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Extracting Global Shipping Networks from Massive Historical Automatic Identification System Sensor Data: A Bottom-Up
Zhihuan Wang1, Christophe Claramunt2,3, Yinhai Wang4
1Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China. zhwang@shmtu.edu.cn.
Sensors (Basel, Switzerland)
|August 3, 2019
Summary
This study introduces a novel method to build Global Shipping Networks (GSN) using Automatic Identification Systems (AIS) data. It reveals spatial-temporal patterns in global ship traffic by analyzing ship movements and port calls.
Area of Science:
- Maritime Logistics
- Data Science
- Network Analysis
Background:
- Big data from Automatic Identification Systems (AIS) enable detailed tracking of global ship activities.
- Understanding spatial-temporal patterns in ship traffic is crucial for optimizing maritime operations.
Purpose of the Study:
- To propose a data integration approach for constructing Global Shipping Networks (GSN) from AIS data.
- To develop a bottom-up methodology for creating multi-level shipping networks.
Main Methods:
- Applied DBSCAN algorithm to identify ship stop locations and events from AIS trajectories.
- Mapped identified stop locations to World Port Index (WPI) for semantic meaning.
- Constructed GSN with stop locations as nodes and ship journeys as links.
Main Results:
- Successfully generated country, port, and terminal level Global Container Shipping Networks (GCSN).
- Demonstrated the approach using AIS data from over 4000 container ships in 2015.
- Analyzed key features of the constructed GCSNs.
Conclusions:
- The proposed bottom-up approach effectively constructs comprehensive Global Shipping Networks from AIS data.
- The method provides valuable insights into global shipping patterns at various scales.
- Identified limitations and areas for future research in GSN construction.
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