| 97 | 0 | 156 |
| 下载次数 | 被引频次 | 阅读次数 |
为准确识别城市道路单向交通网络中的关键交叉口，首先基于霍尔三维结构模型衡量交叉口受到干扰前后的抵抗能力和疏散能力，构造城市单向交通网络关键交叉口初始识别指标；其次，利用融合熵权法的TOPSIS模型筛选关键交叉口识别基础指标，并利用CRITIC法构建决策矩阵；最后，提出一种利用介数中心性的改进K-shell算法识别城市道路单向交通网络关键交叉口。以哈尔滨市南岗区部分城市交通网络为例展开研究，结果表明，鞍山街与邮政街交叉口具有最大K值，为该单向交通网络的关键交叉口。随着关键交叉口的失效，路网交通饱和度大幅增大，相较于现状路网，交通饱和度升高的路段数量增加21.33%,验证了改进K-shell算法识别城市单向交通网络关键交叉口的准确性。
Abstract：To accurately identify the key intersections in the one-way traffic network of urban roads, firstly, the resistance and evacuation capabilities of the intersections before and after being disturbed are measured based on the Hall three-dimensional structure model, and the initial identification indicators of the key intersections in the one-way traffic network of the city are constructed. Secondly, the TOPSIS model integrating the entropy weight method is utilized to screen the basic indicators for identifying key intersections, and the CRITIC method is employed to construct the decision matrix. Finally, an improved K-shell algorithm using betweenness centrality is proposed to identify key intersections in the one-way traffic network of urban roads. Taking the urban traffic network of some areas in Nangang District, Harbin City as an example for research, the results show that the intersection of Anshan Street and Postal Street has the maximum K value and is the key intersection of this one-way traffic network. With the failure of key intersections, the traffic saturation of the road network has increased significantly. Compared with the current road network, the number of sections with increased traffic saturation has increased by 21.33%, verifying the accuracy of the improved K-shell algorithm in identifying key intersections of the urban one-way traffic network.
[1]XU G Q,DONG C.CAGM:A communicability-based adaptive gravity model for influential nodes identification in complex networks[J].Expert Systems with Applications,2024,235:121154.
[2]王晚香，张佳垚，郭瑞军.多种失效模式下高速公路复杂网络可靠性研究[J].大连交通大学学报，2021,42(6):1-7.WANG W X,ZHANG J Y,GUO R J.Research on reliability of freeway complex network under multiple failure modes[J].Journal of Dalian Jiaotong University,2021,42(6):1-7.
[3]CURADO M,TORTOSA L,VICENT J F.A novel measure to identify influential nodes:Return random walk gravity centrality[J].Information Sciences,2023,628:177-195.
[4]UGURLU O.Comparative analysis of centrality measures for identifying critical nodes in complex networks[J].Journal of Computational Science,2022,62:101738.
[5]ZHANG Q,SHUAI B,LYU M.A novel method to identify influential nodes in complex networks based on gravity centrality[J].Information Sciences,2022,618:98-117.
[6]HUANG X L,CHEN J,CAI M,et al.Traffic node importance evaluation based on clustering in represented transportation networks[J].IEEE Transactions on Intelligent Transportation Systems,2022,23(9):16622-16631.
[7]LIU W Y,LI X,LIU T,et al.Approximating betweenness centrality to identify key nodes in a weighted urban complex transportation network[J].Journal of Advanced Transportation,2019,2019(1):9024745.
[8]张诚，汪成银，陈志伟，等.基于空间流量度的城市路网关键节点挖掘方法[J].重庆交通大学学报(自然科学版),2021,40(6):28-35.ZHANG C,WANG C Y,CHEN Z W,et al.Mining method of key nodes of urban road network based on spatial-flow degree[J].Journal of Chongqing Jiaotong University (Natural Science),2021,40(6):28-35.
[9]ZHAO Z L,TANG L L,YANG X,et al.Identifying critical urban intersections from a fine-grained spatio-temporal perspective[J].Travel Behaviour and Society,2024,34:100649.
[10]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].Journal of Tsinghua University (Natural Science Edition),2023,63(11):1770-1780.
[11]何惠倩，张瑞秋，孙炜，等.基于霍尔三维模型的智能停车系统设计[J].广西大学学报(自然科学版),2019,44(6):1675-1682.HE H Q,ZHANG R Q,SUN W,et al.Design of intelligent parking system based on Hall 3D model[J].Journal of Guangxi University (Natural Science Edition),2019,44(6):1675-1682.
[12]卢德友，田桂林，杨泊，等.基于熵权-TOPSIS法的渠系配水模型求解算法综合评价[J].灌溉排水学报，2023,42(9):129-137.LU D Y,TIAN G L,TANG B,et al.Comprehensive evaluation of canal water distribution model solving algorithm based on entropy weight TOPSIS method[J].Journal of Irrigation and Drainage,2023,42(9):129-137.
[13]谢丽霞，孙红红，杨宏宇，等.基于K-shell的复杂网络关键节点识别方法[J].清华大学学报(自然科学版),2022,62(5):849-861.XIE L X,SUN H H,YANG H Y,et al.Key node recognition method for complex networks based on K-shell[J].Journal of Tsinghua University (Natural Science Edition),2022,62(5):849-861.
[14]GARAS A,SCHWEITZER F,HAVLIN S.A k-shell decomposition method for weighted networks[J].New Journal of Physics,2012,14(8):083030.
[15]陈妤，秦威.基于排序学习的复杂网络节点接近中心性近似排序[J].计算机系统应用，2022,31(11):387-392.CHEN Y,QIN W.Approximate ranking of nodes in complex networks based on sorting learning for approaching centrality[J].Computer Systems and Applications,2022,31(11):387-392.
[16]YAN G,ZHOU T,WANG J,et al.Epidemic spread in weighted scale-free networks[J].Chinese Physics Letters,2005,22(2):510-513.
[17]FEI L G,DENG Y.A new method to identify influential nodes based on relative entropy[J].Chaos,Solitons & Fractals,2017,104:257-267.
[18]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 node identification method based on neighborhood K-shell distribution[J].Computer Engineering and Applications,2024,60(2):87-95.
基本信息:
DOI：10.13291/j.cnki.djdxac.2025.05.004
中图分类号:U491.23
引用信息:
[1]裴玉龙,金子微.基于改进K-shell的单向交通网络关键交叉口识别[J].大连交通大学学报,2025,46(05):28-37.DOI:10.13291/j.cnki.djdxac.2025.05.004.
基金信息:
国家重点研发计划项目(2018YFB1600902); 黑龙江省重点研发计划项目(JD22A014); 中央高校基本科研业务费专项资金资助(2572023CT21-02)