基于AIS数据的船舶行为特征挖掘与预测：研究进展与展望

doi:10.12082/dqxxkx.2021.210495

地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (12): 2111-2127.doi: 10.12082/dqxxkx.2021.210495

基于AIS数据的船舶行为特征挖掘与预测：研究进展与展望

甄荣¹^,²^,³(), 邵哲平¹^,^*(), 潘家财¹

1.集美大学航海学院,厦门 361021
2.内河航运技术湖北省重点实验室,武汉 430063
3.武汉理工大学智能交通系统研究中心,武汉 430063

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:
国家自然科学基金项目(52001134);中央军委科技部科技创新特区项目(KL12004);福建省自然科学基金(2020J01661);内河航运技术湖北省重点实验室基金(NHHY2020001)

Advance in Character Mining and Prediction of Ship Behavior based on AIS Data

ZHEN Rong¹^,²^,³(), SHAO Zheping¹^,^*(), PAN Jiacai¹

1. Navigation College, Jimei university, Xiamen 361001, China
2. Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China
3. Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China

Received:2021-08-24 Revised:2021-11-20 Online:2021-12-25 Published:2022-02-25
Supported by:
Natural Science Foundation of China(52001134);Science and Technology Innovation Special Zone project of Science and Technology Department of central Military Commission(KL12004);Natural Science Foundation of Fujian Province(2020J01661);Hubei Provincial Key Laboratory of Inland River Navigation Technology(NHHY2020001)

摘要/Abstract

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。

关键词: 海事地理信息, 水路运输, AIS数据, 船舶行为特征, 船舶航迹聚类, 船舶行为预测

Abstract:

The mining and prediction of ship behavior characteristics is an important research content of maritime intelligent transportation system and a key scientific problem in the field of transportation engineering. In order to systematically study the research status and development trend of ship behavior characteristic mining and prediction, the Vosviewer is used to generate the clustering map and trend evolution map of high-frequency keywords of research content from the perspective of bibliometrics, based on literatures collected from WOS database and CNKI database. After comprehensive analysis, three topics of data mining of maritime traffic elements based on Automatic Identification System (AIS), ship behavior clustering research and ship behavior prediction research are summarized. The research contents, methods and existing problems of each topic are systematically analyzed. The research results show that: ① In the aspect of data mining of maritime traffic elements based on AIS, the research mainly focuses on the mining of spatial features of maritime traffic and temporal features of traffic flow,and the results are lack of sufficient association mining of time features AIS data and background environment features. Further exploration needs to be made on the mining of space-time characteristics and data fusion. ②In the aspect of ship behavior clustering, the research mainly uses the unsupervised clustering method to study the clustering of ship track points and ship track segments to obtain the spatial-temporal distribution of ship navigation behavior patterns and the maneuvering intention. The similarity calculation method of ship trajectory integrating multidimensional features, identification of ship the adaptive selection of clustering parameters and the semantic modeling of ship behavior need to be further studied. ③ In the aspect of ship behavior prediction, it mainly focuses on the prediction of ship behavior based on dynamic equation, traditional intelligent algorithm and deep neural network. Considering the characteristics of randomness, diversity and coupling of ship behavior, the use of hybrid neural network model and combining neural network with vector machine. In the end, the paper proposes the promising research area which include mining of ship behavior feature based on semantic model, the prediction of ship behavior based on deep convolutional neural network, the mining of ship behavior feature based on knowledge graph and the visualization of prediction results.

Key words: marine GIS, waterway transportation, AIS data, characteristic of ship behavior, ship trajectories clustering, ship behavior prediction

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

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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^[11]
	航迹插值	线性插值^[12]、三次样条插值^[13]、拉格朗日插值^[14]、均值插值和向量函数的插值^[15]、复合插值^[16,17,18]、机器学习的插值方法^[19,20]
	航迹融合	统计加权方法^[22]、基于BP神经网络的方法^[23]、基于卡尔曼滤波的方法^[24]、时间序列聚类的方法^[25]、集成融合方法^[26]
	航迹压缩	离线压缩方法（DP算法^[27]、Dead Reckoning^[28]、OLDCAT^[29]、扫描-选择-移动算法^[30]）、在线压缩方法（改进的滑动窗口^[31,32]、在线有向无环图^[33]）
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^[35]、交通流风险模型的拟合^[36]、船舶到达规律模型拟合^[38]
船舶交通流参数特征分布分析	交通流特征合成	船舶交通流场方法^[39]、密度聚类方法^[40]
船舶会遇分析	船舶会遇时空特征	会遇时空特征统计^[41,42]
船舶会遇分析	会遇船舶紧迫度	船舶运动几何关系^[43]、空间约束和热力图方法^[44-45]、支持向量机-贝叶斯滤波方法^[4]

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^[48]、切比雪夫距离^[49]计算方法
船舶航迹距离计算	船舶轨迹段间距离计算	结构相似度^[50]、Hauudorff^[51,52]、动态时间弯矩^[53]、基于行为特征的方法^[54]、基于复合特征的方法^[55]、无监督深度学习^[56]等距离计算方法
船舶航迹点聚类	基于划分的船舶轨迹点聚类	k-means^[57]、k-mediods^[58]
	基于密度的船舶轨迹点聚类	DBSCAN^[59]、密度峰值快速搜索与聚类方法^[60]
	基于层次的船舶轨迹点聚类	AGNES层次聚类方法^[61,62]
船舶航迹段聚类	船舶交通行为模式的提取	密度聚类^[63]、层次聚类^[64]、谱聚类^[65,66]、拉普拉斯特征聚类^[5]方法
船舶航迹段聚类	船舶操纵行为特征的推断	结合DP和DBSCAN的聚类方法^[67]、多步子航迹聚类分析和随机邻居嵌入算法^[68]、概率图形模型^[69]

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^[73]、卡尔曼滤波^[74,75,76]、灰色预测^[77]、矢量分析^[78]和惯性定位原理方法^[79]
基于传统智能算法的船舶行为预测	BP神经网络方法^[80,81,82]、k-最近邻方法^[83]、支持向量机^[84]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^{[85,86,87,88]}、门循环单元^[89]、注意力机制^[90]、生成对抗网络^[91]、改进Seq2Seq^[92]神经网络方法
基于深度卷积神经网络的船舶行为预测	CNN^[95]、CNN+LSTM^[96,97]、多视图特征融合网络方法^[98]

基于AIS数据的船舶行为特征挖掘与预测：研究进展与展望

Advance in Character Mining and Prediction of Ship Behavior based on AIS Data

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