Overview

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 and operations research models. 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 machine learning 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 expertise in maritime research who wish to learn how to apply data analytics and machine learning to their fields.

Full Product Details

Author:   Ran Yan (Research Assistant Professor, The Hong Kong Polytechnic University, Department of Logistics and Maritime Studies, China) ,  Shuaian Wang (Professor, The Hong Kong Polytechnic University, Department of Logistics and Maritime Studies, China)
Publisher:   Institution of Engineering and Technology
Imprint:   Institution of Engineering and Technology
ISBN:  

9781839535598

ISBN 10:   1839535598
Pages:   319
Publication Date:   15 February 2023
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Chapter 1: Introduction of maritime transportation Chapter 2: Ship inspection by port state control Chapter 3: Introduction to data-driven models Chapter 4: Key elements of data-driven models Chapter 5: Linear regression models Chapter 6: Bayesian networks Chapter 7: Support vector machine Chapter 8: Artificial neural network Chapter 9: Tree-based models Chapter 10: Association rule learning Chapter 11: Cluster analysis Chapter 12: Classic and emerging approaches to solving practical problems in maritime transport Chapter 13: Incorporating shipping domain knowledge into data-driven models Chapter 14: Explanation of black-box ML models in maritime transport Chapter 15: Linear optimization Chapter 16: Advanced linear optimization Chapter 17: Integer optimization Chapter 18: Conclusion

Reviews

Author Information

Ran Yan is a research assistant professor in the Department of Logistics and Maritime Studies at The Hong Kong Polytechnic University (PolyU), China. Dr. Yan received her Bachelor of Science degree from Hohai University in China in 2018 and her Master of Philosophy and Doctor of Philosophy degrees from The Hong Kong Polytechnic University in 2020 and 2022, respectively. Dr. Yan's research interests include applying data analytics methods and technologies to improve shipping efficiency and green shipping management. Dr. Yan has published more than 30 papers in international journals and conference proceedings, such as Transportation Research Part B/C/E, Transport Policy, Journal of Computational Science, Maritime Policy & Management, Ocean Engineering, Engineering, Sustainability, and Electronic Research Archive, and won several times of best paper/student paper award from international conferences. Dr. Yan is an editorial assistant of Cleaner Logistics and Supply Chain. Shuaian Wang is currently Professor at The Hong Kong Polytechnic University (PolyU), China. Prior to joining PolyU, he worked as a faculty member at Old Dominion University, USA, and the University of Wollongong, Australia. Dr. Wang's research interests include big data in shipping, green shipping, shipping operations management, port planning and operations, urban transport network modeling, and logistics and supply chain management. Dr. Wang has published over 200 papers in journals such as Transportation Research Part B, Transportation Science, and Operations Research. Dr. Wang is an editor-in-chief of Cleaner Logistics and Supply Chain and Communications in Transportation Research, an associate editor of Transportation Research Part E, Flexible Services and Manufacturing Journal, Transportmetrica A, and Transportation Letters, a handle editor of Transportation Research Record, an editorial board editor of Transportation Research Part B, and an editorial board member of Maritime Transport Research. Dr. Wang dedicates to rethinking and proposing innovative solutions to improve the efficiency of maritime and urban transportation systems, to promote environmental friendly and sustainable practices, and to transform business and engineering education.

Tab Content 6

Author Website:  

Customer Reviews

Recent Reviews

No review item found!

Add your own review!

Countries Available

All regions