.png){kind=icon}
返回
Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm
.png){kind=icon}
OA
AI
DOI：10.1007/s12599-020-00661-0.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Maritime traffic network
Vessel routing
Route planning
AIS
Maritime traffic graph
Waypoint discovery
Graph discovery
Artificial intelligence
Genetic algorithm
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.4
论文数:
862
被引数:
4.0K
机构
引用论文
Maritime Traffic Networks: From Historical Positioning Data to Unsupervised Maritime Traffic Monitoring海上交通网络: 从历史定位数据到无人监督的海上交通监控
PREAI
分享收藏Complete genome sequence of an isolate of Potato virus X (PVX) infecting Cape gooseberry (Physalis peruviana) in Colombia.png)
Virus Genes
Virus GenesIF0
PREAI
分享收藏分享收藏
查看更多内容