系统仿真学报 ›› 2017, Vol. 29 ›› Issue (4): 826-831.doi: 10.16182/j.issn1004731x.joss.201704016

• 仿真建模理论与方法 • 上一篇    下一篇

水上交通流宏观复杂度建模与仿真

文元桥1,2,3, 杜磊1, 黄亚敏1, 孙腾达4, 肖长诗1,2, 周春辉1,2   

  1. 1.武汉理工大学航运学院,湖北 武汉 430063;
    2.湖北省内河航运技术重点实验室,湖北 武汉 430063;
    3.国家水运安全工程技术研究中心,湖北 武汉 430063;
    4. 中国交通通信信息中心,北京 100011
  • 收稿日期:2015-07-06 修回日期:2015-09-08 出版日期:2017-04-08 发布日期:2020-06-03
  • 作者简介:文元桥(1975-),男,湖北松滋,博士,教授,研究方向为水上交通安全与仿真等。
  • 基金资助:
    国家自然科学基金(51679180, 51579204),中央高校基本科研业务费专项资金(142212001), 武汉理工大学双一流项目

Modeling and Simulation of Marine Traffic Macroscopic Flow Complexity

Wen Yuanqiao1,2,3, Du Lei1, Huang Yamin1, Sun Tengda4, Xiao Changshi1,2, Zhou Chunhui1,2   

  1. 1. School of Navigation, Wuhan University of Technology, Wuhan 430063, China;
    2. Hubei Key Laboratory of Inland Shipping Technology,Wuhan 430063, China;
    3. National Engineering Research Center for Water Transport Safety, Wuhan 430063, China;
    4. China Transport Telecommunications & Information Center, Beijing 100011, China
  • Received:2015-07-06 Revised:2015-09-08 Online:2017-04-08 Published:2020-06-03

摘要: 为了准确描述交通管理人员对水上交通流认知的难易程度,提出了一种定量计算水上交通流宏观复杂度的方法。通过模糊聚类将区域交通流的宏观复杂度量转化为对交通簇内、外认知复杂性的测度,并根据交通流动力方程插值得到整个水域的历史交通流场,分别计算聚集复杂度速度特征复杂度以及密度特征复杂度,通过水上交通流宏观复杂度模型获得水上交通流宏观复杂度。构造典型水域进行仿真实验,验证了该模型可客观反映水上交通流宏观复杂度的空间分布,交通管理人员能及时地发现复杂度较高的区域,并识别偏离航道或逆航道航行等船舶异常行为。

关键词: 交通簇, 交通流, 聚集复杂度, 速度特征复杂度, 密度特征复杂度

Abstract: To accurately describe the difficulty of cognitive traffic in the view of traffic managers, a method for quantitative calculation of marine traffic macroscopic flow complexity was proposed. The similar ships were cataloged into a traffic cluster; According to the historical traffic flow field by interpolation method, the aggregation complexity, the speed-diachronic complexity and the density-diachronic complexity were evaluated. The cognitive complexity was obtained by the marine traffic macroscopic flow complexity, showing the cognitive complexity in the macroscopic complexity map. The simulation experiment shows the macroscopic complexity model is available for demonstrating the distribution of complexity in space.

Key words: traffic cluster, traffic flow, aggregation complexity, speed-diachronic complexity, density- diachronic complexity

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