Automatic Identification System (AIS) data represents a rich source of information about maritime traffic and offers a great potential for data analytics and predictive modeling solutions, which can help optimizing logistic chains and to reduce environmental impacts. In this work, we address the main limitations of the validity of AIS navigational data fields, by proposing a machine learning-based data-driven methodology to detect and (to the possible extent) also correct erroneous data. Additionally, we propose a metric that can be used by vessel operators and ports to express numerically their business and environmental efficiency through time and spatial dimensions, enabled with the obtained validated AIS data. We also demonstrate Port Area Vessel Movements (PARES) tool, which demonstrates the proposed solutions.

翻译：自动识别系统(AIS)数据是关于海上交通的丰富信息来源,为数据分析和预测模型解决方案提供了巨大潜力,有助于优化物流链和减少环境影响,在这项工作中,我们通过提出一种基于机械学习的数据驱动方法,以探测和(尽可能)纠正错误数据,解决自动识别系统导航数据领域有效性的主要局限性,此外,我们提出一种衡量标准,供船舶运营商和港口使用,通过时间和空间层面,用获得的经验证的AIS数据,从数字上说明其业务和环境效率,我们还演示了港口区域船舶移动工具,展示了拟议解决方案。