Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks
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Published:2023-07-06
Issue:7
Volume:11
Page:1379
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ISSN:2077-1312
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Container-title:Journal of Marine Science and Engineering
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language:en
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Short-container-title:JMSE
Author:
Li Yong1, Li Zhaoxuan1, Mei Qiang23, Wang Peng24, Hu Wenlong5, Wang Zhishan1, Xie Wenxin1, Yang Yang3, Chen Yuhaoran1
Affiliation:
1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China 2. Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China 3. Navigation College, Jimei University, Xiamen 361021, China 4. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100086, China 5. School of Computer Science, University of Auckland, Auckland 1010, New Zealand
Abstract
The intelligent maritime transportation system has emerged as a pivotal component in port management, owing to the rapid advancements in artificial intelligence and big data technology. Its essence lies in the application of digital modeling techniques, which leverage extensive ship data to facilitate efficient operations. In this regard, effective modeling and accurate prediction of the fluctuation patterns of ship traffic in multiple port regions will provide data support for trade analysis, port construction planning, and traffic safety management. In order to better express the potential interdependencies between ports, inspired by graph neural networks, this paper proposes a data-driven approach to construct a multi-port network and designs a spatiotemporal graph neural network model. The model incorporates graph attention networks and a dilated causal convolutional architecture to capture the temporal and spatial dimensions of traffic variation patterns. It also employs a gated-mechanism-based spatiotemporal bi-dimensional feature fusion strategy to handle the potential unequal relationships between the two dimensions of features. Compared to existing methods for port traffic prediction, this model fully considers the network characteristics of the overall port and fills the research gap in multi-port scenarios. In the experiments, real port ship traffic datasets were constructed using data from the Automatic Identification System (AIS) and port geographical information data for model validation. The results demonstrate that the model exhibits outstanding robustness and performs well in predicting traffic in multiple sub-regional port clusters.
Funder
National Natural Science Foundation of China Natural Science Foundation of Fujian Province Shanghai Science and Technology Committee National Key Research and Development Program of China
Subject
Ocean Engineering,Water Science and Technology,Civil and Structural Engineering
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