时空视角下城市群铁路客运网络抵抗韧性

吴鹏; 李得伟

doi:10.16511/j.cnki.qhdxxb.2025.26.003

清华大学学报（自然科学版） >

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

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

专题：公共安全科学与技术

时空视角下城市群铁路客运网络抵抗韧性

吴鹏 ,
李得伟

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1. 北京交通大学交通运输学院, 北京 100044;
2. 北京交通大学智慧高铁系统前沿科学中心, 北京 100044

收稿日期: 2024-04-23

网络出版日期: 2024-11-05

基金资助

国家自然科学基金项目(71971019);中央高校基本科研业务费专项资金项目(2022JBQY006);中国铁路北京局集团有限公司科技研究开发计划课题(2023BK01)

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

WU Peng ,
LI Dewei

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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

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摘要

交通运输网络抵抗韧性是交通安全领域的研究热点, 为有效评估城市群铁路客运网络的抵抗韧性, 并识别关键路段, 该文首先基于复杂网络Space L建模方法, 建立了以实际铁路客运站为网络节点的时空加权城市群铁路客运网络模型; 其次, 采用单路段删除和多路段连续删除的攻击模式模拟路段中断情景, 提出将基于性能响应函数和旅客时空可达性的网络性能保留率作为客运网络动态抵抗韧性评估指标, 利用抵抗韧性评估模型和仿真评估流程评估路段可替代能力和网络抵抗韧性; 最后, 进行了成渝城市群铁路客运网络实例研究。结果表明：靠近铁路枢纽城市的部分关键路段中断, 最高可使铁路客运网络性能损失12.23%, 从灾前仿真角度识别关键路段具有重要意义; 无权、空间加权和时间加权铁路网络抵抗韧性仿真识别出的关键路段存在明显差异, Spearman相关系数验证了无权与加权铁路网络路段可替代能力的排序相关性较差; 3种铁路网络的抵抗韧性指数表明单路段中断对旅客出行时间的影响较大; 关键路段连续中断对网络性能的影响较大, 对时间加权铁路网络性能的影响大于无权和空间加权铁路网络, 仅从网络拓扑结构和空间距离角度进行灾前仿真, 容易低估风险因素的干扰后果。该文所提方法可为交通网络规划和韧性研究提供参考。

关键词： 交通运输系统工程; 交通网络韧性; 路段中断; 灾前仿真; 复杂网络; 城市群

本文引用格式

吴鹏 , 李得伟 . 时空视角下城市群铁路客运网络抵抗韧性[J]. 清华大学学报（自然科学版）, 2024 , 64(11) : 1860 -1869 . DOI: 10.16511/j.cnki.qhdxxb.2025.26.003

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

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