Speaker
Description
As maritime activities continue to play a pivotal role in global trade, concerns over ship emissions' environmental impact have intensified. This study presents a comprehensive analysis of ship emissions in Strait of Malacca and Singapore (SOMAS), employing Automatic Identification System (AIS) data. By harnessing the rich AIS dataset, the current emissions landscape and key emission hotspots were identified. To envision a sustainable maritime future, we simulated some possible scenarios around the region combined with predictive analytical techniques to project the future conditions of ship emission. By analyzing the conditions in each scenario, essentials for shaping intelligent systems for efficient maritime traffic can be discovered. Our analysis considers evolving factors such as ship routes, operational modes, and traffic patterns. The results provide insights for policymakers, industry stakeholders, and environmental planners seeking to mitigate the local maritime sector's carbon footprint. This study signified the value of AIS data-driven approach to facilitate regional strategist in confronting resolutions for greener maritime operation, aligning with the transition to intelligent and sustainable practices in the maritime industry within the SOMAS.
Keywords – AIS data-driven, SOMAS, Predictive analytics, Smart shipping
| Conference Topic Areas | Track7: Smart Operations and Maintenance |
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