Ship trajectory extraction method for port parking area identification

doi:10.11772/j.issn.1001-9081.2018071625

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (1): 113-117.DOI: 10.11772/j.issn.1001-9081.2018071625

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Ship trajectory extraction method for port parking area identification

ZHENG Zhentao^1,2, ZHAO Zhuofeng¹, WANG Guiling¹, XU Yao²

1. Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data(North China University of Technology), Beijing 100144, China;
2. Shore-based Information System Department, Ocean Information Technology Research Institute Co., Ltd, China Electronics Technology Group Corporation(CETC Ocean Corp.), Beijing 100041, China

Received:2018-07-19 Revised:2018-08-28 Online:2019-01-10 Published:2019-01-21
Supported by:
This work is partially supported by the Natural Science Foundation of Beijing (4172018, 4162021), the Cooperation Project of CETC Ocean Corporation with Universities (402054841879), the Yuyou Team Training Program of North China University of Technology (107051360018XN012/020).

面向港口停留区域识别的船舶停留轨迹提取方法

郑振涛^1,2, 赵卓峰¹, 王桂玲¹, 徐垚²

1. 大规模流数据集成与分析技术北京市重点实验室(北方工业大学), 北京 100144;
2. 中电科海洋信息技术研究院有限公司岸基信息系统部, 北京 100041

通讯作者: 郑振涛
作者简介:郑振涛(1992-),男,河南周口人,硕士研究生,主要研究方向:大规模流数据集成与分析、服务计算;赵卓峰(1977-),男,山东济南人,研究员,博士,CCF会员,主要研究方向:大规模流数据集成与分析、服务计算;王桂玲(1978-),女,山东成武人,副研究员,博士,CCF会员,主要研究方向:大规模流数据集成与分析、服务计算;徐垚(1980-),男,江苏东台人,高级工程师,硕士,主要研究方向:数据融合、海洋大数据分析挖掘、海洋GIS应用。
基金资助:
北京市自然科学基金资助项目（4172018，4162021）；中电科海洋信息技术研究院有限公司高校合作课题项目（402054841879）；北方工业大学毓优团队培养计划项目（107051360018XN012/020）。

Abstract

Abstract: Ship trajectory data shows the characteristics of low precision, sparseness and trajectory drift for the port parking area recognition. To improve the accuracy of port parking area recognition based on ship trajectory big data, a Multi-constrained and Parallel Track Stay Segment Extraction (MPTSSE) method was proposed. Firstly, the definition of stay segment based on ship trajectory data was given as a basic concept for parking area identification. Secondly, a stay segment extraction model based on multiple constraints, such as speed, time difference, dwell time and distance, was introduced. Furthermore, a parallel trajectory stay segment extraction algorithm was proposed. Finally, Hadoop framework was adopted to implement the proposed algorithm. In comparison experiments with the trajectory stay segment extraction method based on Stop/Move model based on real ship trajectory big dataset, the accuracy of MPTSSE is increased by 22% in berth recognition of three ports. The MPTSSE method can effectively avoid misdivision of track stay segment and has better execution efficiency under large-scale ship trajectory dataset.

Key words: port stay area, ship trajectory data, trajectory stay trajectory, multi-constrained extraction, Hadoop framework

摘要： 针对港口停留区域识别时船舶轨迹大数据的精度低、稀疏、漂移等问题，提出了一种多约束条件下的船舶停留轨迹提取（MPTSSE）方法。首先，结合船舶轨迹数据特点，给出了用于停留区域识别与提取的停留段概念的定义；其次，建立了基于速度、时间差、停留时长、距离等多约束的轨迹停留段提取模型和并行化轨迹停留段提取算法；最后，基于Hadoop框架给出了船舶轨迹大数据集上的轨迹停留段提取算法实现。基于真实船舶轨迹数据的实验结果表明，与基于Stop/Move模型的轨迹停留提取方法相比，MPTSSE方法在三个港口泊位的提取中准确率提高了22%。MPTSSE方法能有效避免轨迹停留段误分割情况，同时在大规模船舶轨迹数据下具有较高的执行效率。

关键词: 港口停留区域, 船舶轨迹数据, 停留轨迹, 多约束提取, Hadoop框架

CLC Number:

TP312

ZHENG Zhentao, ZHAO Zhuofeng, WANG Guiling, XU Yao. Ship trajectory extraction method for port parking area identification[J]. Journal of Computer Applications, 2019, 39(1): 113-117.

郑振涛, 赵卓峰, 王桂玲, 徐垚. 面向港口停留区域识别的船舶停留轨迹提取方法[J]. 计算机应用, 2019, 39(1): 113-117.

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Ship trajectory extraction method for port parking area identification

面向港口停留区域识别的船舶停留轨迹提取方法

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