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基于复杂网络的封闭小区交通开放策略探究

陈伟哲 李乡儒

陈伟哲, 李乡儒. 基于复杂网络的封闭小区交通开放策略探究. 自动化学报, 2018, 44(11): 2068-2082. doi: 10.16383/j.aas.2017.c160819
引用本文: 陈伟哲, 李乡儒. 基于复杂网络的封闭小区交通开放策略探究. 自动化学报, 2018, 44(11): 2068-2082. doi: 10.16383/j.aas.2017.c160819
CHEN Wei-Zhe, LI Xiang-Ru. Research on Traffic Opening Strategy of Closed Communities by Complex Network. ACTA AUTOMATICA SINICA, 2018, 44(11): 2068-2082. doi: 10.16383/j.aas.2017.c160819
Citation: CHEN Wei-Zhe, LI Xiang-Ru. Research on Traffic Opening Strategy of Closed Communities by Complex Network. ACTA AUTOMATICA SINICA, 2018, 44(11): 2068-2082. doi: 10.16383/j.aas.2017.c160819

基于复杂网络的封闭小区交通开放策略探究

doi: 10.16383/j.aas.2017.c160819
基金项目: 

国家自然科学基金 61273248

广东省自然科学基金 2014A030313425

国家自然科学基金 61075033

广东省自然科学基金 S2011010003348

详细信息
    作者简介:

    陈伟哲  美国印第安纳大学博士研究生.2018年在华南师范大学数学科学学院获得学士学位.主要研究方向为数据挖掘与计算机视觉.E-mail:cosmo_chan@126.com

    通讯作者:

    李乡儒  华南师范大学教授.2006年获得中国科学院自动化研究所博士学位.主要研究方向为数据挖掘与计算机视觉.本文通信作者.E-mail:xiangru.li@gmail.com

Research on Traffic Opening Strategy of Closed Communities by Complex Network

Funds: 

National Natural Science Foundation of China 61273248

Natural Science Foundation of Guangdong Province 2014A030313425

National Natural Science Foundation of China 61075033

Natural Science Foundation of Guangdong Province S2011010003348

More Information
    Author Bio:

     Ph. D. candidate at Indiana University, USA. He received his bachelor degree from the Department of Mathematical Science, South China Normal University in 2018. His research interest covers data mining and computer vision

    Corresponding author: LI Xiang-Ru  Professor at South China Normal University. He received his Ph. D. degree from the Institute of Automation, Chinese Academy of Sciences in 2006. His research interest covers data mining and computer vision. Corresponding author of this paper
  • 摘要: 封闭小区降低了城市路网密度和可达性,如何开放封闭小区成为一个热点问题.本文对若干中国大型城市的道路网络进行实证研究,发现这些网络的度分布具有无标度性质.基于此性质提出了一个适用于封闭小区开放问题的城市道路复杂网络模型.该模型使得城市道路网络包含小区路网,且道路信息易于查询与筛选,这有利于制定并比较不同的小区开放策略.利用该模型,研究了网络效率的计算和相继故障过程的建模问题,以比较不同的开放策略对城市路网的影响.最后,针对效率和稳定性两个因子,分析城市道路网络的优化特征.结果表明,开放位于非中心地区的小区,开放程度最大,新开交叉口位于连接数不多的道路时,网络效率及稳定性最佳.
    1)  本文责任编委 董海荣
  • 图  1  原始法

    Fig.  1  Primal approach

    图  2  对偶法

    Fig.  2  Dual approach

    图  3  北京道路网络示意图

    Fig.  3  Road network of Beijing

    图  4  上海道路网络示意图

    Fig.  4  Road network of Shanghai

    图  5  广州道路网络示意图

    Fig.  5  Road network of Guangzhou

    图  6  深圳道路网络示意图

    Fig.  6  Road network of Shenzhen

    图  7  北京道路网络的度分布

    Fig.  7  Degree distribution of Beijing road network

    图  8  上海道路网络的度分布

    Fig.  8  Degree distribution of Shanghai road network

    图  9  广州道路网络的度分布

    Fig.  9  Degree distribution of Guangzhou road network

    图  10  深圳道路网络的度分布

    Fig.  10  Degree distribution of Shenzhen road network

    图  11  道路网络图.其中, 黑色线段代表已命名路段, 灰色线段代表未命名路段

    Fig.  11  The road network where black lines represent the named roads, gray lines symbolize the unnamed roads

    图  12  1 000个节点的市政道路网络度分布

    Fig.  12  The degree distribution of municipal road network with 1 000 nodes

    图  13  不同开放策略优化后的城市道路网络效率对比

    Fig.  13  The comparison of the efficiency of urban road networks optimized by different opening strategies

    图  14  随机攻击策略下网络相继故障过程

    Fig.  14  The cascade failure of network under random removal

    图  15  不同开放策略优化后城市道路网络的相继故障过程

    Fig.  15  The cascade failure of rural road networks optimized by difierent opening strategies

    图  16  多次实验结果的均值收敛到某一值

    Fig.  16  The mean of the experimental results converges to a certain value

    表  1  不同的小区开放策略

    Table  1  Difierent community opening strategies

    策略编号小区位置小区开放程度交叉口位置
    A中心地段完全开放繁忙交叉口
    B中心地段完全开放清闲交叉口
    C中心地段部分开放繁忙交叉口
    D中心地段部分开放清闲交叉口
    E非中心地段完全开放繁忙交叉口
    F非中心地段完全开放清闲交叉口
    G非中心地段部分开放繁忙交叉口
    H非中心地段部分开放清闲交叉口
    下载: 导出CSV

    表  2  不同开放策略优化后网络的效率对比

    Table  2  The comparison of the e–ciency among networks optimized by difierent strategies

    策略原网络ABCDEFGH
    效率0.08390.08430.08400.08410.08400.10720.09240.09620.0880
    提升率(%)00.47680.11920.23840.119227.771210.131114.66034.8868
    下载: 导出CSV
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  • 收稿日期:  2016-12-11
  • 录用日期:  2017-08-02
  • 刊出日期:  2018-11-20

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