Theses and Dissertations

Date of Award

8-1-2024

Document Type

Thesis

Degree Name

Master of Science (MS)

First Advisor

Hansheng Lei

Second Advisor

Liyu Zhang

Third Advisor

Jorge Castillo

Abstract

The maritime industry relies on the Automated Identification System (AIS) for real-time vessel tracking and navigation. This thesis focuses on detecting vessel delays and mining delay patterns from AIS data, which are crucial for improving shipping schedules, port operations, and global trade logistics. The research begins with rigorous data preprocessing, including noise reduction, data interpolation, and anomaly detection, to ensure high-quality AIS data as well as focuses on detecting vessel delays and mining delay patterns from AIS data to enhance maritime operations. Advanced machine learning models analyze temporal and spatial features to detect delays, considering vessel type, speed, and route. Pattern mining techniques, such a time-series analysis, identify recurring delay patterns and their causes, including weather conditions, port congestion, and navigational challenges. The findings offer actionable insights for maritime stakeholders, improving decision-making and operational efficiency. The developed methodologies can be integrated into existing monitoring systems for real-time delay predictions, contributing to the optimization of global maritime logistics.

Comments

Copyright 2024 Naznin Ara. https://proquest.com/docview/3115240381

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