基于AIS数据的船舶行为特征挖掘与预测:研究进展与展望

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。

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

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.