Location optimization of metro subway emergency rescue stations considering node importance

doi:10.16265/j.cnki.issn1003-3033.2025.11.1151

Abstract

Abstract:

In order to study the scientific and reasonable location selection of regional emergency rescue stations and base-level emergency rescue stations in the planning and construction stage of the subway network, firstly, the topology model of the subway network was constructed based on complex network theory. Relevant indicators were selected to construct the evaluation system of node importance, and GWR model was used to predict passenger flow to supplement the research data. Secondly, aiming at the highest efficiency, optimal coverage and lowest cost, the multi-objective location model of regional emergency rescue stations was established, and NSGA-Ⅱ algorithm was used to solve the optimal scheme. At the same time, a hybrid analysis calculation method was designed to solve the comprehensive importance value parameters of nodes in the model, and based on the site selection results of regional emergency rescue stations, the enumeration method was used to solve the optimal solution of base-level emergency rescue stations. Finally, Xi'an metro planning network was taken as an example to verify the calculation. The results show that compared with the optimal solution in the case of "no consideration of node importance", the regional location solution with "consideration of node importance" saves about 2.19% in cost, improves the rescue efficiency by about 9.43%, and exceeds the optimal solution with about 4.66% in coverage. Therefore, the location scheme of "considering node importance" is more in line with the actual needs of the subway network.

Key words: location of emergency rescue stations, node importance, subway system, geographic weighted regression (GWR), non-dominated sorting genetic algorithm-II (NSGA-Ⅱ)

CLC Number:

LEI Bin, LI Yujin, LI Na, LIU Wei, LUO Linlin, LEI Dongbo. Location optimization of metro subway emergency rescue stations considering node importance[J]. China Safety Science Journal, 2025, 35(11): 207-219.

Figures/Tables 18

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Table 1

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Table 3

Regression results of GWR model

变量	平均值	标准差	最小值	中位数	最大值
常量	-168 516 130.165	4 224 641 289.852	-15 825 142 686.303	1 218 361.11	11 721 158 427.216
Latitude	209 859.391	30 315 792.293	-73 392 471.835	-332 945.764	116 347 057.038
Longitude	1 486 901.707	31 376 088.717	-90 165 545.648	9 706.352	108 863 525.476
L	58 909.568	261 642.165	-206 190.932	2 378.096	1 302 629.774
WFA	-0.537	2.551	-12.708	0.005	1.170
$H'$	3 120 256.414	11 299 289.166	-15 212 345.320	118 078.390	54 420 625.406
D	27 537.578	163 450.085	-568 774.910	12 576.100	485 804.271

Table 3

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类型	属性	来源
地铁站流量数据	西安市地铁路网2023年8月21日—9月17日期间的各线路站点的每日客流量数据	西安市轨道交通集团有限公司
地铁路网结构等矢量数据	西安市地铁线路和站点的矢量数据文件	百度地图及数据网站
POI数据	西安市地铁各站点地理坐标及类别信息等	百度地图应用程序接口
其他数据	西安市地铁站点周边接驳其他交通情况	大众点评、本地宝、安居客等网站

站点类型	站点编号	站点个数
1	{1,2,3,4,…,237,238}	167
2	{13,24,29,…,220,224}	17
3	{10,12,17,…,233,239}	56

类别	地铁网络节点集合	数量	等级	描述
1	{21,22,…,123,149}	19	Ⅰ	极为重要
2	{10,12,…,225,233}	53	Ⅱ	重要
3	{11,15,…,217,239}	107	Ⅲ	较为重要
4	{1,2,…,237,238}	61	Ⅳ	普通