Developing risk models to predict where collisions between vessels might occur is hindered by the relative sparsity of collisions. To address this, vessel encounters and near-misses have been used as a surrogate indicator of collision risk, referred to as ‘domain analysis’. When constructed empirically, using historical information, previous work is challenged by the multitude of factors which influence the passing distances between vessels. Within this paper, we conduct data mining of big vessel traffic datasets to determine the encounter characteristics across different waterways, vessel types and speeds, weather conditions and other exploratory variables. To achieve this, we utilise a novel approach of machine learning through a random forest algorithm to predict the critical passing distance between vessels in a multitude of conditions. We contribute a far greater range of influencing factors on domain size and shape than previous studies. Finally, we investigate the potential advantages of this approach to assess the spatial risk of collision across a large region. The results help to establish the factors that influence collision risk between navigating vessels and enable empirical maritime risk assessments.

中文翻译：

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使用机器学习开发上下文感知船舶领域

开发风险模型以预测船舶之间可能发生碰撞的位置受到碰撞相对稀疏的阻碍。为了解决这个问题，船舶遭遇和险情已被用作碰撞风险的替代指标，称为“域分析”。当使用历史信息凭经验构建时，以前的工作受到影响船只之间通过距离的众多因素的挑战。在本文中，我们对大型船舶交通数据集进行数据挖掘，以确定不同航道、船舶类型和速度、天气条件和其他探索性变量的相遇特征。为了实现这一点，我们利用一种新的机器学习方法，通过随机森林算法来预测多种条件下船只之间的临界通过距离。与以前的研究相比，我们对域大小和形状的影响因素范围更大。最后，我们研究了这种方法在评估大区域碰撞空间风险方面的潜在优势。结果有助于确定影响航行船舶之间碰撞风险的因素，并能够进行经验性海上风险评估。