AISFuser: Encoding Maritime Graphical Representations With Temporal Attribute Modeling for Vessel Trajectory Prediction

IF 8.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhiwen Zhang;Wei Yuan;Zipei Fan;Xuan Song;Ryosuke Shibasaki
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引用次数: 0

Abstract

Maritime transportation, vital for nearly 90% of global trade, necessitates precise vessel trajectory prediction for safety and efficiency. Although the Automatic Identification System (AIS) provides a comprehensive data source, how to model these multi-modal and heterogeneous time-varying sequences (such as vessels’ kinetic information and ocean weather factors) poses a formidable challenge. Moreover, most existing approaches are limited by the confined scope of vessel trajectory modeling, making it impossible to consider the unique characteristics of maritime transportation system. To tackle these challenges, we propose a novel framework called AISFuser to i) encode unique maritime traffic network into graphical representations, and ii) introduce the heterogeneity into multi-modal temporal embeddings through Self-Supervised Learning (SSL). Specifically, our AISFuser is constructed by combining an attention-based graph block with a transformer network to encode information across space and time, respectively. In terms of temporal dimension, one SSL auxiliary task is also designed to enhance the heterogeneity of temporal representations and supplement the main vessel prediction task. We validate the effectiveness of the proposed AISFuser on a real-world AIS dataset. Extensive experimental results demonstrate that our method can forecast multiple attributes of vessel trajectory for over 10 hours into the future, outperforming competitive baselines.
海上运输对全球近90%的贸易至关重要,为了安全和效率,需要精确的船舶轨迹预测。尽管自动识别系统(AIS)提供了一个全面的数据源,但如何对这些多模态和异构时变序列(如船舶动力学信息和海洋天气因素)进行建模是一个巨大的挑战。此外,现有的大多数方法受限于船舶轨迹建模的范围,无法考虑海上运输系统的独特特性。为了应对这些挑战,我们提出了一个名为AISFuser的新框架,该框架i)将独特的海上交通网络编码为图形表示,ii)通过自监督学习(SSL)将异质性引入多模态时间嵌入。具体来说,我们的AISFuser是通过结合基于注意力的图块和变压器网络来构建的,分别跨空间和时间对信息进行编码。在时间维度方面,还设计了一个SSL辅助任务,以增强时间表示的异质性,并补充主血管预测任务。我们在真实AIS数据集上验证了所提出的AISFuser的有效性。大量的实验结果表明,我们的方法可以预测未来超过10小时的船舶轨迹的多个属性,优于竞争基线。
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来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
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