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基于负载系数的轨道交通网络控制站点辨识

王立夫1 朱枫1 郭戈1 赵国涛1

(1.东北大学秦皇岛分校控制工程学院, 河北秦皇岛 066004)
【知识点链接】经典控制理论; 邻接矩阵; 增广路

【摘要】城市轨道交通客流量对轨道交通运行的效率与安全至关重要.利用客流信息,提出负载系数衡量网络负载情况,并作为轨道交通网络边的权重,建立轨道交通网络模型.运用复杂网络的可控性理论分析轨道交通网络的可控性问题,给出轨道交通网络控制节点的辨识方法,实现对城市轨道交通网络限流车站的控制.以北京地铁网络为实例建立携带负载系数的网络模型,对其控制节点的选取进行分析,结果表明现行常态化控制站点不能使网络完全可控,且选择的控制站点数量较多,成本较高,应用负载系数作为权重选择的限流站点不仅能够使网络完全能控选择的控制站点数量更少,成本较低,而且更多地分布于超载线路上,同时所提出方法可以有效找出控制站点,为实际限流车站的选取提供有效的参考.

【关键词】 复杂网络;轨道交通网络;驱动节点;限流站;负载系数;

【DOI】

【基金资助】 国家自然科学基金项目(61402088); 河北省自然科学基金项目(F2016501023,F2017501041); 中央高校基本科研业务费专项基金项目项目(N2023022);

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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Article Outline

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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