Identification of key nodes in urban rail transit based on LightGBM and TOPSIS method
LI Kun
LIU Jie
GUO Jianmin
SHEN Yongsheng
WANG Zhe
Abstract：In order to improve the accuracy of identifying key nodes in urban rail transit networks,a city rail transit key node identification model is proposed which combines the light gradient boosting machine(LightGBM)machine learning algorithm and the technique for order preference by similarity to ideal solution(TOPSIS)method based on complex network theory and traffic network performance characteristics,considering the urban vitality information of rail transit stations.Taking Hangzhou urban rail transit network as an example,the identified top 15 key nodes are subjected to dynamic attacks,and the accuracy of the model is verified by comparing the network efficiency and the proportion of the largest connected subgraph before and after the removal of key nodes.The results show that after removing the top 5 key nodes,the network efficiency and the proportion of the largest connected subgraph are 46.89%and 56.47%,respectively,which to some extent disrupt the network structure of rail transit.When the top 15 key nodes are removed,the network efficiency and the proportion of the largest connected subgraph decrease to 25.61%and 16.6%,respectively,which indicates that the rail transit network is almost completely disrupted.The city rail transit key node model based on LightGBM and TOPSIS can effectively identify key nodes in traffic networks with high accuracy.
Keywords：urban rail transitkey nodeLightGBMTOPSIS
Publication Date：2023-12-30
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：10( 33-42 )