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

参考文献

[1]DING R, ZHANG T, ZHOU T, et al. Topologic characteristics and sustainable growth of worldwide urban rail networks[J]. International Journal of Modern Physics B, 2021, 35(11): 2150151.

[2]罗艺,钱大琳.公交-地铁复合网络构建及网络特性分析[J]. 交通运输系统工程与信息,2015, 15(5): 39-44. [LUO Y, QIAN D L. Construction of subway and bus transport networks and analysis of the network topology characteristics[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(5): 39-44.]

[3]郑乐,高良鹏,陈学武,等.地铁-公交加权复合网络关键站点识别及鲁棒性研究[J].交通运输系统工程与信息, 2023, 23(5): 120-129. [ZHENG Y, GAO L P, CHEN X W, et al. Critical stations identification and robustness analysis of weighted metro-bus composite network[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(5): 120-129.]

[4]MENG Y, TIAN X, LI Z, et al. Exploring node importance evolution of weighted complex networks in urban rail transit[J]. Physica A: Statistical Mechanics and its Applications, 2020, 558(1):124925.

[5]LIU C, YIN H, SUN Y, et al. A grade identification method of critical node in urban road network based on multi-attribute evaluation correction[J]. Applied Sciences, 2022, 12(2): 813-813.

[6]王亭,张永,周明妮,等.融合网络拓扑结构特征与客流量的城市轨道交通关键节点识别研究[J].交通运输系统工程与信息,2022, 22(6): 201-211. [WANG T, ZHANG Y, ZHOU M N, et al. Identification of key nodes of urban rail transit integrating network topology characteristics and passenger flow[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 201-211.]

[7]孙小慧,刘毅,米玉梅,等.韧性视角下城市地铁与常规公交网络关键站点及线路识别[J].复杂系统与复杂性科学,2024, 5: 1-9. [SUN X H, LIU Y, MI Y M, et al. Identification of key stations and routes in urban metro and conventional bus networks from a resilience perspective[J]. Complex System and Complexity Science, 2024, 5: 1-9.]

[8]ZHE L, XINYU H. Identifying influential spreaders by gravity model considering multi-characteristics of nodes [J]. Scientific Reports, 2022, 12(1): 9879-9879.

[9]吴亚丽,任远光,董昂,等.基于邻域K-shell分布的关键节点识别方法[J]. 计算机工程与应用,2024,60(2): 87-95. [WU Y L, REN Y G, DONG A, et al. Key nodes identification method based on neighborhood K-shell distribution[J]. Computer Engineering and Applications, 2024, 60(2): 87-95.]

[10]王灏翔, 陈俊熙,卫振林,等.一种基于相对熵与邻居影响聚类的复杂网络关键节点识别新算法[J].北京交通大学学报,2024,48(2): 154-164, 175. [WANG H X, CHEN J X, WEI Z L, et al. A novel algorithm for identifying key nodes in complex networks based on relative entropy and neighbor influence clustering[J]. Journal of Beijing Jiaotong University, 2024, 48(2): 154 164, 175.]

[11]马书红, 杨磊,陈西芳,等.城市群生态综合交通网络组团特性分析与关键节点识别[J].清华大学学报(自然科学版), 2023, 63(11): 1770-1780. [MA S H, YANG L, CHEN X F, et al. Analysis of cluster characteristics and key node identification of ecological comprehensive transportation network in urban agglomeration[J]. Tsinghua Science and Technology, 2023, 63(11): 1770 1780.]

[12]冯芬玲,蔡明旭,贾俊杰.基于多层复杂网络的中欧班列运输网络关键节点识别研究[J].交通运输系统工程与信息, 2022, 22(6): 191-200. [FENG F L, CAI M X, JIA J J. Key node identification of China railway express transportation network based on multi-layer complex network[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 191-200.]

[13]JIA T, LIU W Y, LIU X T. A cross-city exploratory analysis of the robustness of bus transit networks using opensource data[J]. Physica A: Statistical Mechanics and its Applications, 2021, 580: 126133.

[14]蔡鉴明, 邓薇.长沙地铁网络复杂特性与级联失效鲁棒性分析[J]. 铁道科学与工程学报,2019,16(6):1587-1596. [CAI J M, DENG W. Complex characteristics of Changsha metro network and robustness analysis of cascading failures[J]. Journal of Railway Science and Engineering, 2019, 16(6): 1587-1596.]

[15]GUO H, WANG S, YAN X, et al. Node importance evaluation method of complex network based on the fusion gravity model[J]. Chaos, Solitons and Fractals: Applications in Science and Engineering: An Interdisciplinary Journal of Nonlinear Science, 2024, 183: 114924.

[16]MA L, MA C, ZHANG H, et al. Identifying influential spreaders in complex networks based on gravity formula [J]. Physica A: Statistical Mechanics and its Applications, 2016, 451: 205-212.

[17]XUAN Y, FUYUAN X. An improved gravity model to identify influential nodes in complex networks based on k-shell method[J]. Knowledge-Based Systems, 2021, 227: 107198.

[18]XIAN T, SEN P, FLAVIANO M, et al. Collective influence of multiple spreaders evaluated by tracing real information flow in large-scale social networks[J]. Scientific Reports, 2016, 6(1): 36043.

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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