Automatic generation of water route based on AIS big data and ECDIS

被引:2
作者
Hao Y. [1 ,2 ]
Zheng P. [1 ,3 ]
Han Z. [2 ]
机构
[1] Merchant Marine College, Shanghai Maritime University, Shanghai
[2] Marine Navigation School, Zhejiang International Maritime College, Zhoushan
[3] Faculty of Maritime and Transportation, Ningbo University, Ningbo
关键词
AIS; Automatic generation; Big data; ECDIS; Route; Water area;
D O I
10.1007/s12517-021-06930-w
中图分类号
学科分类号
摘要
With the goal of improving the real-time automatic generation of water routes and obtaining the optimal water routes, the study is based on automatic identification system (AIS) big data and electronic chart display and information system (ECDIS) water route automatic generation. The parallel AIS data processing technology of the distributed storage system is selected to realize the AIS big data processing that is automatically generated by the water route, the geodetic coordinates of the AIS information after the big data processing are transformed to Mercator projection, and the obtained Mercator coordinates are converted to the latitude and longitude of the sampling point to obtain the final sampling point. The Douglas-Peucker algorithm is selected to compress the sampling points and select the turning points from the compressed sampling points according to the fixed threshold set. The connection of the obtained turning points is the automatically generated initial water route. The ECDIS electronic chart is used to detect the generated route, and the final feasible route is the generated final water route. The experimental results show that the automatically generated water route mileage of this method is shorter, and the automatic generation time is within 300 ms, which has higher water route generation efficiency. © 2021, Saudi Society for Geosciences.
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相关论文
共 15 条
[1]  
Du Z., Qi X., Guo K., Design on automatic route planning algorithm based on ECDIS, Ship Ocean Eng, 48, 1, pp. 163-167, (2019)
[2]  
Li Y., Liu Z., Cai Y., Analysis of maritime traffic characteristics based on AIS data constraint clustering, Ship Ocean Eng, 47, 1, pp. 176-179, (2018)
[3]  
Lv J., Liu Z., Wang N., Method for automatic ship routing based on route stack, J Comput Appl, 38, S1, pp. 16-19, (2018)
[4]  
Ma J., Liu Q., Zhang C., A method for extracting ship encounter situation based on spatio-temporal analysis of AIS data, China Saf Sci J, 29, 5, pp. 111-116, (2019)
[5]  
Ning F., Xiong Y., Mou J., Huang X., Ship maneuverability index identification based AIS data, J Syst Simul, 29, 2, pp. 402-408, (2017)
[6]  
Pan J., Wang Y., Huang Y., Study on assessment method of vessel-bridge collision probability based on AIS data, J Huazhong Univ Sci Technol (Nat Sci Ed), 47, 11, pp. 109-114, (2019)
[7]  
Ren Y., Zhao J., Liu W., Ship navigation behavior prediction based on AIS data and LSTM network, J Shanghai Marit Univ, 40, 3, pp. 32-37, (2019)
[8]  
Sang L., Geng D., Zhang C., Cao D., Yan X., Optimization of ship routes in Wuhan Yangtze River Bridge Waterway based on ship behavior characteristics, Navigation China, 40, 3, pp. 58-63, (2017)
[9]  
Shao Z., Zhou T., Pan J., Zhang Z., University J., Research on dynamic ship domain in restricted water based on AIS data, Geo Inf Sci, 20, 5, pp. 564-570, (2018)
[10]  
Wang Z., AIS data-based ship emission estimation model and real ship verification, J Shanghai Marit Univ, 40, 4, pp. 12-16, (2019)