地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (12): 2111-2127.doi: 10.12082/dqxxkx.2021.210495
收稿日期:
2021-08-24
修回日期:
2021-11-20
出版日期:
2021-12-25
发布日期:
2022-02-25
通讯作者:
*邵哲平(1964— ),男,福建福州人,博士,教授,主要从事海上交通工程研究。E-mail: zpshao@jmu.edu.cn作者简介:
甄 荣(1990— ),男,内蒙古乌兰察布人,博士,主要从事海事大数据挖掘研究。E-mail: zrandsea@163.com
基金资助:
ZHEN Rong1,2,3(), SHAO Zheping1,*(
), PAN Jiacai1
Received:
2021-08-24
Revised:
2021-11-20
Online:
2021-12-25
Published:
2022-02-25
Supported by:
摘要:
船舶行为特征挖掘与预测是水上智能交通系统的重要研究内容,也是交通运输工程领域的关键科学问题。为系统研究基于船舶自动识别系统(Automatic Identification System, AIS)数据的船舶行为特征挖掘与预测的研究现状与发展趋势,本文首先针对Web of Science(WOS)和中国知网(China National Knowledge Infrastructure, CNKI)收录的文献,用知识图谱分析软件VOSviewer对文献关键词进行处理,从文献计量学的角度生成高频关键词的聚类图谱和趋势演化。然后对基于AIS数据的水上交通要素挖掘、船舶行为聚类和船舶行为预测3个主题的研究内容、方法、存在问题进行了系统分析和展望,研究结果表明:① 在基于AIS的水上交通要素挖掘方面,主要集中在对AIS数据中表征船舶行为空间特征和交通流的时间特征单独挖掘分析,缺乏对AIS数据的时间、空间以及环境因素特征的关联挖掘,对于如何进行交通要素的关联融合挖掘研究还有待深入探索;② 在船舶行为聚类方面,研究主要是运用无监督聚类方法研究船舶航迹点和航迹段聚类,得到船舶航行行为模式的时空分布和船舶操纵意图辨识模型,然而融合多维特征的船舶轨迹的相似性计算方法、聚类参数的自适应选取以及船舶行为的语义特征建模有待进一步研究;③ 在船舶行为预测方面,主要集中在基于动力学方程、传统智能算法和深度循环神经网络的船舶行为预测研究,考虑船舶行为的随机性、多样性和耦合性的特点,运用混合神经网络模型以及神经网络与向量机、注意力机制相结合的模型实现多维的船舶航行行为特征的实时预测将是新的研究方向。最后提出了基于语义模型的船舶行为特征挖掘、基于深度卷积神经网络的船舶行为的预测和基于知识图谱的船舶行为特征挖掘和预测结果可视化等有待进一步研究的方向。
甄荣, 邵哲平, 潘家财. 基于AIS数据的船舶行为特征挖掘与预测:研究进展与展望[J]. 地球信息科学学报, 2021, 23(12): 2111-2127.DOI:10.12082/dqxxkx.2021.210495
ZHEN Rong, SHAO Zheping, PAN Jiacai. Advance in Character Mining and Prediction of Ship Behavior based on AIS Data[J]. Journal of Geo-information Science, 2021, 23(12): 2111-2127.DOI:10.12082/dqxxkx.2021.210495
表1
水上交通要素的内容及方法研究总结
研究主题 | 研究内容 | 研究方法及模型 |
---|---|---|
船舶航迹数据预处理 | 航迹清洗 | 数据查询方法[ |
航迹插值 | 线性插值[ | |
航迹融合 | 统计加权方法[ | |
航迹压缩 | 离线压缩方法(DP算法[ | |
船舶交通流参数特征分布分析 | 交通流特征参数拟合 | 船舶交通量和速度模型拟合[ |
交通流特征合成 | 船舶交通流场方法[ | |
船舶会遇分析 | 船舶会遇时空特征 | 会遇时空特征统计[ |
会遇船舶紧迫度 | 船舶运动几何关系[ |
表2
船舶行为聚类研究内容及方法总结
研究主题 | 研究内容 | 研究方法及模型 |
---|---|---|
船舶航迹距离计算 | 船舶轨迹点间距离计算 | 欧式距离[ |
船舶轨迹段间距离计算 | 结构相似度[ | |
船舶航迹点聚类 | 基于划分的船舶轨迹点聚类 | k-means[ |
基于密度的船舶轨迹点聚类 | DBSCAN[ | |
基于层次的船舶轨迹点聚类 | AGNES层次聚类方法[ | |
船舶航迹段聚类 | 船舶交通行为模式的提取 | 密度聚类[ |
船舶操纵行为特征的推断 | 结合DP和DBSCAN的聚类方法[ |
表3
船舶行为预测研究总结
研究内容 | 研究方法及模型 |
---|---|
基于动力学方程船舶行为预测 | 高斯跟踪滤波[ |
基于传统智能算法的船舶行为预测 | BP神经网络方法[ |
基于深度循环神经网络船舶行为预测 | 基于长短期记忆[ |
基于深度卷积神经网络的船舶行为预测 | CNN[ |
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