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Expected by: 01 February 2023
Applications of Machine Learning and Data Analytics Models in Maritime Transportation

Applications of Machine Learning and Data Analytics Models in Maritime Transportation

by Ran Yan, Shuaian Wang

Machine learning and data analytics can be used to inform technical, commercial and financial decisions in the maritime industry. Applications of Machine Learning and Data Analytics Models in Maritime Transportation explores the fundamental principles of analysing maritime-transportation related practical problems using data-driven models, with a particular focus on machine learning.

Data-enabled methodologies, technologies, and applications in maritime transportation are clearly and concisely explained, and case studies of typical maritime challenges and solutions are also included. The authors begin with an introduction to maritime transportation, followed by chapters providing an overview of ship inspection by port state control, and the principles of data driven models. Further chapters cover linear regression models, Bayesian networks, support vector machines, artificial neural networks, tree-based models, association rule learning, cluster analysis, classic and emerging approaches to solving practical problems in maritime transport, incorporating shipping domain knowledge into data-driven models, explanation of black-box ML models in maritime transport, linear optimization, advanced linear optimization, and integer optimization. A concluding chapter provides an overview of coverage and explores future possibilities in the field.

The book will be especially useful to researchers and professionals with existing expertise in maritime research who wish to learn how to apply data analytics and machine learning to their field.

About the Author

Ran Yan is a research assistant professor at The Hong Kong Polytechnic University, China. Her research interests include applying data analytics methods and technologies to improve shipping efficiency and green shipping management. Her research has been published in Transportation Research Part B/C/E, Transport Policy, Journal of Computational Science, Maritime Policy & Management, Ocean Engineering, Engineering, Sustainability, and Electronic Research Archive.

Shuaian Wang is a professor at The Hong Kong Polytechnic University, China. His research interests include big data in shipping, green shipping, and shipping operations management. He has published over 200 papers in international journals. He serves as an editor-in-chief of Cleaner Logistics and Supply Chain and Communications in Transportation Research.



Item Subjects:
Transportation

Publication Year: 2023

Pages: 300

ISBN-13: 978-1-83953-559-8

Format: HBK

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