灾害时空属性下高铁时序网络韧性分析

doi:10.19665/j.issn1001-2400.2022.04.018

Abstract

Abstract:

Evaluating the resilience of the high-speed rail temporal network is of great significance for analyzing the high-speed rail system structure and dispatching the operation of high-speed rail trains.This paper aims at the temporal and spatial attributes of disaster factors that are not well considered in the existing resilience studies of complex transportation networks,in which a new model of the analysis of the resilience of the high-speed rail temporal network is proposed based on the differences between nodes in the time and space dimensions of the network being attacked by different disasters.First,high-speed rail temporal network model is built based on the time sequence of the high-speed rail passing through stations and the spatial relationship between the stations.The resilience index is introduced to evaluate the resilience of the high-speed rail temporal and spatial network under normal conditions.Then,according to the entropy weight method,the weight of different disasters is determined,and an evaluation model for the resilience of the high-speed railway temporal network under multiple disasters is constructed,the resilience of high-speed rail temporal networks is evaluated under disaster situations through resilience indicators.The simulation experiment selects the operation data of China's high-speed rail and the historical data of the past ten years of disasters such as rainstorms and earthquakes,and compares the resilience evaluation model in this paper with two commonly used complex network resilience evaluation models.Experimental results show that compared with the traditional resilience assessment model,the final resilience loss ratio of the resilience assessment model in this paper is reduced by 0.61%,which is more in line with the process of the actual high-speed rail network affected by disasters,indicating that the model have a good theoretical value and practicality.

Key words: complex networks, resilience, temporal and spatial attributes, entropy weight method, heterogeneous disaster

CLC Number:

U293

WANG Qiuling,KE Yuhao,GAO Yimin,CHENG Cheng,WANG Yuhang,LIN Jixiang. Analysis of resilience of the high-speed rail temporal network under disaster temporal and spatial attributes[J].Journal of Xidian University, 2022, 49(4): 156-166.

Figures/Tables 9

References 31

[1]	中共中央, 国务院. 交通强国建设纲要[EB/OL]. [2019-09-19]. http://www.gov.cn/gongbao/content/2019/content_5437132.htm.
[2]	中共中央, 国务院. 国家综合立体交通网规划纲要[EB/OL]. [2021-02-25]. http://www.gov.cn/xinwen/2021-02/24/content_5588654.htm.
[3]	HEUERMANN D F, SCHMIEDER J F. The Effect of Infrastructure on Worker Mobility:Evidence from High-Speed Rail Expansion in Germany[J]. Journal of Economic Geography, 2019, 19(2):335-372. doi: 10.1093/jeg/lby019
[4]	马超群, 张爽, 陈权, 等. 客流特征视角下的轨道交通网络特征及其脆弱性[J]. 交通运输工程学报, 2020, 20(5):208-216.
	MA Chaoqun, ZHANG Shuang, CHEN Quan, et al. Characteristics and Vulnerability of Rail Transit Network Besed on Perspective of Passenger Flow Characteristics[J]. Journal of Traffic and Transportation Engineering, 2020, 20(5):208-216.
[5]	朱岩, 孙健, 王兴举. 基于耦合映像格子模型的城市轨道交通网络可靠性研究[J]. 上海理工大学学报, 2021, 43(1):93-101.
	ZHU Yan, SUN Jian, WANG Xingju. Reliability Analysis of Urban Rail Transit System Network Based on Coupled Map Lattice Model[J]. Journal of University of Shanghai for Science and Technology, 2021, 43(1):93-101.
[6]	ALBERT R, JEONG H, BARABASI A L. Attack and Error Tolerance of Complex Networks[J]. Nature, 2000, 406:378-382. doi: 10.1038/35019019
[7]	余昌仁, 贾连兴, 张斌. 信息流介数分布熵测度的C4ISR网络抗毁性[J/OL]. [2022-04-12]. http://kns.cnki.net/kcms/detail/61.1076.TN.20211026.1037.002.html.
	YU Changren, JIA Lianxing, ZHANG Bin. Invulnerability Measurement for the C4ISR Network Based on Information Flow Betweenness Centrality Distribution Entropy[J/OL]. [2021-11-24]. http://kns.cnki.net/kcms/detail/61.1076.TN.20211026.1037.002.html.
[8]	王秋玲, 朱璋元, 陈红, 等. 基于CML的级联失效下异质多层交通网络节点抵抗特性研究[J]. 中国公路学报, 2022, 35(1):263-274. doi: 10.19721/j.cnki.1001-7372.2022.01.023
	WANG Qiuling, ZHU Zhangyuan, CHEN Hong, et al. Node Resistance Characteristic of Heterogeneous Multi-layer Transportation Networks Under Cascading Failure Based on Coupled Map Lattice[J]. China Journal of Highway and Transport, 2022, 35(1):263-274. doi: 10.19721/j.cnki.1001-7372.2022.01.023
[9]	慕彩红, 柴文壹, 刘逸, 等. 一种改进的网络鲁棒性与有效性增强方法[J]. 西安电子科技大学学报, 2018, 45(4):6-11.
	MU Caihong, CHAI Wenyi, LIU Yi, et al. Improved Approach to Enhancing the Robustness and Effectiveness of Networks[J]. Journal of Xidian University, 2018, 45(4):6-11.
[10]	SUN D J, GUAN S. Measuring Vulnerability of Urban Metro Network from Line Operation Perspective[J]. Transportation Research Part A:Policy and Practice, 2016, 94:348-359. doi: 10.1016/j.tra.2016.09.024
[11]	CATS O, KOPPENOL G, WAMIER M. Robustness Assessment of Link Capacity Reduction for Complex Networks:Application for Public Transport Systems[J]. Reliability Engineering & System Safety, 2017, 167:544-553. doi: 10.1016/j.ress.2017.07.009
[12]	LI T, RONG L. A Comprehensive Method for the Robustness Assessment of High-Speed Rail Network with Operation Data:A Case in China[J]. Transportation Research Part A:Policy and Practice, 2020, 132:666-681. doi: 10.1016/j.tra.2019.12.019
[13]	YANG Y, LIU Y, ZHOU M, et al. Robustness Assessment of Urban Rail Transit Based on Complex Network Theory:A Case Study of the Beijing Subway[J]. Safety Science, 2015, 79:149-162. doi: 10.1016/j.ssci.2015.06.006
[14]	WOHL E, SCAMARDO J E. The Resilience of Logjams to Floods[J]. Hydrological Processes, 2021, 35(1):e13970.
[15]	GU X, ZHANG Q, LI J, et al. The Changing Nature and Projection of Floods Across Australia[J]. Journal of Hydrology, 2020, 584:124703. doi: 10.1016/j.jhydrol.2020.124703
[16]	QIN J, LIU C, HUANG Q. Simulation on Fire Emergency Evacuation in Special Subway Station Based on Pathfinder[J]. Case Studies in Thermal Engineering, 2020, 21:100677. doi: 10.1016/j.csite.2020.100677
[17]	FENG X, HE S W, LI Y B. Temporal Characteristics and Reliability Analysis of Railway Transportation Networks[J]. Transportmetrica A:Transport Science, 2019, 15(2):1825-1847. doi: 10.1080/23249935.2019.1647308
[18]	郭金维, 蒲绪强, 高祥, 等. 一种改进的多目标决策指标权重计算方法[J]. 西安电子科技大学学报, 2014, 41(6):118-125.
	GUO Jinwei, PU Xuqiang, GAO Xiang, et al. Improved Method Weights Determination of Indexes Inmulti-Objective Decision[J]. Journal of Xidian University, 2014, 41(6):118-125.
[19]	WANG W, YANG S, STANLEY H E, et al. Local Floods Induce Large-scale Abrupt Failures of Road Networks[J]. Nature Communications, 2019, 10(1):1-11. doi: 10.1038/s41467-018-07882-8
[20]	PAN R K, SARAMÄKI J. Path Lengths,Correlations,and Centrality in Temporal Networks[J]. Physical Review E, 2011, 84(1):016105. doi: 10.1103/PhysRevE.84.016105
[21]	闫永泽. 公共交通系统的脆弱性评估与控制策略研究[D]. 湖北: 华中科技大学, 2017.
[22]	孔锋. 中国不同强度降雨量的多属性时序变化特征及其对ENSO的响应[J]. 长江流域资源与环境, 2020, 29(6):1387-1400.
	KONG Feng. The Characteristics of Multi-attribute Time Series of Rainfall with Different Intensities in China and Its Response to ENSO[J]. Resources and Environment in the Yangtze Basin, 2020, 29(6):1387-1400.
[23]	DHAMODHARAVADHANI S, RATHIPRIYA R. Region-Wise Rainfall Prediction UsingMapreduce-Based Exponential Smoothing Techniques[M]. Berlin:Springer, 2019:229-239.
[24]	HU Q, BIAN L, TAN M. A Data Perception Model for the Safe Operation of High-Speed Rail in Rainstorms[J]. Transportation Research Part D:Transport and Environment, 2020, 83:102326. doi: 10.1016/j.trd.2020.102326
[25]	冯玉林, 蒋丽忠, 陈梦成, 等. 地震作用下轨道-桥梁系统损伤与轨道不平顺的对应关系[J]. 交通运输工程学报, 2021, 21(3):203-214.
	FENG Yulin, JIANG Lizhong, CHEN Mengcheng, et al. Corresponding Relationship Between Track-Bridge System Damage and Track Irregularity Under Seismic Action[J]. Journal of Traffic and Transportation Engineering, 2021, 21(3):203-214.
[26]	WANG F. Temporal Pattern Analysis of Local Rainstorm Events in China During the Flood Season Based on Time Series Clustering[J]. Water, 2020, 12(3):725. doi: 10.3390/w12030725
[27]	WU Z X, HOLME P. Onion Structure and Network Robustness[J]. Physical Review E, 2011, 84(2):026106. doi: 10.1103/PhysRevE.84.026106
[28]	JIAO J, ZHANG F, LIU J. A Spatiotemporal Analysis of the Robustness of High-Speed Rail Network in China[J]. Transportation Research Part D:Transport and Environment, 2020, 89:102584. doi: 10.1016/j.trd.2020.102584
[29]	GOU H, LI W, ZHOU S, et al. Dynamic Response of High-Speed Train-Track-Bridge Coupling System Subjected to Simultaneous Wind and Rain[J]. International Journal of Structural Stability and Dynamics, 2021, 21(11):2150161. doi: 10.1142/S0219455421501613
[30]	LI T, RONG L. Spatiotemporally Complementary Effect of High-Speed Rail Network on Robustness of Aviation Network[J]. Transportation Research Part A:Policy and Practice, 2022, 155:95-114. doi: 10.1016/j.tra.2021.10.020
[31]	CROWELL B W, MELGAR D, BOCK Y, et al. Earthquake Magnitude Scaling UsingSeismogeodetic Data[J]. Geophysical Research Letters, 2013, 40(23):6089-6094. doi: 10.1002/2013GL058391

事件编号	列车编号	站点名称	站点编号	到站时间	发车时间
1	G1	北京南	59		7∶00
2	G1	天津南	740	7∶31	7∶33
?	?	?	?	?	?
48 428	G9730	衡阳东	370	18∶41	18∶43
48 429	G9730	长沙南	397	19∶26

站点编号	高铁站点	省份
1	安庆站	安徽
2	桐城东站	安徽
?	?	?
819	长寿北站	重庆
820	长寿湖站	重庆

城市名称	2011.01.01	2011.01.01	…	2020.12.31
安庆	0	2.176	…	0.048
蚌埠	0	0.001	…	0.087
?	?	?		?
温州	0	0.018	…	0
重庆	1.26	1.068	…	0.158

发震日期	经度/(°)	纬度/(°)	深度/km	震级/M	震中位置
2011-01-01 15∶31∶58	97.92	24.72	10	4.6	云南省德宏傣族景颇族自治州
2011-01-02 07∶33∶34	97.91	24.72	10	4.8	云南省德宏傣族景颇族自治州
?	?	?	?	?	?
2020-10-21 12∶04∶47	104.17	31.84	17	4.6	四川省绵阳市
2020-10-22 11∶03∶37	104.18	31.83	20	4.7	四川省绵阳市

Analysis of resilience of the high-speed rail temporal network under disaster temporal and spatial attributes

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[3]	GUO Jinwei;PU Xuqiang;GAO Xiang;ZHANG Yongan. Improved method on weights determination of indexes in multi-objective decision [J]. J4, 2014, 41(6): 118-125.
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[6]	FU Li-dong;GAO Lin. Spectral approach to finding communities in networks based on the modularity density [J]. J4, 2010, 37(5): 916-920+965.
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[8]	DU Jian-chao(1);WU Cheng-ke(1);YANG Ya-dong(1;2);XIAO Song(1). Data partitioning method based on picture content [J]. J4, 2006, 33(2): 200-204.