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Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

机译：使用进化算法从AIS数据中提取海上业务网络

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摘要

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.
机译：该方法从AIS数据重建网络（曲线图），该数据反映了血管流量并且可用于路线规划。该方法由三个主要步骤组成：机动点检测，航点发现和边缘结构。机动点检测使用Cusum方法并减少进一步处理的数据量。具有空间分区的遗传算法用于WayPoints Discovery。最后，连接这些航点的边缘形成最终的海运流量网络。该方法旨在推进海上航行规划的实践，该规划通常由船舶导航官手动完成。作者展示了使用Apache Spark，流行分布式和并行计算框架实现的实现结果。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，该方法具有生成类似于现实世界海运业务网络的图形的能力。

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