Vessel Trajectory Prediction Using Historical Automatic Identification System Data

被引:49
|
作者
Alizadeh, Danial [1 ]
Alesheikh, Ali Asghar [1 ]
Sharif, Mohammad [2 ]
机构
[1] KN Toosi Univ Technol, Fac Geodesy & Geomat Engn, Dept Geospatial Informat Syst, Tehran, Iran
[2] Univ Hormozgan, Dept Geog, Fac Humanities, Bandar Abbas, Iran
来源
JOURNAL OF NAVIGATION | 2021年 / 74卷 / 01期
关键词
Trajectory; Movement Models; Ship Behaviour; Automatic Identification System; ANOMALY DETECTION; AIS;
D O I
10.1017/S0373463320000442
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
For maritime safety and security, vessels should be able to predict the trajectories of nearby vessels to avoid collision. This research proposes three novel models based on similarity search of trajectories that predict vessels' trajectories in the short and long term. The first and second prediction models are, respectively, point-based and trajectory-based models that consider constant distances between target and sample trajectories. The third prediction model is a trajectory-based model that exploits a long short-term memory approach to measure the dynamic distance between target and sample trajectories. To evaluate the performance of the proposed models, they are applied to a real automatic identification system (AIS) vessel dataset in the Strait of Georgia, USA. The models' accuracies in terms of Haversine distance between the predicted and actual positions show relative prediction error reductions of 40 center dot 85% for the second model compared with the first model and 23% for the third model compared with the second model.
引用
收藏
页码:156 / 174
页数:19
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