Unveiling individual and collective temporal patterns in the tanker shipping network

TL;DR

This study analyzes four years of high-resolution oil tanker movement data to reveal global spatio-temporal patterns, emphasizing efficiency, seasonal variations, and implications for sustainability in the shipping network.

Contribution

It introduces a novel application of sequential motif mining and dynamic mode decomposition to uncover detailed shipping patterns and their seasonal and regional variations.

Findings

Maximizing cargo time involves route diversification and intra-regional port use.

Identifies a stable global travel structure with seasonal variations.

Highlights the role of high-resolution data in understanding and improving shipping efficiency.

Abstract

The global shipping network, which moves over 80% of the world's goods, is not only a vital backbone of the global economy but also one of the most polluting industries. Studying how this network operates is crucial for improving its efficiency and sustainability. While the transport of solid goods like packaged products and raw materials has been extensively researched, far less is known about the competitive trade of crude oil and petroleum, despite these commodities accounting for nearly 30% of the market. Using 4 years of high-resolution data on oil tanker movements, we employ sequential motif mining and dynamic mode decomposition to uncover global spatio-temporal patterns in the movement of individual ships. Across all ship classes, we demonstrate that maximizing the proportion of time ships spend carrying cargo -- a metric of efficiency -- is achieved through strategic diversification of routes and the effective use of intra-regional ports for trips without cargo. Moreover, we uncover a globally stable travel structure in the fleet, with pronounced seasonal variations linked to annual and semi-annual regional climate patterns and economic cycles. Our findings highlight the importance of integrating high-resolution data with innovative analysis methods not only to improve our understanding of the underlying dynamics of shipping patterns, but to design and evaluate strategies aimed at reducing their environmental impact.