| 基于大数据的复杂海运环境下船舶路径自动生成方法 |
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| 引用本文： | 韩鹏,杨晓霞.基于大数据的复杂海运环境下船舶路径自动生成方法[J].海洋学报(英文版),2020,39(8):113-120. |
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| 作者姓名： | 韩鹏 杨晓霞 |
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| 作者单位： | 中国21世纪议程管理中心(ACCA21), 北京, 100038;成都理工大学地球科学学院, 成都, 610500 |
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| 摘 要： | 随着全球经济的快速发展，海上运输由于其运力大、运费低而变得更具实用性。然而，这也意味着在海上航道行驶的船只正变得越来越多，这将导致在复杂的海洋环境中航海船只发生事故的可能性会很高。据相关历史的统计，在海域中航行缺乏高精度导航数据会导致大量事故，这种累积的事故信息可以被用来提高航海的安全性。本文通过将蕴含在AIS (Automatic Identification System) 大数据中的经验导航信息挖掘出来，以辅助实现复杂海事环境下安全可靠的船舶路径的生成。本文提出了一种基于大数据自动生成船舶路径的新方法。该方法首先在大量船舶轨迹上通过DBSCAN (Density-Based Spatial Clustering of Applications with Noise) 聚类形成不同的轨迹矢量簇。然后，迭代计算轨迹矢量簇的中心线，并从这些中心线之间的节点-弧段拓扑关系来构建航道网络。最后，基于航道网络来实现船舶路径的生成，对于航道网络未覆盖的海域，则通过海洋环境风险栅格的路径规划来实现船舶路径的生成。不同海域不同AIS数据集进行的多次实验结果表明，本文提出的船舶路径生成方法是有效性。
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| 关 键 词： | 船舶航线规划 船舶自动识别系统 大数据 轨迹数据挖掘 电子海图 |
| 收稿时间： | 2019/12/19 0:00:00 |
Big data-driven automatic generation of ship route planning in complex maritime environments |
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| Han Peng,Yang Xiaoxia.Big data-driven automatic generation of ship route planning in complex maritime environments[J].Acta Oceanologica Sinica,2020,39(8):113-120. |
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| Authors: | Han Peng Yang Xiaoxia |
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| Institution: | 1.Administrative Center for China’s Agenda 21, Beijing 100038, China2.College of Earth Science, Chengdu University of Technology, Chengdu 610059, China |
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| Abstract: | With the rapid development of the global economy, maritime transportation has become much more convenient due to large capacities and low freight. However, this means the sea lanes are becoming more and more crowded, leading to high probabilities of marine accidents in complex maritime environments. According to relevant historical statistics, a large number of accidents have happened in water areas that lack high precision navigation data, which can be utilized to enhance navigation safety. The purpose of this work was to carry out ship route planning automatically, by mining historical big automatic identification system (AIS) data. It is well-known that experiential navigation information hidden in maritime big data could be automatically extracted using advanced data mining techniques; assisting in the generation of safe and reliable ship planning routes for complex maritime environments. In this paper, a novel method is proposed to construct a big data-driven framework for generating ship planning routes automatically, under varying navigation conditions. The method performs density-based spatial clustering of applications with noise first on a large number of ship trajectories to form different trajectory vector clusters. Then, it iteratively calculates its centerline in the trajectory vector cluster, and constructs the waterway network from the node-arc topology relationship among these centerlines. The generation of shipping route could be based on the waterway network and conducted by rasterizing the marine environment risks for the sea area not covered by the waterway network. Numerous experiments have been conducted on different AIS data sets in different water areas, and the experimental results have demonstrated the effectiveness of the framework of the ship route planning proposed in this paper. |
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| Keywords: | ship route planning AIS big data trajectory data mining electronic chart |
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