Using machine learning for unsupervised maritime waypoint discovery from streaming AIS data

Andrej Dobrkovic, M. Iacob, J. Hillegersberg
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引用次数: 33

Abstract

Estimating the future position of a deep sea vessel more than 24 hours in advance is a major challenge for Dutch logistics service providers (LSPs). Their unscheduled arrival in ports directly impacts scheduling and waiting times of barges, propagating throughout the entire supply chain network. To help LSPs' planners improve planning operations, we intend to capture the characteristics of maritime routes for a specific region (the North Sea connecting the Netherlands and United Kingdom) in the form of a directed graph, which can be used as a foundation for predicting destination and arrival time of each associated vessel. To create such graph we need an efficient way to extract waypoints for traffic data and this is the problem we will address in this paper. Since LSPs only use publicly available data for arrival estimation, our solution is entirely based on Automatic Identification System (AIS) data. Extracting positional information from AIS, we explore various machine learning approaches to identify clusters. We apply DBSCAN algorithm and show its advantages and disadvantages when used on AIS data. The same process is repeated using meta-heuristics, comparing clustering results generated by a genetic algorithm and by modified ant-colony optimization to those produced by DBSCAN. Finally, we present a hybrid approach and its ability to discover waypoints, highlighting the achieved improvements. To extend the problem, two constraints are added. The first is the requirement to handle large volumes of streaming AIS data on standard PC-based hardware. The second introduces the common situation of "dark areas" in a map due to problems with receiving and transmitting AIS data. The algorithm discovers route waypoints in efficient and effective ways under these constraints.
从流式AIS数据中使用机器学习进行无监督海上航路点发现
对于荷兰物流服务提供商(lsp)来说,提前超过24小时估计深海船只的未来位置是一个重大挑战。它们的意外到达直接影响到驳船的调度和等待时间,并在整个供应链网络中传播。为了帮助LSPs的规划者改进规划操作,我们打算以有向图的形式捕捉特定区域(连接荷兰和英国的北海)的海上航线特征,这可以用作预测每个相关船只的目的地和到达时间的基础。为了创建这样的图,我们需要一种有效的方法来提取交通数据的路点,这是我们将在本文中解决的问题。由于lsp仅使用公开可用的数据进行到达估计,因此我们的解决方案完全基于自动识别系统(AIS)数据。从AIS中提取位置信息,我们探索了各种机器学习方法来识别集群。应用DBSCAN算法对AIS数据进行处理,并分析了其优缺点。使用元启发式重复相同的过程,将遗传算法和改进的蚁群优化产生的聚类结果与DBSCAN产生的聚类结果进行比较。最后,我们介绍了一种混合方法及其发现路点的能力,并强调了所取得的改进。为了扩展问题,添加了两个约束。首先是需要在标准的基于pc的硬件上处理大量的AIS数据流。第二部分介绍了由于接收和传输AIS数据的问题而导致地图上出现“黑暗区域”的常见情况。该算法能在这些约束条件下高效地发现路径点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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