This book provides a comprehensive and application-oriented introduction to data science, machine learning, and deep learning methods for maritime transportation. It is written to help readers understand how maritime data can be processed, analyzed, modeled, and used to support digitalization for shipping industry. The book is organized into two parts. Part I, Fundamentals and Concepts, introduces the theoretical and methodological foundations required for maritime data science. It begins with maritime transportation data sources, structures, and characteristics, followed by essential data preprocessing techniques. It then presents methods for vessel trajectory representation, transformation, and analysis. The part also introduces the fundamental concepts of machine learning and deep learning, with a focus on their relevance to maritime applications. The final chapter of this part provides background knowledge on vessel maneuvering behavior and motion dynamics, establishing a bridge between physical understanding and data-driven modeling. Part II, Practical Applications and Case Studies, focuses on representative real-world problems in maritime data science and intelligent shipping. It covers trajectory clustering and pattern mining, deep learning-based vessel trajectory forecasting, anomaly detection using reconstruction methods, data-driven and physics-informed modeling of vessel propulsion power, regional ocean wave prediction, maritime traffic flow forecasting using graph neural networks, and vessel estimated time of arrival (ETA) prediction using machine learning. Each application chapter is designed as a self-contained case study that combines problem formulation, modeling methodology, implementation details, and practical interpretation. The book is suitable for graduate students, researchers, and practitioners in academia and industry. It can be used both as a structured textbook and as a practical reference for self-study. With hands-on Python examples and source code provided through the author’s GitHub repository, the book enables readers to reproduce key methods, understand maritime data characteristics, select appropriate modeling approaches, and develop data-driven solutions for real operational scenarios in maritime transportation.

Dr. Liang Zhao received his Ph.D. degree in Civil Engineering from Zhejiang University and his bachelor’s degree from Central South University, China. He was a Visiting Researcher in Maritime Studies at Nanyang Technological University, Singapore. He is currently a Research Fellow at Zhejiang University. 

Dr. Zhao has more than seven years of research experience in Maritime Autonomous Surface Ships (MASS), maritime data science, and intelligent shipping management. He has participated as a core researcher in six industry- and government-funded projects related to MASS, green shipping corridors, and artificial intelligence. He has co-authored more than 30 peer-reviewed journal articles and has authored two books, including Data Science for Maritime Transportation (Springer) and Autonomous Marine Vehicles: Planning and Control (Wiley & Scrivener). Dr. Zhao also serves as an Associate Editor of Ships and Offshore Structures, and as a Guest Editor for the Journal of Marine Science and Engineering and Frontiers in Marine Science. His current research focuses on artificial intelligence for maritime transportation, maritime autonomous surface ships, and spatio-temporal data analytics.