIU Haimeng. Progress and prospect of urban resilience research[
J]. Progress in Geography, 2020, 39(10): 1717-1731.
DING R, UJANG N, HAMID H B, et al. Detecting the urban traffic network structure dynamics through the growth and analysis of multi-layer networks[J]. Physica A: Statistical Mechanics and
its Applications, 2018, 503: 800-817.
SERRE D, HEINZLEF C. Assessing and mapping
urban resilience to floods with respect to cascading effects through critical infrastructure networks[J]. International Journal of Disaster Risk Reduction, 2018, 30: 235-243.
仇保兴. 基于复杂适应系统理论的韧性城市设
计方法及原则[J]. 城市发展研究，2018，25（10）：
1-3.
QIU Baoxing. Methods and principles of designing
resilient city based on complex adaptive system
theory[J]. Urban Development Studies, 2018,
25(10): 1-3.
颜文涛，任婕，张尚武，等. 上海韧性城市规划：
关键议题、总体框架和规划策略[J]. 城市规划学
刊，2022（3）：19-28.
YAN Wentao, REN Jie, ZHANG Shangwu, et al. Resilient urban planning in Shanghai: key issues,
general framework, and planning strategies[J].
Urban Planning Forum, 2022(3): 19-28.
ALESSANDRETTI L, OROZCO L G N, SABERI
M, et al. Multimodal urban mobility and multilayer
transport networks[J]. Environment and Planning
B: Urban Analytics and City Science, 2023, 50(8):
2038-2070.
IG K, KA S, Y M, et al. When floods hit the road:
resilience to flood-related traffic disruption in the San Francisco Bay Area and beyond[J]. Science Advances, 2020, 6(32): eaba2423.
RAJPUT A A, NAYAK S, DONG S, et al. Anatomy of perturbed traffic networks during urban flooding[J]. Sustainable Cities and Society, 2023, 97: 104693.
BOEING G, HA J. Resilient by design: simulating
street network disruptions across every urban area
in the world[J]. Transportation Research Part A: Policy and Practice, 2024, 182: 104016.
PORTA S, CRUCITTI P, LATORA V. The network
analysis of urban streets: a dual approach[J]. Physica A: Statistical Mechanics and its Applica-
tions, 2006, 369(2): 853-866.
RUS K, KILAR V, KOREN D. Resilience assessment of complex urban systems to natural disasters: a new literature review[J]. International Journal of Disaster Risk Reduction, 2018, 31: 311-
330.
沈尧，徐怡怡，刘乐峰. 网络渗流视角下的城市
肌理识别与测度研究[J]. 城市规划学刊，2021
（5）：40-48.
SHEN Yao, XU Yiyi, LIU Lefeng. Urban texture analysis from the perspective of network percolation[J]. Urban Planning Forum, 2021(5): 40-48.
BATTY M. Cities as complex systems: scaling, interaction, networks, dynamics and urban morphologies[M]//MEYERS R. Encyclopedia of complexity and systems science. New York: Springer, 2009.
ALLCOTT H, DIAMOND R, DUBÉ J P, et al. Food deserts and the causes of nutritional inequality[J]. Quarterly Journal of Economics,
2019, 134(4): 1793-1844.
PARR J. Metropolitan area definition: a reevalua-
tion of concept and statistical practice[J]. Journal
of Marketing Research, 1970, 7(3): 406.
LIU R-R, EISENBERG D A, SEAGER T P, et al. The "weak" interdependence of infrastructure systems produces mixed percolation transitions in
multilayer networks[J]. Scientific Reports, 2018, 8:
2111.
沈尧，卓健，吴志强. 精准城市设计面向社会效
应精准提升的城市形态[J]. 时代建筑，2021（1）：
26-33.
SHEN Yao, ZHUO Jian, WU Zhiqiang. Precise urban design toward socially sustainable urban
form[J]. Time + Architecture, 2021(1): 26-33.
HONG B, BONCZAK B J, GUPTA A, et al. Measuring inequality in community resilience to natural disasters using large-scale mobility data[J].
Nature Communications, 2021, 12(1): 1870.
HUNG H-C, YANG C-Y, CHIEN C-Y, et al. Building resilience: mainstreaming community participation into integrated assessment of resilience to climatic hazards in metropolitan land
use management[J]. Land Use Policy, 2016, 50: 48-58.
KWAN M-P. The limits of the neighborhood effect: contextual uncertainties in geographic, environmental health, and social science research[J]. Annals of the American Association of
Geographers, 2018, 108(6): 1482-1490.
王德，钟炜菁，谢栋灿，等. 手机信令数据在城市
建成环境评价中的应用——以上海市宝山区为
例[J]. 城市规划学刊，2015（5）：82-90.
WANG De, ZHONG Weijing, XIE Dongcan, et al. The application of cell phone signaling data in
the assessment of urban built environment: a case
study of Baoshan District in Shanghai[J]. Urban Planning Forum, 2015(5): 82-90.
申悦，柴彦威. 基于日常活动空间的社会空间分
异研究进展[J]. 地理科学进展，2018，37（6）：
853-862.
SHEN Yue, CHAI Yanwei. Progress of research on sociospatial differentiation based on daily activity space of urban residents[J]. Progress in Geography, 2018, 37(6): 853-862.
HE R, ZHANG L, TIONG R L K. Responding of metro stations to upcoming floods: to close or to protect?[J]. Reliability Engineering & System Safety, 2025, 256: 110683.
QIAN J, DU Y, LIANG F, et al. Measuring com-
munity resilience inequality to inland flooding
using location aware big data[J]. Cities, 2024, 149:
104915.
LEI D, CHENG L, WANG P, et al. Identifying service bottlenecks in public bikesharing flow networks[J]. Journal of Transport Geography, 2024,
116: 103830.
FAN C, WU F, MOSTAFAVI A. Discovering the influence of facility distribution on lifestyle
patterns in urban populations[J]. Developments in
the Built Environment, 2024, 17: 100348.
ROY K C, CEBRIAN M, HASAN S. Quantifying
human mobility resilience to extreme events using geo-located social media data[J]. EPJ Data
Science, 2019, 8: 18.
MISHRA S K, SINGH V P. Soil conservation service curve number (SCS-CN) methodology[M].
Dordrecht: Springer, 2003.
DU S, WANG C, SHEN J, et al. Mapping the capacity of concave green land in mitigating urban
pluvial floods and its beneficiaries[J]. Sustainable Cities and Society, 2019, 44: 774-782.
黄清雨，董军刚，李梦雅，等. 暴雨内涝危险性情
景模拟方法研究——以上海中心城区为例[J].
地球信息科学学报，2016，18（4）：506-513.
HUANG Qingyu, DONG Jungang, LI Mengya, et
al. Research on the scenario simulation method of
rainstorm waterlogging hazard: a case study in the
central urban area of Shanghai[J]. Journal of Geo-
information Science, 2016, 18(4): 506-513.
WANG P, HUNTER T, BAYEN A M, et al. Understanding road usage patterns in urban areas[J]. Scientific Reports, 2012, 2: 1001.
United States Bureau of Public Roads. Traffic assignment manual for application with a large, high speed computer[M]. Washington, D.C.: U.S. Department of Commerce, Bureau of Public Roads,
Office of Planning, Urban Planning Division: For sale by the Superintendent of Documents, U.S. Government Printing Office, 1964.
丁亮，钮心毅，施澄. 多中心空间结构的通勤效
率——上海和杭州的实证研究[J]. 地理科学，
2021，41（9）：1578-1586.
DING Liang, NIU Xinyi, SHI Cheng. The commuting efficiency of polycentric spatial structure: an empirical study of Shanghai and
Hangzhou[J]. Scientia Geographica Sinica,
2021,41(9): 1578-1586.
谢栋灿，王德，钟炜菁，等. 上海市建成环境的评
价与分析——基于手机信令数据的探索[J]. 城
市规划，2018，42（10）：97-108.
XIE Dongcan, WANG De, ZHONG Weijing, et al.
Assessment and analysis on built environment of
Shanghai: an exploration based on mobile phone
signaling data[J]. City Planning Review, 2018, 42(10): 97-108.
岳丽莹，朱宇，李开明. 双视角、双维度下超大城
市的通勤效率——以上海市为例[J]. 地理研究，
2024，43（2）：429-445.
YUE Liying, ZHU Yu, LI Kaiming. Commuting efficiency in dual dimensions of education and travel mode in Shanghai from the perspectives
of average distance and spatial organization[J]. Geographical Research, 2024, 43(2): 429-445.
