ビッグデータを用いた海運・ロジスティクス解析 Shipping and Logistics Analytics using Big Data

Shibasaki, Ryuichi (Edited by)/ Yang, Dong (Edited by)/ Bai, Xiwen (Edited by)

Taylor & Francis Ltd（2026/12/15発売）

• 製本 Hardcover:ハードカバー版／ページ数 340 p.／サイズ 246mmx174mmx-
• 言語 ENG
• 商品コード 9781032505343
• DDC分類 387.5028557

基本説明

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. 

Full Description

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 the industry. In maritime shipping, the 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.

Contents

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