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。
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.
作者
文元桥
杜磊
黄亚敏
孙腾达
肖长诗
周春辉
Wen Yuanqiao Du Lei Huang Yamin Sun Tengda Xiao Changshi Zhou Chunhui(School of Navigation, Wuhan University of Technology, Wuhan 430063, China Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China National Engineering Research Center for Water Transport Safety, Wuhan 430063, China China Transport Telecommunications ＆ Information Center, Beijing 100011, China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2017年第4期826-831,共6页
Journal of System Simulation
基金
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
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
traffic cluster
traffic flow
aggregation complexity
speed-diachronic complexity
densitydiachronic complexity
