ZHOU Shi-bo
,
XIONG Zhen-nan
. Characteristic analysis of ship traffic flow at Chengshanjiao based on local density[J]. Journal of Dalian Maritime University, 2019
, 45(3)
: 100
-105
.
DOI: 10.16411/j.cnki.issn1006-7736.2019.03.014
[1] 李连博, 牛佳伟, 刘军坡等. 基于AIS 数据的海区通航危险度决策模型[J].中国航海, 2018,41(3):68-74.
LI L B, NIU J W, LIU J P, et al. AIS Data-Based Navigation risk Modeling[J]. NAVIGATION OF CHINA, 2018, 41(3): 68-74.(in Chinese)
[2] WU L, XU Y J, WANG Q, et al. Mapping Global Shipping Density from AIS Data[J]. Journal of Navigation, 2017, 70(1): 67-81.
[3] 肖潇, 赵强, 邵哲平,等. 基于AIS的特定船舶会遇实况分布[J]. 中国航海, 2014, 37(3): 50-53.
XIAO X, ZHAO Q, SHAO ZH P, et al. Specific Ship’s Encounter Live Distribution Based on AIS[J]. NAVIGATION OF CHINA, 2014, 37(3): 50-53. (in Chinese)
[4] STEPHEN H. Mapping UK Shipping Density and Routes Technical[R]. UK: Maritime Organization, 2014: 1-64.
[5] United states Coast Guard. Amver Density Plot Display [EB/OL]. http://www.amver.com/ Reports/DensityPlots.
[6] 朱飞祥, 张英俊, 高宗江. 基于数据挖掘的船舶行为研究[J]. 中国航海, 2012, 35(2): 50-54.
ZHU F X, ZHANG Y J, GAO Z J. Research on Ship Behaviors Based on Data Mining[J]. NAVIGATION OF CHINA, 2014, 37(3): 50-53. (in Chinese)
[7] 宁建强, 黄涛, 刁博宇,等. 一种基于海量船舶轨迹数据的细粒度网格海上交通密度计算方法[J]. 计算机工程与科学, 2015, 37(12): 2242-2249.
NING J Q, HUANG T, DIAO B Y, et al. A fine grained grid-based maritime traffic density algorithm for mass ship trajectory data[J]. Computer Engineering &Science, 2015, 37(12): 2242-2249. (in Chinese)
[8] 丁兆颖, 姚迪, 吴琳,等. 一种基于改进的DBSCAN的面向海量船舶位置数据码头挖掘算法[J]. 计算机工程与科学, 2015, 37(11): 2061-2067.
DING ZH Y, YAO D, WU L, et al. A dock mining algorithm for massive vessel location data based on improved DBSCAN[J]. Computer Engineering &Science, 2015, 37(11): 2061-2067. (in Chinese)
[9] 刘涛,胡勤友,杨春. 水上交通拥挤区域的聚类分析与识别[J]. 中国航海, 2010,33(4): 75-78.
LIU T, HU Q Y, YANG CH. Clustering Analysis and Identification of Traffic Congested Waters[J]. NAVIGATION OF CHINA, 2010, 33(4): 75-78. (in Chinese)
[10] SU Y Y, CHANG S J. Spatial Cluster Detection for the Fishing Vessel Monitoring Systems [C]// OCEANS'08 MTS/IEEE KOBE-TECHNO, KOBE , 2008: 1-4.
[11] RIVEIRO M, FALKMAN G. Interactive visualization of normal behavioral models and expert rules for maritime anomaly detection[C]//Computer Graphics, Imaging and Visualization, 2009. CGIV'09. Sixth International Conference on. IEEE, 2009: 459-466.
[12] RISTIC B, SCALA B, MORELANDE M, et al. Statistical analysis of motion patterns in AIS data: Anomaly detection and motion prediction[C]// Information Fusion, 2008 11th international conference on. IEEE, 2008: 1-7.
[13] LAXHAMMAR R, FALKMAN G, SVIESTINS E. Anomaly detection in sea traffic-a comparison of the gaussian mixture model and the kernel density estimator[C]//Information Fusion, 2009. FUSION'09. 12th International Conference on. IEEE, 2009: 756-763.
[14] LAMPE O D, KEHRER J, HAUSER H. Visual Analysis of Multivariate Movement Data using Interactive Difference Views[C]//VMV. 2010: 315-322.
[15] WILLEMS N, HUUB V, VAN W. Visualization of vessel movements[J]. Proceedings of the National Academy of Sciences of the United States of America, 2010, 28(3): 959-966.
[16] SCHEEPENS R, WILLEMS N, WETERING H, et al. Interactive visualization of multivariate trajectory data with density maps[C]// Visualization Symposium. IEEE, 2011: 147-154.
[17] AZARIADIS P. On using density maps for the calculation of ship routes[J]. Evolving Systems, 2017, 8: 1-11.
[18] JTS-165-2013. 海港总体设计规范[S]. 北京: 中华人民共和国交通运输部, 2013.
[19] 周世波, 徐维祥. 密度峰值快速搜索与聚类算法及其在船舶位置数据分析中的应用[J]. 仪器仪表学报, 2018, 39(7): 152-163.
ZHOU SH B, XU W X. Clustering by fast search and find of density peaks and its application in ship Location data analysis[J]. Chinese Journal of Scientific Instrument, 2018, 39(7): 152-163. (in Chinese)