Developing an AIS Big Data-Driven Framework for Ship Emission Monitoring in Ports

Abstract

The rapid expansion of maritime transportation has significantly impacted air quality due to increased ship emissions. This study aims to develop a ship emission monitoring system utilizing Automatic Identification System (AIS) big data, with Tanjung Priok Port in Indonesia (ID TPP) as the case study. The system is designed to monitor and analyze ship emissions based on historical AIS data, providing actionable insights to mitigate environmental impacts. By integrating various data processing techniques, including data preprocessing, database development, sailing time and speed calculation, and emission estimation, this research provides a comprehensive framework for a ship emission monitoring system. The system can be implemented in ports through the development of an interactive web-based dashboard, enhancing the decision-making capabilities of port authorities and other stakeholders. The results demonstrate the system’s potential for effectively monitoring emissions and promoting sustainable maritime operations.