测绘通报 ›› 2026, Vol. 0 ›› Issue (10): 161-167.doi: 10.13474/j.cnki.11-2246.2026.1025

• 技术交流 • 上一篇    

基于GIS与复杂网络理论的铁路地理网络脆弱性分析

周命端1, 谭瀚1, 王雪甜2, 胡新春2, 王子纯1   

  1. 1. 北京建筑大学测绘与城市空间信息学院, 北京 102616;
    2. 北京交通大学土木建筑工程学院, 北京 100044
  • 收稿日期:2026-06-22 发布日期:2026-11-06
  • 作者简介:周命端(1985—),男,博士,副教授,主要研究方向为卫星定位与激光雷达技术、测绘与空间信息技术。E-mail:mdzhou@bucea.edu.cn
  • 基金资助:
    北京建筑大学教育科学研究重点项目(Y2402)

Vulnerability analysis of railway geographic network based on GIS and complex network theory

Zhou Mingduan1, Tan Han1, Wang Xuetian2, Hu Xinchun2, Wang Zichun1   

  1. 1. School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China;
    2. School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
  • Received:2026-06-22 Published:2026-11-06

摘要: [目的] 本文旨在构建融合地理属性与拓扑特征的铁路地理网络模型,评估不同攻击策略下网络结构与功能的变化,分析铁路网络结构脆弱性,为铁路运输系统抗干扰能力提升提供科学依据。[方法] 基于GIS与复杂网络理论,利用Space L建模方法构建铁路地理网络,分析聚类系数、度分布、介数等拓扑特征;以巨连通分支相对大小和网络全局效率为评估指标,结合Python与NetworkX库,设计随机攻击、初始度/介数攻击及重计算度/介数攻击等不同攻击策略,系统评估节点与边失效条件下网络的响应过程。[结果] 结果表明:我国铁路地理网络具有小世界与无标度双重特性,空间上呈现明显聚类效应;蓄意攻击对网络连通性与全局效率的破坏显著强于随机攻击,其中重计算介数攻击在初期即可使网络几乎完全失连,网络全局效率在攻击中后期趋近于0;随机攻击下网络保持较强韧性。[结论] 铁路地理网络的脆弱性高度依赖关键节点与边的分布,重计算介数攻击能更精准识别网络薄弱环节。

关键词: 铁路地理网络, 复杂网络, GIS, 脆弱性分析, 攻击策略

Abstract: [Purposes] This paper constructs a railway geographic network model integrating geographical attributes and topological characteristics,evaluates the changes of network structure and function under different attack strategies,and analyzes the vulnerability of railway network structure,so as to provide a scientific basis for improving the anti-interference ability of railway transportation system. [Methods] Based on the GIS technology and complex network theory,the Chinese railway geographic network is constructed using the Space L modeling method.Topological characteristics such as clustering coefficient,degree distribution,and betweenness centrality are analyzed.Taking the relative size of the giant connected component and network global efficiency as evaluation indicators,simulation strategies including random attack,initial degree/betweenness attack,and recalculated degree/betweenness attack are designed using Python and NetworkX to systematically evaluate the response of the network under node and edge failures. [Findings] The Chinese railway geographic network exhibits both small-world and scale-free characteristics,with significant spatial clustering effects.Intentional attacks cause more severe damage to network connectivity and global efficiency than random attacks.Among them,the recalculated betweenness attack reduces the giant connected component by nearly 100%in the early stage,and the network global efficiency approaches zero in the middle and late stages of the attack.Under random attacks,the network maintains relatively strong resilience. [Conclusions] The vulnerability of the railway geographic network is highly dependent on the distribution of key nodes and edges,and the recalculated betweenness attack can more accurately identify network weak links.

Key words: railway geographic network, complex network, GIS, vulnerability analysis, attack strategy

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