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基于Lempel-Ziv算法与TOPSIS的水上交通流复杂性测度

张明阳 张笛 郭欢 付姗姗 黄亚敏

张明阳, 张笛, 郭欢, 付姗姗, 黄亚敏. 基于Lempel-Ziv算法与TOPSIS的水上交通流复杂性测度[J]. 交通运输工程学报, 2017, 17(1): 109-118.
引用本文: 张明阳, 张笛, 郭欢, 付姗姗, 黄亚敏. 基于Lempel-Ziv算法与TOPSIS的水上交通流复杂性测度[J]. 交通运输工程学报, 2017, 17(1): 109-118.
ZHANG Ming-yang, ZHANG Di, GUO Huan, FU Shan-shan, HUANG Ya-min. Complexity metric of waterborne traffic flow based on Lempel-Ziv algorithm and TOPSIS[J]. Journal of Traffic and Transportation Engineering, 2017, 17(1): 109-118.
Citation: ZHANG Ming-yang, ZHANG Di, GUO Huan, FU Shan-shan, HUANG Ya-min. Complexity metric of waterborne traffic flow based on Lempel-Ziv algorithm and TOPSIS[J]. Journal of Traffic and Transportation Engineering, 2017, 17(1): 109-118.

基于Lempel-Ziv算法与TOPSIS的水上交通流复杂性测度

基金项目: 

国家科技支撑计划项目 2015BAG20B00

国家自然科学基金项目 51579203

武汉理工大学研究生优秀学位论文培育项目 2016-YS-041

详细信息
    作者简介:

    张明阳(1993-), 河南周口人, 武汉理工大学工学博士研究生, 从事水上交通复杂性研究

    张笛(1983-), 湖北武汉人, 武汉理工大学副研究员, 工学博士

  • 中图分类号: U692.4

Complexity metric of waterborne traffic flow based on Lempel-Ziv algorithm and TOPSIS

More Information
  • 摘要: 为了定量分析并划分水上交通流的复杂性等级, 提出了一种基于Lempel-Ziv算法与TOPSIS的水上交通流复杂性测度方法。运用Lempel-Ziv算法求得实测水上交通流时间序列和对比序列(周期序列、Logistic序列、Henon序列、随机序列) 的复杂性特征值, 采用TOPSIS得到各个序列的贴近度, 根据对比序列的贴近度划分复杂性等级区间, 按照水上交通流时间序列的贴近度和所在的复杂性等级区间来表征各个序列的复杂程度, 并对长江口南槽航道的实测水上交通流进行复杂性测度。计算结果表明: 船舶交通事故数量和下行标准船舶数量与船舶交通流时序复杂性贴近度的相关系数分别为0.698 1、0.769 2, 变化趋势基本一致, 表明贴近度的计算结果可以反映水域船舶交通流的复杂性; 周期序列的贴近度为0.000 1, 随机序列的贴近度为0.999 9, Logistic序列和Henon序列的贴近度分别为0.449 2、0.537 7, 其值大于周期序列的贴近度, 小于随机序列的贴近度; 2013年7~11月水上交通流序列的贴近度分别为0.828 0、0.852 7、0.856 5、0.823 7、0.810 7, 说明序列的复杂性基本一致; 5个月的水上交通流序列的贴近度远大于周期序列的贴近度, 处于随机序列和Henon序列的贴近度之间, 更接近随机序列的贴近度, 说明水上交通流系统不是周期与完全随机的动力学系统; 5个月水上交通流复杂性的整体等级为1级, 表现出高复杂性的特点。

     

  • 图  1  水上交通流复杂性测度步骤

    Figure  1.  Steps of complexity metric for waterborne traffic flow

    图  2  四个序列复杂性特征值

    Figure  2.  Complexity eigenvalues of 4sequences

    图  3  四个序列的距离尺度和贴近度

    Figure  3.  Distance scales and close degrees of 4sequences

    图  4  地图上数据采集所选取的断面

    Figure  4.  Section based on data collection from map

    图  5  海图上数据采集所选取的断面

    Figure  5.  Section based on data collection from chart

    图  6  船舶时间序列

    Figure  6.  Time sequence for ships

    图  7  采样船舶的船头时距变化

    Figure  7.  Headway variation of sampling ships

    图  8  序列的复杂性特征值对比

    Figure  8.  Comparison of complexity eigenvalues of sequence

    图  9  交通事故数量和贴近度的关系

    Figure  9.  Relationship between number of traffic accidents and close degree

    图  10  下行标准船舶数量与贴近度的关系

    Figure  10.  Relationship between number of downside standard ships and close degree

    表  1  复杂性等级框架

    Table  1.   Framework of complexity class

    下载: 导出CSV

    表  2  实测序列和对比序列的复杂性特征值

    Table  2.   Complexity eigenvalues of measured sequences and compared sequences

    下载: 导出CSV

    表  3  时间序列与对比序列的距离尺度及贴近度

    Table  3.   Distance scales and close degrees of time sequences and compared sequences

    下载: 导出CSV
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  • 收稿日期:  2016-08-11
  • 刊出日期:  2017-02-25

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