交通信息与安全2023，Vol.41Issue(6)：82-89,8.DOI:10.3963/j.jssn.1674-4861.2023.06.009
基于时空注意力机制的STA-GRU船舶轨迹预测方法
Ship Trajectory Prediction Method of Gated Recurrent Unit Based on Spatial-temporal Attention Mechanism
摘要
Abstract
The accuracy of ship trajectory prediction is crucial for the intelligence level of ship's navigation.Ad-dressing the insufficient capability of the gated recurrent unit(GRU)in capturing spatial-temporal information from ship data,which leads to poor accuracy in trajectory prediction,a method of GRU ship trajectory prediction based on spatial-temporal attention mechanism(STA-GRU)is investigated.The traditional activation function in GRU is improved by a weighted activation function set to retain more comprehensive ship trajectory data.A spatial attention mechanism module is introduced to extract spatial location features of ships using latitude,longitude,relative lati-tude,and relative longitude as input sequences.This module computes spatial-temporal weight attention factors to obtain spatial feature vectors.The resulting vectors serve as the training dataset for the STA-GRU model used for ship trajectory prediction.Experimental validation is conducted using AIS data from Qingdao Port,with an input du-ration of 20 minutes and a sampling frequency of 2 min.A ship navigation trajectory dataset is constructed under these conditions.Results indicate that,compared to LSTM,AT-GRU,and Bi-GRU algorithms,the STA-GRU model not only converges faster during training but also significantly reduces the root mean square error,mean absolute er-ror,and final displacement error.The average reductions of the aforementioned indexes for trajectory prediction are 50.2%,38.7%,and 48.3%,respectively.For longitude prediction,the average reductions are 43.8%,50.5%,and 49.5%,respectively.For latitude prediction,the average reductions are 52.4%,48.4%,and 50.5%,respectively.Therefore,the proposed STA-GRU model exhibits significantly improved accuracy in ship trajectory prediction and meets the real-time requirements for trajectory prediction.关键词
智能航行/AIS数据/船舶轨迹预测/门控循环单元/注意力机制Key words
intelligent navigation/AIS data/ship trajectory prediction/Gated Recurrent Unit/attention mechanism分类
交通工程引用本文复制引用
黄敏,杨亚东,吴新鹏..基于时空注意力机制的STA-GRU船舶轨迹预测方法[J].交通信息与安全,2023,41(6):82-89,8.基金项目
国家自然科学基金项目(62073251)资助 （62073251）
