Key node recognition of highway network in high altitude mountainous area based on improved LeaderRank algorithm

doi:10.16265/j.cnki.issn1003-3033.2025.05.1144

Abstract

Abstract:

To improve the stability and safety of highway networks in plateau mountainous areas, an evaluation index system for critical nodes was established by selecting five indicators: degree centrality, betweenness centrality, closeness centrality, travel time weight, and adjacent node travel time degree. A critical node ide.pngication method integrating topological structure and traffic functionality was proposed. Taking the highway network in western Sichuan Plateau mountainous region as a case study, the differential performance of three improved algorithms was investigated. This algorithms included modified LeaderRank, PageRank, and degree centrality. Their performance was examined in ide.pngying critical nodes within an undirected weighted network framework. Additionally, the variations in network efficiency and connectivity rate under node failure scenarios were systematically examined. The results demonstrate that the three algorithms exhibit distinct prioritization in assessing node criticality. Node failure sequences ranked by the modified LeaderRank algorithm induce the most rapid and significant decline in both network efficiency and connectivity rate.

Key words: improved LeaderRank, highway in plateau mountain area, network key node, node importance, complex network

CLC Number:

HE Yunyong, HE Enhuai, CHEN Zhiyu, GAO Jianping, ZHANG Le, SUN Lu. Key node recognition of highway network in high altitude mountainous area based on improved LeaderRank algorithm[J]. China Safety Science Journal, 2025, 35(5): 229-236.

Figures/Tables 12

Fig.1

Table 1

Table 2

Fig.2

Table 3

Table 4

Table 5

Table 6

Table 7

Table 8

Fig.3

Fig.4

References 16

[1]	刘瑞, 叶堃晖, 张正丰. 复杂网络视角下我国高速公路演变特征研究[J]. 项目管理技术, 2020, 18(10): 26-33.
	LIU Rui, YE Kunhui, ZHANG Zhengfeng. Research on the evolution characteristics of China's highways from the perspective of complex networks[J]. Project Management Technology, 2023, 18(10): 26-33.
[2]	崔杨, 曾俊伟, 钱勇生, 等. 基于复杂网络的西部地区公路网可靠性研究[J]. 公路工程, 2018, 43(3): 46-51.
	CUI Yang, ZENG Junwei, QIAN Yongsheng, et al. Reliability research of highway network in northwest China based on complex network theory[J]. Highway Engineering, 2018, 43(3): 46-51.
[3]	代洪娜, 李炜. 考虑流量加权的高速公路网脆弱性研究[J]. 公路, 2019, 64(5): 181-187.
	DAI Hongna, LI Wei. Study of expressway network vulnerability considering on traffic flow[J]. Highway, 2019, 64(5): 181-187.
[4]	林培群, 刘子豪, 闫明月. 基于多元加权中心特征的高速公路网韧性研究[J]. 重庆交通大学学报:自然科学版, 2023, 42(10): 122-131.
	LIN Peiqun, LIU Zihao, YAN Mingyue. Resilience of highway network based on multiple weighted center characteristics[J]. Journal of Chongqing Jiaotong University :Natural Science, 2023, 42(10):122-131.
[5]	吴丽娜, 吕岩峰, 慈玉生. 省域高速公路网络连通可靠性:黑龙江省案例分析[J]. 交通运输系统工程与信息, 2018, 18(增1): 134-140.
	WU Li'na, LYU Yanfeng, CI Yusheng. Connectivity Reliability for provincial freeway network: a case study in Heilongjiang, China[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(S1): 134-140.
[6]	AKBARZADEH M, MEMARMONTAZERIN S, DERRIBLE S, et al. The role of travel demand and network centrality on the connectivity and resilience of an urban street system[J]. Transportation, 2019, 46(5): 1127-1141.
[7]	王灵丽, 黄敏, 高亮. 基于聚类算法的交通网络节点重要性评价方法研究[J]. 交通信息与安全, 2020, 38(2): 80-88.
	WANG Lingli, HUANG Min, GAO Liang. Methods of importance evaluation of traffic network node based on clustering algorithms[J]. Journal of Transport Information and Safety, 2020, 38(2): 80-88.
[8]	张贵兰. 基于节点综合重要度的城市道路交通网络相继拥堵疏散路径规划[D]. 长春: 吉林大学, 2022.
	ZHANG Guilan. Cascading failure evacuation path planning of urban road traffic network based on comprehensive importance of nodes[D]. Changchun: Jilin University, 2022.
[9]	冯慧芳, 柏凤山, 徐有基. 基于轨迹大数据的城市交通感知和路网关键节点识别[J]. 交通运输系统工程与信息, 2018, 18(3): 42-47, 54.
	FENG Huifang, BAI Fengshan, XU Youji. Urban traffic perception and critical node ide.pngication of road network based on trajectory big data[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(3): 42-47,54.
[10]	李卓, 何瑞春, 李文霞. 考虑时空动态特征的高速铁路网络鲁棒性评估[J]. 中国安全科学学报, 2024, 34(4): 111-120. doi: 10.16265/j.cnki.issn1003-3033.2024.04.0648
	LI Zhuo, HE Ruichun, LI Wenxia. Robustness evaluation for high-speed railway network with spatiotemporal dynamic characteristics[J]. China Safety Science Journal, 2024, 34(4): 111-120. doi: 10.16265/j.cnki.issn1003-3033.2024.04.0648
[11]	徐云帆. 基于复杂网络理论的公路网脆弱性识别方法研究[D]. 济南: 山东交通学院, 2023.
	XU Yunfan. The ide.pngication method research of road network's vulnerability based on complex network theory[D]. Ji'nan: Shandong Jiaotong University, 2023.
[12]	李倩, 张飞涟. 模糊可拓工程优选模型的城市轨道交通路网方案评价方法[J]. 中国铁道科学, 2009, 30(6): 126-131.
	LI Qian, ZHANG Feilian. Evaluation methodology of urban mass transit network project option using the fuzzy expandable engineering optimization model[J]. China Railway Science, 2009, 30(6): 126-131.
[13]	朱克毓, 董庆兴, 梁昌勇, 等. 一类三角模糊层次分析法的无效性分析[J]. 系统工程理论与实践, 2015, 35(8): 2104-2112. doi: 10.12011/1000-6788(2015)8-2104
	ZHU Keyu, DONG Qingxing, LIANG Changyong, et al. The analysis of the invalidity of one kind triangular fuzzy AHP[J]. Systems Engineering-Theoryj & Practice, 2015, 35(8): 2104-2112.
[14]	魏业文, 吴希韬, 聂俊波, 等. 基于博弈论-改进TOPSIS 的电能质量综合评价[J]. 电子测量技术, 2021, 44(16): 50-56.
	WEI Yewen, WU Xitao, NIE Junbo, et al. Comprehensive evaluation of power quality based on BWM-CRITIC-TOPSIS method[J]. Electronic Measurement Technology, 2021, 44(16): 50-56.
[15]	王铁旦, 穆毅强, 彭定洪. HAZOP风险评级的变权犹豫模糊方法[J]. 中国安全科学学报, 2022, 32(5): 61-67. doi: 10.16265/j.cnki.issn1003-3033.2022.05.1335
	WANG Tiedan, MU Yiqiang, PENG Dinghong. Hesitant fuzzy method with variable weight for HAZOP risk sorting[J]. China Safety Seience Journal, 2022, 32(5): 61-67.
[16]	魏震波, 鞠啟, 易刚春, 等. 基于改进LeaderRank算法的电网连锁故障关键线路辨识方法[J]. 高电压技术, 2021, 47(12): 4265-4273.
	WEI Zhenbo, JU Qi, YI Gangchun, et al. Ide.pngication of critical lines of power grid cascading failures based on modified LeaderRank algorithm[J]. High Voltage Engineering, 2021, 47(12): 4265-4273.

序号	名称	序号	名称	序号	名称
1	都江堰	6	汶川	11	朴头
2	龙池	7	克枯	12	古尔沟
3	映秀	8	桃坪	13	米亚罗
4	银杏	9	薛城	14	尽头寨
5	绵虒	10	理县	15	梭磨

序号	节点名称	度中心性	中介中心性	接近中心性	通行时间权值	邻接节点通行时间度
1	都江堰	3	0.089 9	0.044 6	25	36.111 1
2	龙池	2	0.097 3	0.046 5	22	36.317 5
3	映秀	4	0.121 0	0.048 5	38	57.000 0
4	银杏	2	0.127 4	0.050 6	20	32.857 1
5	绵虒	2	0.134 8	0.052 9	18	29.387 8

准则层	准则层权重	指标层	指标层权重	主观权重
拓扑结构指标	0.5	度中心性	0.625	0.313
		中介中心性	0.136	0.068
		接近中心性	0.238	0.119
交通属性指标	0.5	行程时间权值	0.302	0.151
交通属性指标	0.5	邻接节点行程时间度	0.698	0.349

评价指标	度中心性	中介中心性	接近中心性	行程时间权值	邻接节点行程时间度
波动性	0.128	0.241	0.258	0.167	0.177
冲突性	1.680	1.556	2.590	1.369	1.291
客观权重	0.188	0.173	0.325	0.160	0.154

排序	节点序号	节点名称	排序	节点序号	节点名称
1	24	康定	6	3	映秀
2	17	雅安	7	76	茂县
3	35	冕宁	8	93	理塘县
4	1	都江堰	9	14	尽头寨
5	39	西昌	10	18	多功