基于大规模船舶轨迹数据的航道边界提取方法

doi:10.11772/j.issn.1001-9081.2018071739

计算机应用 ›› 2019, Vol. 39 ›› Issue (1): 105-112.DOI: 10.11772/j.issn.1001-9081.2018071739

• 2018年全国开放式分布与并行计算学术年会(DPCS 2018)论文 • 上一篇下一篇

基于大规模船舶轨迹数据的航道边界提取方法

徐垚¹, 李卓然^2,3, 孟金龙^2,3, 赵利坡¹, 温建新¹, 王桂玲^2,3

1. 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;
2. 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;
3. 北方工业大学计算机学院, 北京 100144

收稿日期:2018-07-19 修回日期:2018-08-29 发布日期:2019-01-21 出版日期:2019-01-10
通讯作者: 李卓然
作者简介: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;李卓然(1994-),男,河南周口人,硕士研究生,主要研究方向:大规模流数据处理与分析;孟金龙(1994-),男,甘肃酒泉人,硕士研究生,主要研究方向:数据处理、软件服务;赵利坡(1984-),男,河北邢台人,工程师,硕士,主要研究方向:海洋大数据分析;温建新(1986-),男,山东菏泽人,工程师,硕士,主要研究方向:信息融合、海洋大数据分析;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。
基金资助:
国家自然科学基金资助项目（61832004，61672042）；北京市自然科学基金资助项目（4172018）；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）。

Extraction method of marine lane boundary from exploiting trajectory big data

XU Yao¹, LI Zhuoran^2,3, MENG Jinlong^2,3, ZHAO Lipo¹, WEN Jianxin¹, WANG Guiling^2,3

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

Received:2018-07-19 Revised:2018-08-29 Online:2019-01-21 Published:2019-01-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61832004, 61672042), the Natural Science Foundation of Beijing (4172018), the Cooperative Project of CETC Ocean Corporation with Universities (402054841879), the Yuyou Team Training Program of North China University of Technology (107051360018XN012/020).

摘要/Abstract

摘要： 传统的道路数据获取方法成本高、更新慢等无法适用于海洋航道的获取，从众源轨迹数据中提取道路或航道信息具有成本低、更新快等特性，然而，由于船舶轨迹数据噪声多、数据量大、不同区域分布不均使得航道边界提取面临较大挑战。针对该问题，提出一种基于大规模船舶轨迹数据进行航道边界提取的方法。首先对大规模的船舶轨迹数据进行并行化去噪、插值、轨迹分段；然后，基于并行化及基于Geohash编码的空间聚类，将轨迹数据化简为多个方形区域的点集数据；其次，对其进行窗口划分，对传统的NiBlack方法进行扩展，提出SpatialNiBlack算法，对方形区域进行航道识别；最后，提出一种新的提取算法del-alpha-shape，基于航道识别结果获得航道边界。理论分析与实验结果表明，所提方法在最大密度值是200，最小密度值是10，窗口长和宽分别为5和5时，可同时达到86.7%的准确率和79.4%的召回率。实验结果表明，该方法可以从大规模的轨迹数据中提取有价值的航道边界，是一种有效的航道提取方法。

关键词: 轨迹数据, 自动识别系统, 时空大数据, Delaunay三角网, 航道提取

Abstract: The traditional road information extraction method is high-cost and slow-update. Compared with it, road or marine lane information extraction from crowdsourcing trajectory data is low-cost and easier to update. However, it is difficult to extract lane boundary due to vessel trajectory data with high noise, large data volume and uneven distribution across different regions. To solve this problem, an extraction method of marine lane boundary from exploiting trajectory big data was proposed. Firstly, the parallelized denoising, interpolation and trajectory segmentation for trajectory big data was conducted. Then, based on parallelization and Geohash-encoded spatial clustering, trajectory data was simplified into multiple square regions. The regions were divided and the NiBlack method was extended as SpatialNiBlack algorithm to recognize regions on lane. Finally, based on the filtering results, del-alpha-shape algorithm was proposed to construct a Delaunay triangulation network and obtain marine lane boundary. The theoretical analysis and experimental results show that the proposed method can achieve an accuracy of 86.7% and a recall rate of 79.4% when the maximum density value is 200, minimum density value is 10, length and width of window are 5 and 5 respectively. The experimental results show that the proposed method is effective to extract valuable marine lane boundaries from large-scale trajectory data.

Key words: trajectory data, Automatic Identification System (AIS), spatio-temporal big data, Delaunay triangulation network, marine lane extraction

中图分类号:

TP319

徐垚, 李卓然, 孟金龙, 赵利坡, 温建新, 王桂玲. 基于大规模船舶轨迹数据的航道边界提取方法[J]. 计算机应用, 2019, 39(1): 105-112.

XU Yao, LI Zhuoran, MENG Jinlong, ZHAO Lipo, WEN Jianxin, WANG Guiling. Extraction method of marine lane boundary from exploiting trajectory big data[J]. Journal of Computer Applications, 2019, 39(1): 105-112.

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基于大规模船舶轨迹数据的航道边界提取方法

Extraction method of marine lane boundary from exploiting trajectory big data

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