Resistance resilience of railway passenger transport networks in urban agglomerations from a spatiotemporal perspective

WU Peng; LI Dewei

doi:10.16511/j.cnki.qhdxxb.2025.26.003

Journal of Tsinghua University(Science and Technology) >

2024 , Vol. 64 >Issue 11: 1860 - 1869

DOI: https://doi.org/10.16511/j.cnki.qhdxxb.2025.26.003

SPECIAL SECTION: PUBLIC SAFETY SCIENCE AND TECHNOLOGY

Resistance resilience of railway passenger transport networks in urban agglomerations from a spatiotemporal perspective

WU Peng ,
LI Dewei

Expand

1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;
2. Frontiers Science Center for Smart High-speed Railway System, Beijing Jiaotong University, Beijing 100044, China

Received date: 2024-04-23

Online published: 2024-11-05

Fold

Abstract

[Objective] The resilience of transportation networks is a prominent research area in transportation safety. However, current studies on transportation network resilience often inadequately measure the changes in spatiotemporal travel costs for passengers, primarily focusing on the recovery phase rather than the resistance phase in two-stage resilience. There is also insufficient identification and analysis of critical segments, and a lack of suitable resilience simulation and evaluation methods for urban agglomeration railway passenger transport networks. This paper proposes a resistance resilience assessment model and a resistance resilience simulation evaluation process for urban agglomeration railway passenger transport networks centered on spatiotemporal accessibility for passengers. The aim is to evaluate the resistance resilience of these networks and identify critical segments. [Methods] This paper explores the concepts of resistance resilience and recovery resilience within transportation networks. Utilizing the complex network Space L modeling method, this paper develops a spatiotemporal weighted urban agglomeration railway passenger transport network model that considers actual railway passenger stations as network nodes. Segment interruption scenarios were simulated using attack modes involving single segment deletion and multiple segment continuous deletion. A dynamic resistance resilience evaluation index termed the network performance retention rate, was introduced based on the performance response function and spatiotemporal accessibility of passengers. This paper devises a resistance resilience assessment model and simulation evaluation process to evaluate the substitutability of segments and the overall network resistance resilience. The Chengdu—Chongqing urban agglomeration was selected as a case study to identify and compare critical segments and resistance resilience across unweighted, spatially weighted, and temporally weighted railway networks. [Results] The results of this paper were as follows: (1) The interruption of critical segments near railway hub cities could lead to a maximum network performance loss of 12.23%. It was necessary to identify critical segments through predisaster simulations. (2) Significant differences were found in the critical segments identified through resistance resilience simulations across unweighted, spatially weighted, and temporally weighted railway networks. The Spearman correlation coefficient indicated a relatively poor correlation between the critical segment rankings of unweighted and weighted railway networks. (3) The resistance resilience indices of the three railway networks highlighted that single segment interruptions significantly affected travel time. (4) Continuous interruption of identified critical segments severely affected network performance, with temporally weighted railway networks experiencing a stronger impact than spatially weighted and unweighted railway networks. Predisaster simulations solely based on topological structure or spatial distance might underestimate the consequences of risk interference. [Conclusions] The methods proposed in this paper address the gap in targeted research on the resistance resilience of railway passenger transport networks in urban agglomerations. Simulations of single segment interruption and multiple segment continuous interruption enable the identification and verification of key network segments. Additionally, analyzing the network resistance to interruptions provides a scientific foundation for transportation network planning and decision-making. Furthermore, analyzing the network's resilience evaluation index of the network performance retention rate proposed in this paper offsets the impact of disturbance time uncertainty, providing a scientific foundation for transportation network resilience research.

Key words： transportation system engineering; transportation network resilience; segment interruption; predisaster simulation; complex network; urban agglomeration

Cite this article

WU Peng , LI Dewei . Resistance resilience of railway passenger transport networks in urban agglomerations from a spatiotemporal perspective[J]. Journal of Tsinghua University(Science and Technology), 2024 , 64(11) : 1860 -1869 . DOI: 10.16511/j.cnki.qhdxxb.2025.26.003

References

[1] HOLLING C S. Resilience and stability of ecological systems [J]. Annual Review of Ecology, Evolution, and Systematics, 1973, 4: 1-23.
[2] BRUNEAU M, CHANG S E, EGUCHI R T, et al. A framework to quantitatively assess and enhance the seismic resilience of communities [J]. Earthquake Spectra, 2003, 19(4): 733-752.
[3] GU Y, FU X, LIU Z Y, et al. Performance of transportation network under perturbations: Reliability, vulnerability, and resilience [J]. Transportation Research Part E: Logistics and Transportation Review, 2020, 133: 101809.
[4] ZHANG D M, DU F, HUANG H W, et al. Resiliency assessment of urban rail transit networks: Shanghai metro as an example [J]. Safety Science, 2018, 106: 230–243.
[5] XU C, XU X G. A two-stage resilience promotion approach for urban rail transit networks based on topology enhancement and recovery optimization [J]. Physica A: Statistical Mechanics and its Applications, 2024, 635: 129496.
[6] 黄莺, 刘梦茹, 魏晋果, 等. 基于韧性曲线的城市地铁网络恢复策略研究 [J]. 灾害学, 2021, 36(1): 32-36.HUANG Y, LIU M R, WEI J G, et al. Research on urban metro network recovery strategy based on resilience curve [J]. Journal of Catastrophology, 2021, 36(1): 32-36. (in Chinese)
[7] 马书红, 武亚俊, 陈西芳. 城市群多模式交通网络结构韧性分析：以关中平原城市群为例 [J]. 清华大学学报(自然科学版), 2022, 62(7): 1228-1235.MA S H, WU Y J, CHEN X F. Structural resilience of multimodal transportation networks in urban agglomerations: A case study of the Guanzhong plain urban agglomeration network [J]. Journal of Tsinghua University (Science and Technology), 2022, 62(7): 1228-1235. (in Chinese)
[8] LU Q C. Modeling network resilience of rail transit under operational incidents [J]. Transportation Research Part A: Policy and Practice, 2018, 117: 227-237.
[9] CHEN J Q, LIU J, PENG Q Y, et al. Resilience assessment of an urban rail transit network: A case study of Chengdu subway [J]. Physica A: Statistical Mechanics and its Applications, 2022, 586: 126517.
[10] LI T, RONG L L. Impacts of service feature on vulnerability analysis of high-speed rail network [J]. Transport Policy, 2021, 110: 238-253.
[11] 李涛, 荣莉莉. 时空视角下中国高铁网络脆弱性分析 [J]. 铁道科学与工程学报, 2022, 19(7): 1801-1809.LI T, RONG L L. Vulnerability analysis of high-speed rail network in China from spatial-temporal perspective [J]. Journal of Railway Science and Engineering, 2022, 19(7): 1801-1809. (in Chinese)
[12] 吕彪, 管心怡, 高自强. 地铁网络服务韧性评估与最优恢复策略 [J]. 交通运输系统工程与信息, 2021, 21(5): 198-205.LÜ B, GUAN X Y, GAO Z Q. Evaluation and optimal recovery strategy of metro network service resilience [J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 198-205. (in Chinese)
[13] 马飞, 赵成勇, 孙启鹏, 等. 重大公共卫生灾害主动限流背景下城市轨道交通网络集成韧性 [J]. 交通运输工程学报, 2023, 23(1): 208-221.MA F, ZHAO C Y, SUN Q P, et al. Integrated resilience of urban rail transit network with active passenger flow restriction under major public health disasters [J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 208-221. (in Chinese)
[14] MA Z A, YANG X, SHANG W L, et al. Resilience analysis of an urban rail transit for the passenger travel service [J]. Transportation Research Part D: Transport and Environment, 2024, 128: 104085.
[15] CHEN J Q, LIU X W, DU B, et al. Passenger-oriented resilience assessment of an urban rail transit network under partial disturbances [J]. Journal of Transportation Engineering, Part A: Systems, 2023, 149(11): 04023114.
[16] 路庆昌, 刘鹏, 徐标, 等. 运营事件下基于韧性的地铁网络保护决策优化 [J]. 交通运输工程学报, 2023, 23(3): 209-220.LU Q C, LIU P, XU B, et al. Resilience-based protection decision optimization for metro network under operational incidents [J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 209-220. (in Chinese)
[17] 马敏, 胡大伟, 舒兰, 等. 城市轨道交通网络韧性评估及恢复策略 [J]. 吉林大学学报(工学版), 2023, 53(2): 396-404.MA M, HU D W, SHU L, et al. Resilience assessment and recovery strategy on urban rail transit network [J]. Journal of Jilin University (Engineering and Technology Edition), 2023, 53(2): 396-404. (in Chinese)
[18] BEŠINOVIĆ N. Resilience in railway transport systems: A literature review and research agenda [J]. Transport Reviews, 2020, 40(4): 457-478.
[19] 杨琦, 张雅妮, 周雨晴, 等. 复杂网络理论及其在公共交通韧性领域的应用综述 [J]. 中国公路学报, 2022, 35(4): 215-229.YANG Q, ZHANG Y N, ZHOU Y Q, et al. A review of complex network theory and its application in the resilience of public transportation systems [J]. China Journal of Highway and Transport, 2022, 35(4): 215-229. (in Chinese)
[20] ZHOU Y M, WANG J W, YANG H. Resilience of transportation systems: Concepts and comprehensive review [J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(12): 4262-4276.
[21] 刘杰, 陈锦渠, 彭其渊, 等. 城市轨道交通网络可靠性和运输服务质量评估 [J]. 西南交通大学学报, 2021, 56(2): 395-402.LIU J, CHEN J Q, PENG Q Y, et al. Reliability and service quality evaluation for urban rail transit network [J]. Journal of Southwest Jiaotong University, 2021, 56(2): 395-402. (in Chinese)
[22] 马超群, 张爽, 陈权, 等. 客流特征视角下的轨道交通网络特征及其脆弱性 [J]. 交通运输工程学报, 2020, 20(5): 208-216.MA C Q, ZHANG S, CHEN Q, et al. Characteristics and vulnerability of rail transit network based on perspective of passenger flow characteristics [J]. Journal of Traffic and Transportation Engineering, 2020, 20(5): 208-216. (in Chinese)
[23] 马飞, 委笑琳, 孙启鹏, 等. 考虑互补效应的城市群多模式客运网络鲁棒性 [J]. 浙江大学学报(工学版), 2024, 58(2): 388-398.MA F, WEI X L, SUN Q P, et al. Robustness of multimodal passenger transport network in urban agglomeration considering complementary effect [J]. Journal of Zhejiang University (Engineering Science), 2024, 58(2): 388-398. (in Chinese)
[24] 李成兵, 张帅, 杨志成, 等. 蓄意攻击下城市群客运交通网络级联抗毁性仿真 [J]. 交通运输系统工程与信息, 2019, 19(2): 14-21.LI C B, ZHANG S, YANG Z C, et al. Invulnerability simulation in urban agglomeration passenger traffic network under targeted attacks [J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(2): 14-21. (in Chinese)
[25] 张洁斐, 任刚, 马景峰, 等. 基于韧性评估的地铁网络修复时序决策方法 [J]. 交通运输系统工程与信息, 2020, 20(4): 14-20.ZHANG J F, REN G, MA J F, et al. Decision-making method of repair sequence for metro network based on resilience evaluation [J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(4): 14-20. (in Chinese)
[26] CHEN J Q, LIU J, PENG Q Y, et al. Strategies to enhance the resilience of an urban rail transit network [J]. Transportation Research Record: Journal of the Transportation Research Board, 2022, 2676(1): 342-354.
[27] MARTÍN B, ORTEGA E, CUEVAS-WIZNER R, et al. Assessing road network resilience: An accessibility comparative analysis [J]. Transportation Research Part D: Transport and Environment, 2021, 95: 102851.
[28] BI W, MACASKILL K, SCHOOLING J. Old wine in new bottles? Understanding infrastructure resilience: Foundations, assessment, and limitations [J]. Transportation Research Part D: Transport and Environment, 2023, 120: 103793.
[29] 侯本伟, 游丹, 范世杰, 等. 基于网络效率的城市轨道交通网络抗震韧性评估 [J]. 清华大学学报(自然科学版), 2024, 64(3): 509-519.HOU B W, YOU D, FAN S J, et al. Seismic resilience evaluation of urban rail transit network based on network efficiency [J]. Journal of Tsinghua University (Science and Technology), 2024, 64(3): 509-519. (in Chinese)
[30] CHEN J Q, LIU J, DU B, et al. Resilience assessment of an urban rail transit network under short-term operational disturbances [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(12): 24841-24853.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References

Visited