Evaluation and spatio-temporal characteristics analysis of waterlogging resilience of urban road traffic system

doi:10.16265/j.cnki.issn1003-3033.2026.08.0926

Abstract

Abstract:

In order to cope with the interference of waterlogging events on the operation of the urban road traffic system and enhance the adaptability and recovery ability of the traffic system, a road traffic waterlogging resilience model was constructed to analyze the temporal and spatial evolution characteristics of resilience. Based on a geographic information system (GIS), a two-layer analysis platform of road network waterlogging, including a lower-layer rainstorm flood analysis model and an upper-layer two-dimensional hydrodynamic model, was built. The dynamic weight function was introduced to construct the dynamic evaluation model of road network topology, and the travel time ratio was used as the traffic performance evaluation index. Then, the comprehensive evaluation system for road traffic resilience under waterlogging scenarios was constructed. The kernel density estimation method was used to analyze the temporal and spatial dynamic evolution of resilience, and the spatial correlation analysis of resilience was carried out based on the network global Moran's index and the spatial analysis technology of cold and hot spots, so as to reveal the temporal and spatial characteristics of road traffic resilience under waterlogging scenarios. A case study on the resilience of the road traffic system under different rainfall intensities was conducted using the road network in Qingdao Economic Development Zone as an example. The results show that as rainfall intensity increases across scenarios with return periods ranging from 10 to 100 years, the range, depth and duration of road waterlogging show an increasing trend. The peak time of water accumulation advances from 105 min to 90 min, and the proportion of inundated roads increases from 4.75% to 20.08%. The road network resilience shows a decreasing trend, and the minimum value decreases from 0.86 to 0.74. The recovery time shows an extended trend, with the recovery delay increasing by up to 67%. The hot spot (high value) and cold spot (low value) of waterlogging resilience exhibit a clustered spatial distribution. The spatial overlap between cold spots and waterlogged areas reaches 80%, and the hot spot areas shift spatially as the waterlogged area expands. The lower the technical grade of a road is, the greater the impact of waterlogging is, and the failure of local roads leads to contiguous vulnerable areas. The high-density road network has good functional stability under low-intensity rainfall, and the failure of key road sections in sparse road networks causes spatial spillover effects.

Key words: urban roads, traffic system, traffic resilience, waterlogging, resilience evaluation, traffic performance

CLC Number:

Yang Jinshun, Gao Xiaohan, Wang Zhaoqiang, Zhang Fahui, Guo Chuanwei, Luan Siliang. Evaluation and spatio-temporal characteristics analysis of waterlogging resilience of urban road traffic system[J]. China Safety Science Journal, 2026, 36(8): 206-215.

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References 12

[1]	Lu Yiding, Zhang Zhan, Fang Xinyi, et al. Resilience of urban road network to malignant traffic accidents[J]. Journal of Advanced Transportation, 2022, 2022:DOI:10.1155/2022/3682472.
[2]	Dong Shangjia, Gao Xinyu, Ali M, et al. Characterizing resilience of flood disrupted dynamic transportation network through the lens of link reliability and stability[J]. Reliability Engineering and System Safety, 2023, 232:DOI:10.1016/j.ress.2022.109071.
[3]	陈津怡, 李铁柱, 郭竞文, 等. 相继故障下城市轨道交通系统韧性评估方法[J]. 中国安全科学学报, 2024, 34(9): 202-208. doi: 10.16265/j.cnki.issn1003-3033.2024.09.1963
	Chen Jinyi, Li Tiezhu, Guo Jingwen, et al. Resilience assessment method of urban rail transit system under cascading failure[J]. China Safety Science Journal, 2024, 34(9): 202-208. doi: 10.16265/j.cnki.issn1003-3033.2024.09.1963
[4]	吕彪, 高自强, 管心怡, 等. 基于日变交通配流的城市道路网络韧性评估[J]. 西南交通大学学报, 2020, 55(6):1181-1190.
	Lyu Biao, Gao Ziqiang, Guan Xinyi, et al. Resilience assessment of urban road network based on day-to-day traffic assignment[J]. Journal of Southwest Jiaotong University, 2020, 55(6):1181-1190.
[5]	Ni Xiaoyong, Osaragi T, Huang Hong, et al. Resilience-oriented performance assessment method for road-traffic system: a case study in Beijing, China[J]. KSCE Journal of Civil Engineering, 2021, 25(10):1-18. doi: 10.1007/s12205-020-0535-y
[6]	Miller-Hooks E, Zhang Xiaodong, Faturechi R. Measuring and maximizing resilience of freight transportation networks[J]. Computers and Operations Research, 2012, 39(7):1633-1643. doi: 10.1016/j.cor.2011.09.017
[7]	Li Yang, Wu Jialu, Xiao Yunjiang, et al. Resilience analysis of highway network under rainfall using a data-driven percolation theory-based method[J]. Physica A: Statistical Mechanics and its Applications, 2024:DOI:10.1016/J.PHYSA.2024.129639.
[8]	Wang Hongwei, Peng Zhongren, Wang Dongsheng, et al. Evaluation and prediction of transportation resilience under extreme weather events: a diffusion graph convolutional approach[J]. Transportation Research Part C, 2020, 115:DOI:10.1016/j.trc.2020.102619.
[9]	胡小兵, 李佳茹, 刘骋越, 等. 一种评估网络结点和链接重要性的改进介数[J]. 中国安全科学学报, 2022, 32(3): 33-40. doi: 10.16265/j.cnki.issn1003-3033.2022.03.005
	Hu Xiaobing, Li Jiaru, Liu Chengyue, et al. An improved betweenness for importance assessment of network nodes and links[J]. China Safety Science Journal, 2022, 32(3): 33-40. doi: 10.16265/j.cnki.issn1003-3033.2022.03.005
[10]	Zhang Mingliang, Xu Menghua, Wang Zhaoli, et al. Assessment of the vulnerability of road networks to urban waterlogging based on a coupled hydrodynamic model[J]. Journal of Hydrology, 2021,603:DOI:10.1016/J.JHYDROL.2021.127105.
[11]	Li Zhuohong, He Wei, Cheng Mofan, et al. SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data[DB/OL]. [2024-12-26]. https://doi.org/10.5194/essd-15-4749-2023.
[12]	住房和城乡建设部, 中国气象局. 城市暴雨强度公式编制和设计暴雨雨型确定技术导则[Z/OL].[2025-02-16]. http://msks.qxcbs.com/file_server/upload/file/20191211/135029-5628_L.pdf.

参数名称	率定值	参数名称	率定值
不透水区洼蓄量/mm	2	透水区洼蓄量/mm	4
无洼蓄不透水区比例/%	10	不透水区曼宁系数	0.015
管道曼宁系数	0.012	透水区曼宁系数	0.06
最大入渗率	76	最小入渗率	0.5
衰减常数	4	完全干燥天数/d	7

重现期/a	分类	道路等级
	分类	快速路		主干路		次干路		支路
	淹没深度/m	<0.27	≥0.27	<0.27	≥0.27	<0.27	≥0.27	<0.27	≥0.27
10	淹没长度/km	0.00	0.00	43.63	0.00	37.54	1.98	31.35	1.07
10	占比/%	0.00	0.00	44.92	0.00	52.64	2.78	57.66	1.97
20	淹没长度/km	0.00	0.00	45.30	0.00	38.59	4.41	32.49	2.33
20	占比/%	0.00	0.00	46.64	0.00	54.11	6.18	59.76	4.29
50	淹没长度/km	0.00	0.00	47.15	3.40	39.66	5.13	34.08	3.61
50	占比/%	0.00	0.00	48.54	3.50	55.61	7.19	62.68	6.64
100	淹没长度/km	0.00	0.00	48.55	3.42	40.35	5.45	34.92	4.85
100	占比/%	0.00	0.00	49.98	3.52	56.58	7.64	64.23	8.92

[1]	Lou Hezhuang, Jiang Hao. Construction and parameter analysis of vulnerability measurement model for waterlogging disaster in underground space of Beijing [J]. China Safety Science Journal, 2026, 36(8): 251-259.
[2]	MA Liqiang, ZHAO Ang, ZHENG Binbin. Research on influencing factors of rainstorm waterlogging resilience at rail transit stations based on complex network [J]. China Safety Science Journal, 2025, 35(6): 142-147.
[3]	ZHOU Sicong. Propagation blocking mechanism of urban waterlogging disaster chain in Changsha based on complex network [J]. China Safety Science Journal, 2025, 35(12): 230-237.
[4]	HAO Jingkai, LI Hongyan, ZHANG Feng, ZHANG Chong, MAO Libo, LIU Dawei. Risk assessment of urban waterlogging and site selection of storage tank based on MCDM-BPNN [J]. China Safety Science Journal, 2024, 34(8): 214-221.
[5]	XING Jinduo, YANG Wei, ZHANG Jun. Quantitative analysis of accident causes of urban subway waterlogging from perspective of complex socio-technical systems [J]. China Safety Science Journal, 2024, 34(11): 193-201.
[6]	DUAN Zaipeng, LI Fan, GUO Jin, LI Jiong. Integrated warning model for structural safety of buildings in urban waterlogged area [J]. China Safety Science Journal, 2023, 33(7): 173-180.
[7]	TANG Shaohu, ZHU Wei, CHENG Guang, ZHENG Jianchun, ZHOU Jin. Safety resilience assessment of urban road traffic system under rainstorm waterlogging [J]. China Safety Science Journal, 2022, 32(7): 143-150.
[8]	WEI Qiang, LIU Jiaqi, WANG Jingchun, WANG Peng. Evaluation of safety resilience in tunnel construction based onideal fuzzy matter element [J]. China Safety Science Journal, 2021, 31(8): 62-68.
[9]	WANG Junwu, WU Han, YANG Tingyou. Vulnerability assessment of rainfall and waterlogging in subway stations based on projection pursuit model [J]. China Safety Science Journal, 2019, 29(9): 1-7.
[10]	WU Fuwei, WANG Xin, WANG Chang. Research on lateral risk avoidance characteristics of drivers on urban roads [J]. China Safety Science Journal, 2018, 28(8): 7-12.
[11]	MA Xiaoli, LIU Yujie, LU Jian. Research on assessment of risk in hazardous materials transportation on urban road [J]. China Safety Science Journal, 2018, 28(5): 179-184.
[12]	. Water Traffic System Safety Modality:Definition and Modeling [J]. China Safety Science Journal, 2013, 23(6): 32-.
[13]	. Design and Realization of Assessment System on Dynamic Risk of Ship Collision with Bridge [J]. China Safety Science Journal, 2013, 23(4): 120-.