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Vessel movement analysis and pattern discovery using density-based clustering approach

机译：使用基于密度的聚类方法进行船舶运动分析和模式发现

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Automatic identification system (AIS) has been widely equipped on vessels for maritime communication, positioning and traffic monitoring. The comprehensive data obtained by AIS provides spatio-temporal traces depicting the vessels' trajectories and can be used as a coherent source of information for vessels' behavior and the overall maritime traffic analysis, in supporting of the better traffic planning and service optimization. However, it is challenging to process and analysis such a large amount of AIS data that is associated with a great variety of vessels. In this paper, we propose an unsupervised data mining method using density-based strategy to analyze vessels' trajectories and extract the traffic patterns from historical AIS data. It starts with stops and moves identification from vessels' trajectories, followed by the extraction of stationary areas of interest from the stops and the detection of the main traffic routes from the moves using density-based clustering method, which takes both the speed and direction into consideration. Experiments on the real AIS data demonstrate the effectiveness of this work.
机译：自动识别系统（AIS）已广泛安装在船舶上，用于海上通讯，定位和交通监控。 AIS获得的综合数据提供了描述船舶轨迹的时空轨迹，可以用作船舶行为和整体海上交通分析的一致信息源，以支持更好的交通规划和服务优化。但是，处理和分析与大量船只相关的大量AIS数据具有挑战性。在本文中，我们提出了一种基于密度的策略的无监督数据挖掘方法，以分析船只的轨迹并从历史AIS数据中提取交通模式。它首先从停靠点开始，然后从船只的轨迹进行识别，然后从停靠点中提取感兴趣的固定区域，并使用基于密度的聚类方法从停靠点中检测出主要交通路线，将速度和方向都纳入考虑。对真实的AIS数据进行的实验证明了这项工作的有效性。

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