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MaritimeDS: a data service framework for unsupervised maritime traffic monitoring based on trajectory big data

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Abstract

The large maritime traffic volume and its great impact on economy, environment and security make an unsupervised maritime traffic monitoring system in great need. Most of the current related research focuses on intelligent analysis or trajectory mining algorithms, while ignoring how to make good use of domain knowledge and model flexible software components, which leads to the related business functions implemented by IT staff through ad hoc coding. And what’s more, the lack of rich and detailed road information in the maritime transportation field makes it challenging to carry out diverse maritime monitoring business. This paper proposes a data service framework for unsupervised maritime traffic monitoring named MaritimeDS. The framework provides a unified domain model, in which the vessel trajectories are modeled through a layer-by-layer data model and maritime traffic structure is modeled as a spatial model. The highest layer is semantic trajectory, which is modeled based on the traffic structure generated through a novel T2I-CycleGAN model-based trajectory analysis service solving the problem of lacking paired training sets in maritime data. Case study and experiments show that compared with similar work, in the absence of detailed road information, IT staff can build maritime traffic structure, and carry out unified modeling and implementation of business functions related to traffic monitoring on this basis, which can improve development efficiency.

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Notes

  1. https://www.github.com/yxk121/MaritimeDS/.

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Authors and Affiliations

  1. School of Information Science and Technology, North China University of Technology, Beijing, 100144, China

    Xuankai Yang, Guiling Wang & Jing Gao

  2. Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, North China University of Technology, Beijing, 100144, China

    Xuankai Yang, Guiling Wang & Jing Gao

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  1. Xuankai Yang
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  2. Guiling Wang
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  3. Jing Gao
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Correspondence to Guiling Wang.

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This work is supported by National Natural Science Foundation of China (Grant no. 61832004) and Projects of International Cooperation and Exchanges NSFC (Grant no. 62061136006).

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Yang, X., Wang, G. & Gao, J. MaritimeDS: a data service framework for unsupervised maritime traffic monitoring based on trajectory big data. J Reliable Intell Environ 8, 3–19 (2022). https://doi.org/10.1007/s40860-021-00163-0

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  • DOI: https://doi.org/10.1007/s40860-021-00163-0

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