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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Data-Driven Analysis for Safe Ship Operation in Ports Using Quantile Regression Based on Generalized Additive Models

Hyeong-Tak Lee1, Hyun Yang2, Ik-Soon Cho3

  • 1Ocean Science and Technology School, Korea Maritime and Ocean University, Busan 49112, Korea.

Sensors (Basel, Switzerland)
|December 28, 2021
PubMed
Summary

This study introduces advanced methods for analyzing ship trajectories to create safer port operations. The findings support developing quantitative guidelines for ship navigation in ports, enhancing maritime safety.

Keywords:
automatic information systemdeep learningdeep neural networkgeneralized additive modelsquantile regressionsafe ship operationship trajectories

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Area of Science:

  • Maritime Safety
  • Naval Architecture
  • Data Science

Background:

  • Marine accidents in ports pose significant risks to life, property, and the environment.
  • A recent pier collision in Busan New Port highlighted the need for improved safety guidelines.
  • Existing ship trajectory data analysis methods struggle with the variability and uncertainty of navigation data.

Purpose of the Study:

  • To develop quantitative, data-driven guidelines for safe ship operations within ports.
  • To address the limitations of traditional regression analysis in handling complex ship trajectory data.
  • To leverage advanced statistical and deep learning models for enhanced maritime safety analysis.

Main Methods:

  • Utilized ship trajectory data from the Automatic Information System (AIS).
  • Applied quantile regression with two models: Generalized Additive Models (GAMs) and neural networks (deep learning).
  • Analyzed key navigation parameters: speed over ground, course over ground, and ship's position.

Main Results:

  • Quantile regression models effectively analyzed variable and uncertain ship trajectory data.
  • The study identified patterns and parameters crucial for safe ship navigation in port environments.
  • Model performance was evaluated using quantile loss, demonstrating analytical robustness.

Conclusions:

  • The findings enable the proposal of evidence-based safe operation guidelines for ship position, speed, and course.
  • This research provides a foundation for developing future manuals for autonomous ship operations in ports.
  • Enhanced analysis of ship trajectory data is critical for improving maritime safety and port efficiency.