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Deep Learning-Based Caution Area Traffic Prediction with Automatic Identification System Sensor Data.

Kwang-Il Kim1, Keon Myung Lee2

  • 1Department of Computer Science, Chungbuk National University, Cheongju 28644, Korea. kikim82@cbnu.ac.kr.

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Summary

Predicting ship traffic in busy harbors is crucial for safety. A new deep learning model, STENet, uses Automatic Identification Service data to forecast future vessel traffic, improving control and efficiency.

Keywords:
VTSautomatic identification data sensorconvolution neural networkdeep learningsensor datatraffic prediction

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Area of Science:

  • Maritime Safety and Operations
  • Artificial Intelligence in Transportation
  • Deep Learning for Predictive Analytics

Background:

  • Controlling ship traffic in crowded harbors is vital for safety and port efficiency.
  • Vessel Traffic Service (VTS) operators focus on high-risk areas like intersections and congestion zones.
  • Predicting future ship traffic is essential for timely risk mitigation due to ships' limited maneuverability.

Purpose of the Study:

  • To propose a novel deep neural network model, Ship Traffic Extraction Network (STENet), for predicting medium-term and long-term ship traffic in harbor caution areas.
  • To leverage Automatic Identification Service (AIS) sensor data for enhanced traffic prediction.
  • To improve the efficiency and safety of Vessel Traffic Service operations through accurate forecasting.

Main Methods:

  • Developed a hierarchical deep neural network architecture (STENet) utilizing AIS sensor data.
  • Employed a convolutional neural network for extracting ship movement features.
  • Integrated five separated fully-connected neural networks for contextual attribute feature extraction to prevent crosstalk.
  • Concatenated features from movement and contextual modules before feeding into a prediction module.

Main Results:

  • STENet demonstrated significant performance improvements on a real-world AIS dataset from Yeosu port.
  • Achieved an average of 50.65% relative performance improvement for medium-term predictions compared to the SVR benchmark.
  • Showcased an average of 57.65% relative performance improvement for long-term predictions over the SVR benchmark.
  • Outperformed other compared methods, including VGGNet-based models.

Conclusions:

  • The proposed STENet model effectively predicts medium-term and long-term ship traffic in harbor areas.
  • STENet's hierarchical architecture and feature extraction approach enhance prediction accuracy using AIS data.
  • The model offers a valuable tool for Vessel Traffic Service operators to improve maritime safety and port efficiency.