Shipping and Logistics Analytics using Big Data

Automatic Identification System Data
1. Auflage 2026
ISBN: 978-1-032-50534-3
Verlag: Taylor & Francis

Buch, Englisch, 488 Seiten, Format (B × H): 174 mm x 246 mm, Gewicht: 453 g

With the advancement of information and communication technology, a data-driven approach applying the tools and methodologies of big data analytics has become quite common across industry. In maritime shipping, automatic identification system (AIS) vessel movement data is reliable and improving and has been used for the detection of suspicious vessels and to support vessel navigation. However, since detailed cargo items and loading/unloading conditions cannot be directly obtained from AIS data, research using this data in the fields of logistics, shipping, and shipbuilding is still limited. This book introduces the state of the art on the worldwide use of AIS and other maritime big data for shipping and logistics analysis, with data-oriented case studies, for the maritime and related industries and for university use in maritime economics, industrial engineering, and transport engineering.

Zielgruppe

Postgraduate and Professional

Autoren/Hrsg.

Shibasaki, Ryuichi

Yang, Dong

Bai, Xiwen

Jia, Haiying

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Produktionsmanagement, Qualitätskontrolle
• Mathematik | Informatik Mathematik Operations Research
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Technische Wissenschaften Verkehrstechnik | Transportgewerbe Verkehrstechnologie: Allgemeines
• Technische Wissenschaften Bauingenieurwesen Verkehrsingenieurwesen, Verkehrsplanung
• Technische Wissenschaften Bauingenieurwesen Bauingenieurwesen
• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Einkauf, Logistik, Supply-Chain-Management

Inhaltsverzeichnis

Part 1. Introduction to Maritime Big Data  1: General Introduction to Maritime Shipping using Maritime Trade Statistics 2: AIS Data and its Characteristics 3: VHF Data Exchange System: Maritime Big Data for the Next Generation 4: Combining AIS Data with Multiple Related Data Sources for Better Decision Making  Part 2. Shipping Network Analysis using Maritime Big Data  5: Vessel Navigation Network Development Using Trajectory Data 6: Global Liner Shipping Network Connectivity Analysis 7: Global Vessel Routing Analysis Focusing on the Suez Canal 8: Global Liner Shipping Network Resilience Analysis 9: AIS Derived Fleet Productivity and the Case of the Russia-Ukraine War 10: Maritime Disruption Analysis Focusing on Vessel Origins and Destinations: The Cases of the Red Sea and Panama Canal 11: Containerization and Semi-containerships in the age of Globalization: Diverse Regional Trends 12: Optical Character Recognition of Lloyd's List Intelligence in 1880  Part 3. Vessel Operational Analysis using Maritime Big Data  13: Vessel Operational Patterns and their Applications 14: AIS-Based Vessel Trajectory and Destination Prediction 15: Predicting Ship Position in Long Time Intervals Using End-to-end Deep Learning with AIS data 16: Analysis of Vessel Schedule Recovery Policies in Liner Services Using AIS Data 17: Geographical Analysis of Spot Contracts in Dry Bulk Shipping 18: Extraction and International Comparison of Bunkering Services Using AIS Data  Part 4. Green Transition and Maritime Big Data  19: Fuel Consumption Estimation, Fleet Operations, and Emission Accounting 20: Geographical Analysis of Vessel Emissions 21: Carbon Pricing, Emission Trading System, and AIS 22: Emission Control Measures for Sustainable Arctic Shipping Activities  Part 5. Logistics Analysis using Maritime Big Data  23: Forecasting Shipping Freight Rates Using Big Data in Maritime Logistics 24: Short-term Forecast of Weekly Port Cargo Throughput with Machine Learning Models 25: Predicting Vessel Demand and Supply: Simulation-based Approach using AIS Data 26: Cargo Handling Performance Analysis by Combining Satellite-derived Crane Operational Data and AIS

Über Autor(innen)

Ryuichi Shibasaki is an Associate Professor at the Department of Systems Innovation, School of Engineering, The University of Tokyo. After getting PhD, he worked as a researcher at the National Institute for Land and Infrastructure Management, Ministry of Land, Infrastructure, Transport and Tourism, Japan for 15 years, and returned to The University of Tokyo in 2017. His research focus is primarily on global intermodal freight flow modelling, port logistics, and maritime big data analysis. Dr. Shibasaki’s research has received awards including three for “Best Application in Practices” from the Eastern Asia Society of Transport Studies (EASTS) and two from the International Association of Maritime Economists (IAME). He currently serves as an associate editor of Maritime Policy & Management, International Journal of Shipping and Transport Logistics, and Asian Transport Studies. Also, he is one of the representative organizers of the International Conference on Transportation and Logistics (TLOG Network).

Dong Yang is an Associate Professor at The Hong Kong Polytechnic University, where he also holds positions as the associate head of the Department of Logistics and Maritime Studies, director of Doctor of International Shipping and Port program joint with Zhejiang University (D.ISP) and International Shipping and Transport Logistics Master Program (ISTL), director of PolyU Maritime Data and Sustainable Development Centre, deputy director of Research Centre for Environmental, Social, and Governance Advancement. Dr. Yang serves as an associate editor for the International Journal of Shipping and Transport Logistics (IJSTL) and Maritime Policy & Management (MPM) and an editorial board member of the Journal of Transport Geography and Transportation Research Part E and Maritime Economics & Logistics. Prof. YANG obtained his Ph.D. in maritime logistics science from Kobe University, Japan, in 2008. After that, he has successively served as an assistant professor at the Southern University of Denmark, a research fellow at the Centre for Maritime Studies, National University of Singapore, and a senior research fellow at China Waterborne Transport Research Institute.

Xiwen Bai is currently an Associate Professor with the Department of Industrial Engineering, Tsinghua University. She received the B.S. and Ph.D. degree from Nanyang Technological University. Her main research interests include maritime economics and digital shipping. Her research has been published in leading international journals including Transportation Science and Transportation Research Part series. She also serves as associate editor for Maritime Policy & Management, a flagship journal in maritime transportation management.

Haiying Jia is Professor of Quantitative Business Economics at the Norwegian School of Economics (NHH). She earned her PhD in Finance from Bayes Business School, City University of London, the United Kingdom, and previously worked in London as a Quantitative and Investment Analyst in the finance industry. She also holds an Honorary Professorship at Bayes Business School. An internationally recognized scholar in shipping economics and maritime data analytics, Professor Jia has spent more than a decade pioneering the use of maritime Automatic Identification System (AIS) big data to study shipping markets, global trade, and financial dynamics in maritime industries. Her research bridges economics, finance, and maritime analytics, offering perspectives on how large-scale shipping data can inform research on international trade and global supply chains. She has been a visiting scholar at leading institutions worldwide, including the Massachusetts Institute of Technology (MIT), the University of Tokyo, and Nanyang Technological University. Drawing on her combined experience in academia, the finance industry, and entrepreneurship, Professor Jia’s work advances innovative approaches to understanding maritime trade and the evolving role of AIS data in global economic systems.