[1] Chen Jing, Zhang Yong, Liu Lei,. Vulnerability analysis of multimodal transport networksbased on complex network theory [J]. Journal of Southeast University (English Edition), 2021, 37 (2): 209-215. [doi:10.3969/j.issn.1003-7985.2021.02.011]

Copy

Vulnerability analysis of multimodal transport networksbased on complex network theory()

基于复杂网络理论的多式联运网络脆弱性分析

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

37

Issue:

2021 2

Page:

209-215

Research Field:

Traffic and Transportation Engineering

Publishing date:

2021-06-20

Info

Title:

Vulnerability analysis of multimodal transport networksbased on complex network theory

基于复杂网络理论的多式联运网络脆弱性分析

Author(s):

Chen Jing;  Zhang Yong;  Liu Lei

School of Transportation, Southeast University, Nanjing 211189, China

陈静;  张永;  刘磊

东南大学交通学院, 南京 211189

Keywords:

multimodal transport;  topology structure;  vulnerability;  complex network

多式联运;  拓扑结构;  抗毁性;  复杂网络

PACS:

U15

DOI:

10.3969/j.issn.1003-7985.2021.02.011

Abstract:

To explore the structural characteristics and vulnerability of multimodal transport networks, this study identifies the structural characteristics of a multimodal transport network on the basis of the complex network theory. Key nodes are clarified from the analysis of the structural characteristics. The characteristic path length and percentage of the largest subgraph are applied to analyze the vulnerability of the multimodal transport network after random and intentional attacks on the nodes. The network of a multimodal transport company is taken as an example in the empirical analysis. Results show that with more than ten nodes under a random attack, the percentage of the largest subgraph is less than 20%, and the characteristic path length is less than 2. The same performance is observed for more than seven nodes under an intentional attack. The multimodal transport network is more vulnerable under an international attack against key nodes. The results of the topology and node failure under random or intentional attacks would support the management of the multimodal transport network. Suggestions for the emergency transportation organization of enterprises under attacks are proposed accordingly. These suggestions should help improve network invulnerability and recovery from node failure.

为了研究多式联运网络的结构特性以及在网络节点失效的情况下网络的脆弱性, 基于复杂网络理论描述网络拓扑结构特性, 并明确关键节点.分析多式联运网络在节点遭遇随机攻击和刻意攻击(关键节点)后网络平均最短路径和最大连通子图的比例, 以此分析网络脆弱性.以一家多式联运企业网络为例进行实证分析.结果表明, 随机攻击下, 被攻击节点数大于10时, 最大连通子图的比例低于20%且平均最短路径小于2, 而攻击关键节点情景下, 失效节点数大于7就已达到同等效果.关键节点失效时网络性能下降速度明显大于随机节点失效情景, 保障关键节点运作能力能有效降低网络脆弱性.该方法可帮助多式联运企业确定关键节点并制定节点失效应急管理方案, 提高企业网络从节点失效中恢复的能力.

References:

[1] Zhang D, He R, Li S, et al. A multimodal logistics service network design with time windows and environmental concerns[J].PLoS One, 2017, 12(9): e0185001. DOI:10.1371/journal.pone.0185001.
[2] Tsao Y C, Linh V T. Seaport-dry port network design considering multimodal transport and carbon emissions[J]. Journal of Cleaner Production, 2018, 199: 481-492. DOI:10.1016/j.jclepro.2018.07.137.
[3] Jiang J H, Zhang D Z, Meng Q, et al. Regional multimodal logistics network design considering demand uncertainty and CO₂ emission reduction target: A system-optimization approach[J]. Journal of Cleaner Production, 2020, 248: 119304. DOI:10.1016/j.jclepro.2019.119304.
[4] Resat H G, Turkay M. A bi-objective model for design and analysis of sustainable intermodal transportation systems: A case study of Turkey[J]. International Journal of Production Research, 2019, 57(19): 6146-6161. DOI:10.1080/00207543.2019.1587187.
[5] Fazayeli S, Eydi A, Kamalabadi I N. Location-routing problem in multimodal transportation network with time windows and fuzzy demands: Presenting a two-part genetic algorithm[J]. Computers & Industrial Engineering, 2018, 119: 233-246. DOI:10.1016/j.cie.2018.03.041.
[6] Wang Q Z, Chen J M, Tseng M L, et al.Modelling green multimodal transport route performance with witness simulation software[J]. Journal of Cleaner Production, 2020, 248: 119245. DOI:10.1016/j.jclepro.2019.119245.
[7] Li J, Yang B, Zhu X L. Path optimization of green multimodal transportation under mixed uncertainties[J].Journal of Transportation Systems Engineering and Information Technology, 2019, 19(4): 13-19, 27. DOI:10.16097/j.cnki.1009-6744.2019.04.003. (in Chinese)
[8] Zhao Y R, Yang Z Z, Haralambides H. Optimizing the transport of export containers along China’s coronary artery: The Yangtze River[J]. Journal of Transport Geography, 2019, 77: 11-25. DOI:10.1016/j.jtrangeo.2019.04.005.
[9] Saeedi H, Wiegmans B, Behdani B, et al. European intermodal freight transport network: Market structure analysis[J]. Journal of Transport Geography, 2017, 60: 141-154. DOI:10.1016/j.jtrangeo.2017.03.002.
[10] Sitek P, Wikarek J. Cost optimization of supply chain with multimodal transport[C]//2012 Federated Conference on Computer Science and Information Systems(FedCSIS). Wroclaw, Poland, 2012: 1111-1118.
[11] Saeedi H, Behdani B, Wiegmans B, et al. Assessing the technical efficiency of intermodal freight transport chains using a modified network DEA approach[J]. Transportation Research Part E: Logistics and Transportation Review, 2019, 126: 66-86. DOI:10.1016/j.tre.2019.04.003.
[12] Wang Z Y, Hill D J, Chen G, et al. Power system cascading risk assessment based on complex network theory[J].Physica A: Statistical Mechanics and Its Applications, 2017, 482: 532-543. DOI:10.1016/j.physa.2017.04.031.
[13] Wang S G, Zheng L L, Yu D X. The improved degree of urban road traffic network: A case study of Xiamen, China[J]. Physica A: Statistical Mechanics and Its Applications, 2017, 469: 256-264. DOI:10.1016/j.physa.2016.11.090.
[14] Porta S, Crucitti P, Latora V. The network analysis of urban streets: A dual approach[J]. Physica A: Statistical Mechanics and Its Applications, 2006, 369(2): 853-866. DOI:10.1016/j.physa.2005.12.063.
[15] Bellingeri M, Bevacqua D, Scotognella F, et al. Efficacy of local attack strategies on the Beijing road complex weighted network[J]. Physica A: Statistical Mechanics and Its Applications, 2018, 510: 316-328. DOI:10.1016/j.physa.2018.06.127.
[16] Motter A E, Lai Y C. Cascade-based attacks on complex networks[J]. Physical Review E, 2002, 66(6): 065102. DOI:10.1103/physreve.66.065102.
[17] Wang Y C, Xiao R B. An ant colony based resilience approach to cascading failures in cluster supply network[J].Physica A: Statistical Mechanics and Its Applications, 2016, 462: 150-166. DOI:10.1016/j.physa.2016.06.058.
[18] Crucitti P, Latora V, Porta S. Centrality measures in spatial networks of urban streets[J]. Physical Review E, 2006, 73(3): 036125. DOI:10.1103/physreve.73.036125.
[19] Babaei M, Ghassemieh H, Jalili M. Cascading failure tolerance of modular small-world networks[J]. IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, 2011, 58(8): 527-531. DOI:10.1109/TCSII.2011.2158718.
[20] Lü C C, Si S B, Duan D L, et al. Dynamical robustness of networks against multi-node attacked[J]. Physica A: Statistical Mechanics and Its Applications, 2017, 471: 837-844. DOI:10.1016/j.physa.2016.12.066.
[21] Wang S, Liu J. Designing comprehensively robust networks against intentional attacks and cascading failures[J].Information Sciences, 2019, 478: 125-140. DOI:10.1016/j.ins.2018.11.005.
[22] Wang Q Z, Chen J M, Tseng M L, et al.Modelling green multimodal transport route performance with witness simulation software[J]. Journal of Cleaner Production, 2020, 248: 119245. DOI:10.1016/j.jclepro.2019.119245.

Memo

Memo:

Biographies: Chen Jing(1990—), female, Ph.D. candidate; Zhang Yong(corresponding author), male, doctor, professor, zhangyong@seu.edu.cn.
Foundation item: The Science and Technology Demonstration Project of Multimodal Freight Transport in Jiangsu Province(No.2018Y02).
Citation: Chen Jing, Zhang Yong, Liu Lei. Vulnerability analysis of multimodal transport networks based on complex network theory[J].Journal of Southeast University(English Edition), 2021, 37(2):209-215.DOI:10.3969/j.issn.1003-7985.2021.02.011.

Common functions