基于船舶轨迹数据的时空共现模式挖掘算法

IF 2.1 4区 工程技术 Advances in Mechanical Engineering Pub Date : 2024-09-10 DOI:10.1177/16878132241274449
Chengxu Feng, Jianghu Xu, Jianqiang Zhang, Houpu Li
{"title":"基于船舶轨迹数据的时空共现模式挖掘算法","authors":"Chengxu Feng, Jianghu Xu, Jianqiang Zhang, Houpu Li","doi":"10.1177/16878132241274449","DOIUrl":null,"url":null,"abstract":"Finding the potential spatiotemporal co-occurrence behavior patterns of large groups of ships while sailing is a challenging problem of great importance in many real-world applications. Through spatiotemporal data mining of ship trajectory data, route rules, navigation behavior, and potential anomalies can be mined, providing important support for maritime management, navigation safety, and emergency response. With the analysis and mining of ship trajectory data in some hotspot sea areas, this paper introduced a ship spatiotemporal co-occurrence pattern mining algorithm based on association rules. Based on the research of data model and the judgment criterion of spatio-temporal co-occurrence law, such concepts as candidate set, frequency set, and instance set are introduced together with the key procedure of algorithm, including pruning and pasting of candidate sets, screening of instance sets, definition of association reasoning, and association rule mining. Subsequently, the process of implementing the spatiotemporal co-occurrence pattern mining algorithm is devised. In the end, the algorithm is verified by taking the automatic identification system data of ships in hotspot sea areas as the source data. The proposed algorithm can find several ship combinations with spatiotemporal co-occurrence regularity in these hotspot sea areas, and the association rules on the co-occurrence of several ships. The performance of the proposed algorithms is illustrated on a real-world ship trajectory database and made a detailed comparative analysis. The results are very promising in terms of computational time. The experimental results show that our algorithm can effectively identify the motion patterns and behavior characteristics of ships, which provides an important reference and support for Marine traffic management, ship safety and Marine environment protection. The research results of this paper are of great significance for improving the efficiency and safety of maritime traffic, and also provide new ideas and methods for further research in related fields.","PeriodicalId":7357,"journal":{"name":"Advances in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A spatiotemporal co-occurrence pattern mining algorithm based on ship trajectory data\",\"authors\":\"Chengxu Feng, Jianghu Xu, Jianqiang Zhang, Houpu Li\",\"doi\":\"10.1177/16878132241274449\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Finding the potential spatiotemporal co-occurrence behavior patterns of large groups of ships while sailing is a challenging problem of great importance in many real-world applications. Through spatiotemporal data mining of ship trajectory data, route rules, navigation behavior, and potential anomalies can be mined, providing important support for maritime management, navigation safety, and emergency response. With the analysis and mining of ship trajectory data in some hotspot sea areas, this paper introduced a ship spatiotemporal co-occurrence pattern mining algorithm based on association rules. Based on the research of data model and the judgment criterion of spatio-temporal co-occurrence law, such concepts as candidate set, frequency set, and instance set are introduced together with the key procedure of algorithm, including pruning and pasting of candidate sets, screening of instance sets, definition of association reasoning, and association rule mining. Subsequently, the process of implementing the spatiotemporal co-occurrence pattern mining algorithm is devised. In the end, the algorithm is verified by taking the automatic identification system data of ships in hotspot sea areas as the source data. The proposed algorithm can find several ship combinations with spatiotemporal co-occurrence regularity in these hotspot sea areas, and the association rules on the co-occurrence of several ships. The performance of the proposed algorithms is illustrated on a real-world ship trajectory database and made a detailed comparative analysis. The results are very promising in terms of computational time. The experimental results show that our algorithm can effectively identify the motion patterns and behavior characteristics of ships, which provides an important reference and support for Marine traffic management, ship safety and Marine environment protection. The research results of this paper are of great significance for improving the efficiency and safety of maritime traffic, and also provide new ideas and methods for further research in related fields.\",\"PeriodicalId\":7357,\"journal\":{\"name\":\"Advances in Mechanical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Mechanical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1177/16878132241274449\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Mechanical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/16878132241274449","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

寻找大型船舶群航行时的潜在时空共现行为模式是一个具有挑战性的问题,在现实世界的许多应用中具有重要意义。通过对船舶轨迹数据的时空数据挖掘,可以挖掘出航线规则、航行行为和潜在异常,为海事管理、航行安全和应急响应提供重要支持。结合对部分热点海域船舶轨迹数据的分析与挖掘,本文介绍了一种基于关联规则的船舶时空共现模式挖掘算法。在研究数据模型和时空共现规律判断标准的基础上,引入了候选集、频率集、实例集等概念和算法的关键过程,包括候选集的剪枝和粘贴、实例集的筛选、关联推理的定义和关联规则挖掘。随后,设计了时空共现模式挖掘算法的实现过程。最后,以热点海域船舶自动识别系统数据为源数据,对算法进行了验证。所提出的算法可以找到热点海域中具有时空共现规律性的几种船舶组合,以及几种船舶共现的关联规则。在实际船舶轨迹数据库中对所提算法的性能进行了说明,并做了详细的对比分析。在计算时间方面,结果非常令人满意。实验结果表明,我们的算法能有效识别船舶的运动模式和行为特征,为海洋交通管理、船舶安全和海洋环境保护提供了重要的参考和支持。本文的研究成果对于提高海上交通的效率和安全性具有重要意义,同时也为相关领域的进一步研究提供了新的思路和方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A spatiotemporal co-occurrence pattern mining algorithm based on ship trajectory data
Finding the potential spatiotemporal co-occurrence behavior patterns of large groups of ships while sailing is a challenging problem of great importance in many real-world applications. Through spatiotemporal data mining of ship trajectory data, route rules, navigation behavior, and potential anomalies can be mined, providing important support for maritime management, navigation safety, and emergency response. With the analysis and mining of ship trajectory data in some hotspot sea areas, this paper introduced a ship spatiotemporal co-occurrence pattern mining algorithm based on association rules. Based on the research of data model and the judgment criterion of spatio-temporal co-occurrence law, such concepts as candidate set, frequency set, and instance set are introduced together with the key procedure of algorithm, including pruning and pasting of candidate sets, screening of instance sets, definition of association reasoning, and association rule mining. Subsequently, the process of implementing the spatiotemporal co-occurrence pattern mining algorithm is devised. In the end, the algorithm is verified by taking the automatic identification system data of ships in hotspot sea areas as the source data. The proposed algorithm can find several ship combinations with spatiotemporal co-occurrence regularity in these hotspot sea areas, and the association rules on the co-occurrence of several ships. The performance of the proposed algorithms is illustrated on a real-world ship trajectory database and made a detailed comparative analysis. The results are very promising in terms of computational time. The experimental results show that our algorithm can effectively identify the motion patterns and behavior characteristics of ships, which provides an important reference and support for Marine traffic management, ship safety and Marine environment protection. The research results of this paper are of great significance for improving the efficiency and safety of maritime traffic, and also provide new ideas and methods for further research in related fields.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Advances in Mechanical Engineering
Advances in Mechanical Engineering Engineering-Mechanical Engineering
自引率
4.80%
发文量
353
期刊介绍: Advances in Mechanical Engineering (AIME) is a JCR Ranked, peer-reviewed, open access journal which publishes a wide range of original research and review articles. The journal Editorial Board welcomes manuscripts in both fundamental and applied research areas, and encourages submissions which contribute novel and innovative insights to the field of mechanical engineering
期刊最新文献
Influence of urea solution condition on NOx reduction in marine diesel engines Characteristics of deploying longitudinal folding wings with compound actuation Research on the service life of bearings in the gearbox of rolling mill transmission system under non-steady lubrication state Research and application of a coupled wheel-track off-road robot based on separate track structure Research on energy consumption evaluation and energy-saving design of cranes in service based on structure-mechanism coupling
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1