基于引力影响模型的轨道交通网络关键节点识别研究

doi:10.16097/j.cnki.1009-6744.2025.01.011

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (1): 102-112.DOI: 10.16097/j.cnki.1009-6744.2025.01.011

基于引力影响模型的轨道交通网络关键节点识别研究

左忠义^*1，刘泽宇¹，杨广川²

1. 大连交通大学，交通工程学院，辽宁大连116028；2.北卡罗来纳州立大学，交通研究与教育研究所，北卡罗来纳州NC27695，美国

收稿日期:2024-10-27 修回日期:2024-12-24 接受日期:2024-12-25 出版日期:2025-02-25 发布日期:2025-02-21
作者简介:左忠义(1973—)，男，辽宁大连人，教授。
基金资助:
辽宁省科学技术计划项目(2021JH4/10100061)；辽宁省教育厅基本科研项目(JYTMS20230007)。

Key Node Identification of Rail Transit Network Based on Gravity Influence Model

ZUO Zhongyi^*1, LIU Zeyu¹, YANG Guangchuan²

1. School of Transportation Engineering, Dalian Jiaotong University, Dalian 116028, Liaoning, China; 2. Institute for Transportation Research and Education, North Carolina State University, North Carolina State NC27695, USA

Received:2024-10-27 Revised:2024-12-24 Accepted:2024-12-25 Online:2025-02-25 Published:2025-02-21
Supported by:
Science and Technology Plan Project of Liaoning Province, China (2021JH4/10100061)；Basic Research Project of Liaoning Provincial Education Department, China (JYTMS20230007)。

摘要/Abstract

摘要： 有效识别轨道交通网络中的关键节点，有助于分析轨道交通网络鲁棒性，并制定轨道交通网络抗风险预案，保障轨道交通网络的正常运行。本文考虑轨道交通网络中节点之间的相互影响情况，选取连接重要度(DC)、路径重要度(BC)和可达重要度(CC)作为节点重要度的综合衡量指标；将现实轨道交通网络构造为相应拓扑网络，借助引力影响模型识别轨道交通网络关键节点，并分析不同影响因素下的网络性能差异，得出最佳引力影响半径与攻击策略；结合现实轨道交通网络，从引力角度分析轨道交通网络关键节点，并提出相关建议。结果表明：节点的重要度由目标节点与其他节点产生的引力作用组成；当引力影响模型的引力影响半径R=8，并选取动态攻击策略时，与R=7和R=9相比，最大连通子图相对大小下降率分别提高13.25%和10.39%，网络客流效率相对大小下降率分别提高5.12%和6.71%；相较于FGM(融合引力模型)、GC(万有引力中心性指标)、KSGC(基于k-shell改进的万有引力模型)和考虑集体影响力的CI模型，引力影响模型在轨道交通网络关键节点识别中有明显优势。此外，在攻击前30个节点后,北京市地铁网络最大连通子图相对大小降低91.68%，网络客流效率相对大小降低86.17%，表明引力影响模型在北京市地铁网络中具有适用性与有效性。通过引力影响模型识别轨道交通网络中关键节点，可以为分析网络鲁棒性提供新的思考角度，为决策者制定网络抗风险预案提供有效依据。

关键词: 城市交通, 关键节点, 引力影响模型, 网络性能, 复杂网络

Abstract: The identification of key nodes in a rail transit network is critical to evaluate the network robustness and develop risk resistant plans and therefore ensure efficient operation of the transit network. This paper considers the mutual influence between nodes in the rail transit network and selects the Degree Centrality (DC), Betweenness Centrality (BC) and Closeness Centrality (CC) as comprehensive measurement indicators of node importance. The real rail transit network is converted as the corresponding topological network. The key nodes of the rail transit network are identified through the gravitational influence model, and the differences in network performance under different influencing factors are analyzed to obtain the optimal gravitational influence radius and attack strategy. The study assesses the robustness of the rail transit network from a gravitational perspective, and proposes relevant improvement recommendations. The results indicate that the importance of nodes is composed of the gravitational attraction generated by the target node and other nodes. When the gravitational influence model has a gravitational radius R=8 and a dynamic attack strategy is selected, the relative size decrease rate of the largest connected subgraph is respectively 13.25% and 10.39% higher than that when R=7 and R=9. The relative size decrease rate of network passenger flow efficiency is respectively 5.12% and 6.71% higher than that when R=7 and R=9 . Compared with the FGM, GC, KSGC, CI recognition models, the gravitational influence model has obvious advantages in identifying key nodes in rail transit networks. In addition, after attacking the top 30 nodes, the relative size of the largest connected subgraph in Beijing's subway network decreases by 91.68%, and the relative size of network passenger flow efficiency decreases by 86.17%. The results show that the gravitational influence model is applicable and effective in Beijing's subway network. The proposed method provides a new perspective for analyzing network robustness and provides an effective basis for decision makers to create network risk prevention plans.

Key words: urban traffic, key nodes, gravitational influence model, network performance, complex network

中图分类号:

U121

左忠义, 刘泽宇, 杨广川. 基于引力影响模型的轨道交通网络关键节点识别研究[J]. 交通运输系统工程与信息, 2025, 25(1): 102-112.

ZUO Zhongyi, LIU Zeyu, YANG Guangchuan. Key Node Identification of Rail Transit Network Based on Gravity Influence Model[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(1): 102-112.

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http://www.tseit.org.cn/CN/Y2025/V25/I1/102

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基于引力影响模型的轨道交通网络关键节点识别研究

Key Node Identification of Rail Transit Network Based on Gravity Influence Model

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