|Table of Contents|

Citation:
　Xiaofeng Xu,Xiangen Bai,Yingjie Xiao,et al.A Port Ship Flow Prediction Model Based on the Automatic Identification System and Gated Recurrent Units[J].Journal of Marine Science and Application,2021,(3):572-580.[doi:10.1007/s11804-021-00228-9]
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A Port Ship Flow Prediction Model Based on the Automatic Identification System and Gated Recurrent Units

Info

Title:
A Port Ship Flow Prediction Model Based on the Automatic Identification System and Gated Recurrent Units
Author(s):
Xiaofeng Xu1;  Xiang’en Bai1;  Yingjie Xiao1;  Jia He1;  Yuan Xu2;  Hongxiang Ren3
Affilations:
Author(s):
Xiaofeng Xu1;  Xiang’en Bai1;  Yingjie Xiao1;  Jia He1;  Yuan Xu2;  Hongxiang Ren3
1. College of Merchant Shipping, Shanghai Maritime University, Shanghai, 201306, China;
2. Shanghai Waterway Engineering Design and Consulting Co., Ltd., Shanghai, 200120, China;
3. Naval Architecture and Ocean Engineering College, Dalian Maritime University, Dalian, 116026, China
Keywords:
Ship flow prediction|GRU neural network|Markov residual correction|AIS data
分类号:
-
DOI:
10.1007/s11804-021-00228-9
Abstract:
Water transportation today has become increasingly busy because of economic globalization. In order to solve the problem of inaccurate port traffic flow prediction, this paper proposes an algorithm based on gated recurrent units (GRUs) and Markov residual correction to pass a fixed cross-section. To analyze the traffic flow of ships, the statistical method of ship traffic flow based on the automatic identification system (AIS) is introduced. And a model is put forward for predicting the ship flow. According to the basic principle of cyclic neural networks, the law of ship traffic flow in the channel is explored in the time series. Experiments have been performed using a large number of AIS data in the waters near Xiazhimen in Zhoushan, Ningbo, and the results show that the accuracy of the GRU-Markov algorithm is higher than that of other algorithms, proving the practicability and effectiveness of this method in ship flow prediction.

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Memo

Memo:
Received date:2021-04-16。
Corresponding author:Xiaofeng Xu,E-mail:xfxu@shmtu.edu.cn
Last Update: 2021-11-04