Unveiling individual and collective temporal patterns in the tanker shipping network

Area of Science:

• Maritime Logistics
• Network Analysis
• Data Science

Background:

• Global oil tanker shipping is complex, influenced by economic, environmental, and operational factors.
• Existing analyses often use static data, missing crucial temporal dynamics in vessel routing and cargo flow.
• Understanding these dynamics is key to optimizing shipping efficiency.

Purpose of the Study:

• To introduce a dual-scale framework integrating sequential motif analysis and Dynamic Mode Decomposition (DMD).
• To analyze temporal dynamics in tanker movement data and their impact on global cargo flows.
• To identify how vessel routing strategies affect economic and environmental efficiency.

Main Methods:

• Sequential motif analysis to identify recurrent patterns in vessel movement sequences.
• Dynamic Mode Decomposition (DMD) to extract temporal dynamics from vessel trajectories.
• Analysis of tanker movement data across four vessel classes.

Main Results:

• Vessels with diverse regional exploration spent up to 50% more time transporting cargo, indicating improved efficiency.
• DMD revealed distinct seasonality in the shipping network (16% average peak-to-trough amplitude).
• Import regions showed synchronous annual demand cycles, while export regions exhibited anti-synchronicity.

Conclusions:

• Temporal analysis of tanker shipping reveals performance differences missed by static methods.
• Optimizing vessel routes based on dynamic temporal patterns can yield significant economic and environmental benefits.
• The dual-scale framework provides a novel approach to understanding and improving global shipping networks.