Centrality Characteristics Analysis of Urban Rail Network

被引:0
|
作者
Tu Yingfei [1 ]
机构
[1] Tongji Univ, Sch Econ & Management, Shanghai 200092, Peoples R China
关键词
urban rail network; degree centrality; betweenness centrality; closeness centrality; total passenger flow; COMPLEX NETWORKS; SUBWAY; SYSTEMS; CHINA;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
There is a consensus that the developing the urban rail transit system is the prime way for solving urban traffic problems. Urban rail network has been identified as a complex network. In this paper, three kinds of centrality characteristics of an urban rail network have been analyzed. The degree-based index describes the possible travel activities that travelers can get at a station. The betweenness-based index describes the ability to control the travel activities of a station. The closeness-based index describes the independence or efficiency of a station. Three indices reflect the importance of a station from different viewpoints. Shanghai urban rail network is taken as the object to be analyzed, which consists of 12 lines and 296 stations. The analysis results about the importance of stations and the importance of lines are compared with their operational conditions. It indicates that the closeness centrality is the index most relevant to the operational condition of the line. The centrality characteristics analysis is useful for the urban transit management and operation affairs and is the basic for the network's vulnerability analysis which is important for the safety of urban rail transit system.
引用
下载
收藏
页码:286 / 291
页数:6
相关论文
共 50 条
  • [21] Research on Analysis Method of Characteristics Generation of Urban Rail Transit
    Cai, Zhi
    Li, Tong
    Su, Xing
    Guo, Limin
    Ding, Zhiming
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2020, 21 (09) : 3608 - 3620
  • [22] Research on Centrality of Urban Transport Network Nodes
    Wang, Kui
    Fu, Xiufen
    MATERIALS SCIENCE, ENERGY TECHNOLOGY, AND POWER ENGINEERING I, 2017, 1839
  • [23] Connectivity and Centrality Characteristics of the Epileptogenic Focus Using Directed Network Analysis
    Adkinson, Joshua A.
    Karumuri, Bharat
    Hutson, Timothy N.
    Liu, Rui
    Alamoudi, Omar
    Vlachos, Ioannis
    Iasemidis, Leonidas
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2019, 27 (01) : 22 - 30
  • [24] Evaluation Method for Node Importance of Urban Rail Network Considering Traffic Characteristics
    Chen, Ting
    Ma, Jianxiao
    Zhu, Zhenjun
    Guo, Xiucheng
    SUSTAINABILITY, 2023, 15 (04)
  • [25] Controllability of Urban Rail Transit Network
    Zeng, Lu
    Qin, Yong
    Liu, Jun
    Wang, Li
    PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON ELECTRICAL AND INFORMATION TECHNOLOGIES FOR RAIL TRANSPORTATION (EITRT) 2017: TRANSPORTATION, 2018, 483 : 873 - 883
  • [26] A centrality analysis of the Lightning Network
    Zabka, Philipp
    Foerster, Klaus -T.
    Decker, Christian
    Schmid, Stefan
    TELECOMMUNICATIONS POLICY, 2024, 48 (02)
  • [27] Patrol scheduling in urban rail network
    Hoong Chuin Lau
    Zhi Yuan
    Aldy Gunawan
    Annals of Operations Research, 2016, 239 : 317 - 342
  • [28] Patrol scheduling in urban rail network
    Lau, Hoong Chuin
    Yuan, Zhi
    Gunawan, Aldy
    ANNALS OF OPERATIONS RESEARCH, 2016, 239 (01) : 317 - 342
  • [29] CHARACTERISTICS AND RELIABILITY ANALYSIS OF THE COMPLEX NETWORK IN GUANGZHOU RAIL TRANSIT
    Jiang, Chaozhe
    Wu, Lu
    Xu, Fang
    Yuan, Jixue
    INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2013, 19 (02): : 217 - 225
  • [30] Characteristic analysis of basic unit and complex network for urban rail transit
    Ma, Jia-Qi
    Bai, Yan
    Han, Bao-Ming
    Jiaotong Yunshu Gongcheng Xuebao/Journal of Traffic and Transportation Engineering, 2010, 10 (04): : 65 - 70