Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2017 , Vol. 51 >Issue 7: 840 - 845

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2017.07.011

 Visual Analytic System of Vessel Traffic in Bridge Waterway

Expand
  •  1. Intelligent Transport System Research Center, Wuhan University of Technology,
     Wuhan 430063, China; 2. National Engineering Research Center for Water Transport Safety,
     Wuhan 430063, China; 3. School of Energy and Power Engineering, Wuhan University of
     Technology, Wuhan 430063, China; 4. School of Economics and Management,
    Minjiang University, Fuzhou 350108, China

Online published: 2017-07-31

Supported by

 

Fold

Abstract

 Current practice shows that working with bridge waterway automatic identification system (AIS) data often does not lead to insight but rather confusion because large data volume without further being processed is hard to understand. In order to deal with this issue, bridge waterway of Wuhan section in Yangtze River was selected as research area for the study of AIS visualization model and humancomputer interaction method. Additionally, two dimensional kernel density estimation approach was adopted in the heatmap generation to overcome the defects of traditional grid method. Furthermore, based on electric navigation charts, a visual analytics system was built. Finally, three instances were illustrated and results demonstrated that it is conductive to the users in maritime department to make decisions depending on the knowledge they discovered from the visual analytics system.

Key words：  
vessel traffic in bridge waterway; visual analytics; automatic identification system (AIS) visualization; waterway transportation

Cite this article

LEI Jinyu1,2,3,CHU Xiumin1,2,HE Wei4,ZHOU Yingping1,2 .  Visual Analytic System of Vessel Traffic in Bridge Waterway[J]. Journal of Shanghai Jiaotong University, 2017 , 51(7) : 840 -845 . DOI: 10.16183/j.cnki.jsjtu.2017.07.011

References

 ［1］徐武雄, 初秀民, 刘兴龙. 水上交通信息可视化技术研究进展［J］. 中国航海, 2015, 38(1):  3438.
XU Wuxiong, CHU Xiumin, LIU Xinglong. Advances of maritime traffic information visualization techniques［J］. Navigation of China, 2015, 38(1):  3438.
［2］ANDRIENKO G, ANDRIENKO N, BAK P, et al. Visual analytics of movement［M］. Berlin:  Springer, 2013: 13.
［3］熊振南, 程俊康, 翁跃宗. 点统计法在船舶交通流分析中的应用［J］. 中国航海, 2009, 32(1):  6467.
XIONG Zhennan, CHENG Junkang, WENG Yuezong. Application of pointstatistic method in the analysis of vessel traffic flow［J］. Navigation of China, 2009, 32(1):  6467.
［4］徐武雄， 初秀民， 刘兴龙. 多桥水域航道通过能力仿真研究［J］. 交通运输系统工程与信息， 2015， 15(3):  127133.
XU Wuxiong, CHU Xiumin, LIU Xinglong. Simulation for transit capacity of a multibridge waterway［J］. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(3):  127133.
［5］SILVEIRA P A M, TEIXEIRA A P, SOARES C G. Use of AIS data to characterise marine traffic patterns and ship collision risk off the coast of Portugal［J］. Journal of Navigation, 2013, 66(6):  879898.
［6］甘浪雄, 张磊, 邹早建, 等. 基于场方法的船舶交通流分析［J］. 上海交通大学学报, 2014, 48(4): 551557.
GAN Langxiong, ZHANG Lei, ZOU Zaojian, et al. Analysis of vessel traffic flow based on field method［J］. Journal of Shanghai Jiao Tong University, 2014, 48(4): 551557.
［7］GUO H, WANG Z, YU B, et al. Tripvista:  Triple perspective visual trajectory analytics and its application on microscopic traffic data at a road intersection［C］∥Pacific Visualization Symposium. USA:  IEEE, 2011:  163170.
［8］何兆成，周亚强，余志. 基于数据可视化的区域交通状态特征评价方法［J］. 交通运输工程学报, 2016, 16(1):  133140.
HE Zhaocheng, ZHOU Yaqiang, YU Zhi. Regional trafic state evaluation method based on data visualization［J］. Journal of Traffic and Transportation Engineering, 2016, 16(1):  133140.
［9］ANDRIENKO G, ANDRIENKO N. Spatiotemporal aggregation for visual analysis of movements［C］∥Proceedings of IEEE Symposium on Visual Analytics Science & Technology IEEE Comput. Columbus, Ohio, USA: IEEE, 2008: 5158.
［10］WILLEMS N, SCHEEPENS R, VAN DE WETERING H, et al. Visualization of vessel traffic［M］.  New York:  Springer, 2013:  7387.
［11］SCHEEPENS R, WILLEMS N, HUUB V D W, et al. Interactive visualization of multivariate trajectory data with density maps［C］∥Pacific Visualization Symposium. USA:  IEEE, 2011: 147154.
［12］SCHEEPENS R, HURTER C, VAN DE WETERING H, et al. Visualization, selection, and analysis of traffic flows［J］.  IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):  379388.
［13］WANG Z, LU M, YUAN X, et al. Visual traffic jam analysis based on trajectory data［J］.  IEEE Transactions on Visualization and Computer Graphics, 2013, 19(12):  21592168.
［14］姜晓睿, 郑春益, 蒋莉, 等. 大规模出租车起止点数据可视分析［J］. 计算机辅助设计与图形学学报, 2015, 27(10):  19071917.
JIANG Xiaorui, ZHENG Chunyi, JIANG Li, et al. Visual analysis of large taxi origindestination data［J］. Journal of ComputerAided Design & Computer Graphics, 2015, 27(10): 19071917.
［15］SILVERMAN B W. Density estimation for statistics and data analysis［M］. London:  CRC Press, 1986: 711.
Options
Outlines

/

〈 〉