Control station identification of rail transport network based on loading coefficient

WANG Li-fu1 ZHU Feng1 GUO Ge1 ZHAO Guo-tao1

(1.School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, Hebei Province, China 066004)

【Abstract】Urban rail transit passenger flow is critical to the efficiency and safety of rail transit operations. This paper proposes to measure the network load with load coefficients which are taken as the edge weights of rail transit network, based on passenger flow, creating the rail transit network model. The controllability theory of complex network is used to analyze the controllability of the rail transit network, and the identification method for identifying the control nodes of rail transit network is given to realize the control of finite-flow stations of urban rail transit network. With the Beijing subway network taken as an example, the network model with load coefficients is established, and the selection of its control nodes is analyzed. The results show that the current normalized control nodes cannot make the network fully controllable. Meanwhile, it also faces a large number of selected finite-flow stations as well as high cost. Applying load coefficients to the selection of finite-flow stations cannot only make the network fully controllable, but also obtain fewer control nodes at lower cost and the control nodes are distributed mainly on the overloaded line. The proposed method can effectively find the control nodes and provide an effective reference for the selection of the actual finite-flow stations.

【Keywords】 complex network; rail transport network; driver nodes; finite-flow station; load coefficient;

【DOI】

【Funds】 National Natural Science Foundation of China (61402088), Natural Science Foundation of Hebei Province, China (F2016501023, F2017501041) Fundamental Research Funds for the Central Universities of Ministry of Education of China (N172304030)

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This Article

ISSN:1001-0920

CN: 21-1124/TP

Vol 35, No. 10, Pages 2319-2328

October 2020

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Abstract

  • 0 Introduction
  • 1 Traffic network model and structural controllability
  • 2 Model improvement and controllability research
  • 3 Application of driver node identification algorithm in Beijing Subway Network
  • 4 Conclusions
  • References