Super-network Perspective on Studying the Method of Identifying the Important Urban Transport Hub

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Abstract To overcome the difficulty of complex network theory (CNT) to meet the requirements of integrated, comprehensive, and networked urban transport hub network system modeling. It leads to the incomplete depiction of heterogeneity characteristics of urban hub network elements and loss of internal structure characteristics of the hub, which leads to a large deviation in importance discrimination. Firstly, based on the super-network theory (SNT), the SNT model of urban transportation hub is defined and constructed. Secondly, combined with the actual structure characteristics of the hub and the characteristics of the hub SNT model, the lateral and longitudinal selection indexes of the super-edge (SE) are proposed, and based on this, the SE selection index is constructed. Then, from the perspective of the SNT, the paper introduces the method of judging the importance of urban transportation hub by using the SE selection degree. Finally, taking Beijing’s urban transportation hub as the research object, 16 transportation hubs composed of aviation, railway, long-distance passenger transport, urban public transport, and urban rail transit are selected for modeling and importance analysis. The results show that: compared with the node degree identification method of CNT modeling, the identification result of urban transportation hub importance based on the SE selection degree from the perspective of the SNT has a high degree of coincidence with the actual situation, which can deeply mine the topological structure features that are homogenized due to CNT modeling, and reveal that a single hub has multiple transportation modes and multiple links between hubs The functional characteristics of two modes of transportation connection. In terms of performance law, the fitting results of importance and selectivity from the perspective of SNT show the significant heavy-tailed distribution and power-law distribution, which are in line with the law of network evolution and development. It is of high theoretical and practical value to verify the importance discrimination method of selectivity as a measure from the perspective of SNT.

Key words： urban traffic urban hub super network importance discrimination selectivity

Received: 01 April 2021

Fund:Supported by the Project of Beijing Municipal Education Commission Science and Technology Program General Project (No. KM202110005002); the Basic Research Foundation Project of Beijing University of Technology (No.038000546319519); the Science and Technology Plan Projects of Beijing Municipal Commission of Transport (No. 11000022210200021338- XM001)

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	YUAN Guang
	SUN Li-shan
	KONG De-wen
	BAI Zi-xi
	SHAO Juan

Cite this article:

YUAN Guang,SUN Li-shan,KONG De-wen, et al. Super-network Perspective on Studying the Method of Identifying the Important Urban Transport Hub[J]. Journal of Highway and Transportation Research and Development, 2023, 17(1): 82-91.

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http://journal10.magtechjournal.com/Jwk_gljtkj_en/EN/ OR http://journal10.magtechjournal.com/Jwk_gljtkj_en/EN/Y2023/V17/I1/82

[1] PRYOR K O, SLEIGH J. The Seven Bridges of Konigsberg[J]. Anesthesiology. 2011, 114(4): 739-740.
[2] BARABASI A L, ALBERT R. Emergence of Scaling in Random Networks[J]. SCIENCE. 1999, 286(5439): 509-512.
[3] ERDOS P, RENYI A R. On Random Graph I[J]. Publicationes Mathematicae. 1959, 6:290-297.
[4] LI J, WAng J, XU N, et al. Importance Degree Research of Safety Risk Management Processes of Urban Rail Transit Based on Text Mining Method[J]. Information. 2018, 9(2): 26.
[5] IVAN P G, JULIA O, OLIVER S, et al. Mechanisms of Reciprocity and Diversity in Social Networks: A Modeling and Comparative Approach[J]. Behavioral Ecology. 2018,29(3): 745-760.
[6] WANG S, ZHANG J, XIN Y. Multiple Robustness Assessment Method for Understanding Structural and Functional Characteristics of the Power Network[J]. Physica A: Statistical Mechanics and Its Applications. 2018, 510: 261-270.
[7] PAGANI G A, AIELLO M. Power Grid Complex Network Evolutions for the Smart Grid[J]. Physica A: Statistical Mechanics and Its Applications. 2014, 396:248-266.
[8] LI Jin, MA Jun-hai. Research of Complexity of Urban Subway Network[J] Journal of Xidian University: Social Science Edition. 2009, 19(2): 51-55. (in Chinese)
[9] YAO Hui-yun. Research of Urban Traffic Network Complexity Measurement[D]. Nanjing: Southeast University, 2017. (in Chinese)
[10] LI Bo, CHEN Jin-shan. Complexity Research of Urban and Rural Passenger Transportation Network Based on the Scale-free Network[J]. Journal of Chongqing Jiaotong University: Natural Sciences. 2012, 12(1): 25-27. (in Chinese)
[11] SHENG J, DAI J, WANG B, et al. Identifying Influential Nodes in Complex Networks Based on Global and Local Structure[J]. Physica A: Statistical Mechanics and Its Applications. 2020, 541: 123262.
[12] KUMAR S, PANDA B S. Identifying Influential Nodes in Social Networks: Neighborhood Coreness Based Voting Approach[J]. Physica A: Statistical Mechanics and Its Applications. 2020, 553: 124215.
[13] WU Tong, WEI Xing-pei. Importance Analysis of Urban Rail Transit Station Based on Complex Network[C]// 2019 Annual Meeting of China Urban Transport Planning. Chengdu: Urban Planning Society of China, 2019: 3372-3381. (in Chinese)
[14] ZHANG Ting-ping. Research on Node Importance Evaluation in Complex Networks[D]. Chongqing: Chongqing University, 2017. (in Chinese)
[15] REN Xiao-long, LV Lin-yuan. Review of Ranking Nodes in Complex Networks[J]. Chinese Science Bulletin, 2014, 59(13): 1175-1197. (in Chinese)
[16] SPORNS O, HONEY C J, KOTTER R. Identification and Classification of Hubs in Brain Networks[J]. Plos One. 2007, 2: e104910.
[17] ZHANG X, WU J P, DU Y M. The Study of Critical Nodes of Road Network Based on Traffic Shockwave Model[C]// 2012 7th International Conference on Computing and Convergence Technology. Seoul: IEEE, 2012.
[18] XUE F, HE C L, SUN Z S, et al. Key Node Identification Method of Chengdu Metro Network Based on Comprehensive Assessment[C]// International Conference on Smart Vehicular Technology, Transportation, Communication and Applications. Berlin: Springer, 2018: 48-58.
[19] MCCORD M R. Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods[J]. Transportation Research Part A: General, 1987, 21(6): 481-484.
[20] HU Feng, ZHAO Hai-xing, MA Xiu-juan. An Evolving Hypernetwork Model and Its Properties[J]. Scientia Sinica: Physica, Mechanica & Astronomica, 2013, 43(1): 16-22. (in Chinese)
[21] YUAN G, KONG D W, SUN L S, et al. Connectivity Contribution to Urban Hub Network Based on Super Network Theory: Case Study of Beijing[J]. Promet-traffic & Transportation, 2021, 33(1): 35-47.
[22] ESTRADA E, RODRÍGUEZ-VELÁZQUEZ J A. Subgraph Centrality and Clustering in Complex Hyper-networks[J]. Physica A: Statistical Mechanics and Its Applications. 2006, 364: 581-594.
[23] MA N, LIU Y. SuperedgeRank Algorithm and Its Application in Identifying Opinion Leader of Online Public Opinion Supernetwork[J]. Expert Systems with Applications. 2014, 41(4): 1357-1368.
[24] YUAN G, SUN L S, KONG D W, et al. Which Characteristics Do Indexes Represent in Urban Hub Super-network: a Case Study in Beijing[C] // 2020 IEEE 23rd International Conference on Intelligent Transportation Systems. Rhodes, Greece: IEEE, 2020.
[25] LIANG Xiao-he. Research on Topic Detection of Microblog Public Opinion Based on Super Network Analysis[D]. Beijing: Chinese Academy of Agricultural Sciences, 2019. (in Chinese)
[26] QIAO Ya-feng, Identifying Opinion Leader Nodes in Social Networks Based on the Closeness and Application[D]. Xi’an: Xidian University, 2015. (in Chinese)
[27] LI Mei-zhu. The Study of Information Fusion in Identification of Influent Nodes in Multilayer Network[D]. Chongqing: Southwest University, 2016. (in Chinese)
[28] MENG Meng. Traffic Assignment Model and Algorithm with Combined Modes on Urban Transportation Network[D]. Beijing: Beijing Jiaotong University, 2013. (in Chinese)
[29] HUANG Jia-zeng. Node Importance Evaluation Method Based on the Heterogeneity of Complex Networks[J]. Computer Engineering and Software, 2017, 38(4): 77-84. (in Chinese)
[30] TAN Yue-jin, WU Jun. Network Structure Entropy and Its Application to Scale-free Networks[J]. System Engineering-Theory and Practice, 2004, 24(6): 1-3. (in Chinese)
[31] Xu Congjun. Research on Bus Station Design in the Perspective of Urban Comprehensive Transport Hub[D]. Harbin: Harbin Institute of Technology, 2018. (in Chinese)