Volume 40 Issue 6
Dec.  2022
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Article Navigation >  Journal of Transport Information and Safety  > 2022  >  40(6): 1-11
HUANG Chen, CHEN Deshan, WU Bing, YAN Xinping. A Real-time Detection of Nautical Traffic Events: A Review and Prospect[J]. Journal of Transport Information and Safety, 2022, 40(6): 1-11. doi: 10.3963/j.jssn.1674-4861.2022.06.001
Citation: HUANG Chen, CHEN Deshan, WU Bing, YAN Xinping. A Real-time Detection of Nautical Traffic Events: A Review and Prospect[J]. Journal of Transport Information and Safety, 2022, 40(6): 1-11. doi: 10.3963/j.jssn.1674-4861.2022.06.001
shu

A Real-time Detection of Nautical Traffic Events: A Review and Prospect

doi: 10.3963/j.jssn.1674-4861.2022.06.001
  • 1.

    School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China

  • 2.

    National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China

  • 3.

    Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China

  • Received Date: 2022-06-06
    Available Online: 2023-03-27
    Abstract
  • Nautical Traffic Event Detection(NTED) methods mostly rely on offline methods using historical data, which are insufficient for real-time traffic supervision. The studies on abnormal behavior detection and incidents detection of ships are collected and investigated, and the findings are concluded as follows: from the perspective of data, the detection data rely on a single source and the environmental information is usually missing; from the perspective of methodologies, classical models that are based on statistical methods, risk assessments, etc., have high efficiency but low accuracy; while, the machine-learning based methods, such as neural networks, image recognition, etc., have high accuracy but low efficiency; and the combination of multi-source data fusion and multi-technology have become new trends.Three key technologies for the real-time NTED are summarized: ① maritime big data technologies, which process ships and environment data efficiently and standardize multi-source heterogeneous data structures, which reduces the false alarm caused by the single data source; ②dynamic behavior modeling, which uses knowledge graph or other technologies to integrate nautical contextual information, and uses deep learning, semantic association, graph neural network or other methods to develop different models for dynamic ship behaviors in different nautical context, which improves the accuracy of the NTED; ③the real-time analysis and visualization techniques combined with parallel systems, which can transfer information between the virtual and real systems, analyze the simulated results, and display the detection process which facilitates human-computer interactions in the supervision. A Nautical Traffic Event Parallel Detection System(NTEPDS) is proposed, which includes three functional modules: ①the data acquisition; ②the backend service; ③the client application. The NTEPDS can receive real-time navigation data, analyze and predict real-time traffic status, dynamically detect and report traffic events and display the simulation results. Finally, the prospects of the real-time NTED are concluded from three aspects: data fusion, traffic state perception, and traffic virtuality-reality mapping, which reveals the development directions of real-time NTED at the practical level.

     

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