Navigation College, Jimei University, Xiamen 361021, China.
Naval Academy Research Institute, 29240 Brest, France.
Sensors (Basel). 2022 Jul 14;22(14):5281. doi: 10.3390/s22145281.
The successful emergence of real-time positioning systems in the maritime domain has favored the development of data infrastructures that provide valuable monitoring and decision-aided systems. However, there is still a need for the development of data mining approaches oriented to the detection of specific patterns such as unusual ship behaviors and collision risks. This research introduces a CSBP (complex ship behavioral pattern) mining model aiming at the detection of ship patterns. The modeling approach first integrates ship trajectories from automatic identification system (AIS) historical data, then categorizes different vessels' navigation behaviors, and introduces a visual-oriented framework to characterize and highlight such patterns. The potential of the model is illustrated by a case study applied to the Jiangsu and Zhejiang waters in China. The results show that the CSBP mining model can highlight complex ships' behavioral patterns over long periods, thus providing a valuable environment for supporting ship traffic management and preventing maritime accidents.
实时定位系统在航海领域的成功应用，促进了数据基础设施的发展，这些基础设施提供了有价值的监测和决策辅助系统。然而，仍然需要开发面向特定模式（如异常船舶行为和碰撞风险）检测的数据挖掘方法。本研究提出了一种 CSBP（复杂船舶行为模式）挖掘模型，旨在检测船舶模式。该建模方法首先整合来自自动识别系统（AIS）历史数据的船舶轨迹，然后对不同船舶的航行行为进行分类，并引入一个面向可视化的框架来描述和突出这些模式。该模型的潜力通过应用于中国江苏和浙江水域的案例研究得到了验证。结果表明，CSBP 挖掘模型可以突出长时间内复杂船舶的行为模式，从而为支持船舶交通管理和预防海上事故提供了有价值的环境。
