华南理工大学学报(自然科学版) ›› 2010, Vol. 38 ›› Issue (3): 70-75,81.doi: 10.3969/j.issn.1000-565X.2010.03.013

• 交通与运输工程 • 上一篇    下一篇

城市公交网络的鲁棒性分析模型

段后利 李志恒 张毅   

  1. 清华大学 自动化系, 北京 100084
  • 收稿日期:2009-04-16 修回日期:2009-07-30 出版日期:2010-03-25 发布日期:2010-03-25
  • 通信作者: 段后利(1982-),男,博士生,主要从事智能交通系统研究. E-mail:duanhouli00@mails.tsinghua.edu.cn
  • 作者简介: 段后利(1982-),男,博士生,主要从事智能交通系统研究.
  • 基金资助:

    国家“973”计划项目(2006CB705506);国家“863”计划项目(2007AA11Z215);国家自然科学基金资助项目(60834001,60774034,60721003,50708055);北京市科学技术委员会博士生论文资助专项项目(ZZ0807)

Robustness Analysis Model of Urban Transit Networks

Duan Hou-li  Li Zhi-heng  Zhang Yi   

  1. Department of Automation, Tsinghua University, Beijing 100084, China
  • Received:2009-04-16 Revised:2009-07-30 Online:2010-03-25 Published:2010-03-25
  • Contact: 段后利(1982-),男,博士生,主要从事智能交通系统研究. E-mail:duanhouli00@mails.tsinghua.edu.cn
  • About author:段后利(1982-),男,博士生,主要从事智能交通系统研究.
  • Supported by:

    国家“973”计划项目(2006CB705506);国家“863”计划项目(2007AA11Z215);国家自然科学基金资助项目(60834001,60774034,60721003,50708055);北京市科学技术委员会博士生论文资助专项项目(ZZ0807)

摘要: 城市公交网络的鲁棒性是考核城市公交系统性能的重要指标,对其进行分析有助于更好地提出评价和优化措施.文中基于二分图模型,构建了城市公交系统的公交原始网络模型、公交站点网络模型和公交线路网络模型,定义了城市公交网络的拓扑结构鲁棒性指标,提出了针对大规模网络的鲁棒性分析的快速算法.最后以北京市公交系统为例,对3种公交网络模型在随机攻击和蓄意攻击下的鲁棒性进行了分析.结果表明:公交网络与随机网络相比,对于随机攻击方式的鲁棒性差别不大,但是对于按度数和按介数的攻击方式的鲁棒性要差于随机网络.

关键词: 城市公交网络, 鲁棒性分析, 二分图模型, 复杂网络

Abstract:

The analysis of robustness, an important performance index of urban transit networks, helps to well evaluate and optimize the measures of urban transit systems. In this paper, first, three models of urban transit systems respectively for the original transit network, the transit station network and the transit line network are established based on the bipartite graph model, and a robustness index of the topology structure of urban transit networks is de- fined. Then, a fast algorithm is proposed for the robustness analysis of large-scale networks. Finally, the robustness of the three established models is analyzed, with Beijing transit system under random and intentional attacks as an example. The results indicate that, compared with the random network, the urban transit network is not sensitive to the random attack, but somewhat sensitive to degree-based and betweenness-based attack.

Key words: urban transit network, robustness analysis, bipartite graph model, complex network

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