The presented method reconstructs a network (a graph) from AIS data, which reflects vessel traffic and can be used for route planning. The approach consists of three main steps: maneuvering points detection, waypoints discovery, and edge construction. The maneuvering points detection uses the CUSUM method and reduces the amount of data for further processing. The genetic algorithm with spatial partitioning is used for waypoints discovery. Finally, edges connecting these waypoints form the final maritime traffic network. The approach aims at advancing the practice of maritime voyage planning, which is typically done manually by a ship’s navigation officer. The authors demonstrate the results of the implementation using Apache Spark, a popular distributed and parallel computing framework. The method is evaluated by comparing the results with an on-line voyage planning application. The evaluation shows that the approach has the capacity to generate a graph which resembles the real-world maritime traffic network.

中文翻译：

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使用进化算法从 AIS 数据中提取海上交通网络

所提出的方法从 AIS 数据重建网络（图形），该数据反映了船舶流量并可用于路线规划。该方法包括三个主要步骤：机动点检测、航路点发现和边缘构建。机动点检测采用 CUSUM 方法，减少了进一步处理的数据量。具有空间划分的遗传算法用于航路点发现。最后，连接这些航点的边形成最终的海上交通网络。该方法旨在推进海上航行规划的实践，这通常由船舶导航员手动完成。作者展示了使用 Apache Spark（一种流行的分布式并行计算框架）实现的结果。通过将结果与在线航行计划应用程序进行比较来评估该方法。评估表明，该方法能够生成类似于现实世界海上交通网络的图表。