Sequential Modeling of Complex Marine Navigation: Case Study on a Passenger Vessel (Student Abstract)

• URL: http://arxiv.org/abs/2403.13909v1
• Date: Wed, 20 Mar 2024 18:29:55 GMT
• Title: Sequential Modeling of Complex Marine Navigation: Case Study on a Passenger Vessel (Student Abstract)
• Authors: Yimeng Fan, Pedram Agand, Mo Chen, Edward J. Park, Allison Kennedy, Chanwoo Bae,
• Abstract summary: This paper uses a machine learning approach to explore ways to reduce vessel fuel consumption. We leverage a real-world dataset spanning two years of a ferry in west coast Canada. Our focus centers on the creation of a time series forecasting model. It serves as an evaluative tool to assess the proficiency of the ferry's operation under the captain's guidance.
• Score: 5.253408036933116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The maritime industry's continuous commitment to sustainability has led to a dedicated exploration of methods to reduce vessel fuel consumption. This paper undertakes this challenge through a machine learning approach, leveraging a real-world dataset spanning two years of a ferry in west coast Canada. Our focus centers on the creation of a time series forecasting model given the dynamic and static states, actions, and disturbances. This model is designed to predict dynamic states based on the actions provided, subsequently serving as an evaluative tool to assess the proficiency of the ferry's operation under the captain's guidance. Additionally, it lays the foundation for future optimization algorithms, providing valuable feedback on decision-making processes. To facilitate future studies, our code is available at \url{https://github.com/pagand/model_optimze_vessel/tree/AAAI}

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