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Adaptive Extraction and Refinement of Marine Lanes from Crowdsourced Trajectory Data

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Abstract

Crowdsourced trajectory data of ships provide the opportunity for extracting marine lane information. However, extracting useful knowledge from massive amounts of trajectory data is a challenging problem. Trajectory data collected from crowdsourcing can be extremely diverse in different areas and its quality might be very low. Moreover, the density distribution of the crowdsourced trajectory points is quite uneven in different areas. Furthermore, it is necessary to extract marine lanes with high extraction precision in offshore and nearshore water areas, but extraction precision can be lower in the open sea. We propose an adaptive approach for marine lane extraction and refinement based on grid merging and filtering to meet the challenges. In this paper, after pre-processing and clustering the trajectory data based on the density value of grids with a parallel GeoHash encoding algorithm, we propose a parallel grid merging and filtering algorithm based on a QuadTree data structure. The algorithm performs grid merging on the simplified grid data according to the density value of grid, then filters the merged grid data based on a local sliding window mechanism to get the marine lane grid data. Applying the Delaunay Triangulation on the marine lane grid data, the marine lane boundary information can be extracted with adaptive extraction precision. Experimental results show that the proposed approach can extract marine lanes with high extraction precision in offshore and nearshore water area and low extraction precision in open sea area.

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Acknowledgments

This work is supported by the National Key Research and Development Program of China No.2018YFB1402500, Beijing Natural Science Foundation No.4172018, National Natural Science Foundation of China No.61832004, No. 61672042, and University Cooperation Projects Foundation of CETC Ocean Corp.

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Authors and Affiliations

  1. Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, School of Information Science and Technology, North China University of Technology, No. 5 Jinyuanzhuang Road, Shijingshan District, Beijing, China

    Guiling Wang

  2. Ocean Information Technology Company, China Electronics Technology Group Corporation (CETC Ocean Corp.), No. 11 Shuangyuan Road, Badachu Hi-Tech Park, Shijingshan District, Beijing, China

    Guiling Wang

  3. Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, North China University of Technology, No.5 Jinyuanzhuang Road, Shijingshan District, Beijing, China

    Jinlong Meng, Zhuoran Li, Weilong Ding & Yanbo Han

  4. paluno - The Ruhr Institute for Software Technology, University of Duisburg-Essen, Schützenbahn 70, 45127, Essen, Germany

    Marc Hesenius & Volker Gruhn

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  1. Guiling Wang
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  2. Jinlong Meng
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  3. Zhuoran Li
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  5. Weilong Ding
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  6. Yanbo Han
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  7. Volker Gruhn
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Correspondence to Guiling Wang.

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We thank Ole Meyer for his help in reviewing this paper.

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Wang, G., Meng, J., Li, Z. et al. Adaptive Extraction and Refinement of Marine Lanes from Crowdsourced Trajectory Data. Mobile Netw Appl 25, 1392–1404 (2020). https://doi.org/10.1007/s11036-019-01454-w

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