General cargo and containership emergent routes: A complex networks description

• Published in: Transport policy 2012-11, Vol.24, p.126-140
• Main Authors: Pais Montes, Carlos, Freire Seoane, Maria Jesus, González Laxe, Fernando
• Format: Article
• Language: English
• Subjects: Complex networks; Computer networks; Containers; Data science; Evolution; Geography; Logistics; Maritime; Markets; Networks; Port throughput; Ports; Routing (telecommunications); Statistics; Transport
• ISSN: 0967-070X; 1879-310X
• DOI: 10.1016/j.tranpol.2012.06.022

The paper aims to explain the evolution of the containerized and general cargo maritime routes in the last 3 years using complex networks analysis. Several particular results are searched: which ports are currently rising or dwindling in throughput; how is the structure of their network dynamics; and how to describe the resemblances and differences between these two transport patterns. In a densely connected logistic scheme, like the current maritime transport network, classic statistical techniques cannot show an accurate measure of the regional and global importance of a port or a route, within the deeply interrelated global market. The influence of a given harbor must be put in relation with the whole set of the network nodes. Standard statistic tools also cannot explain the chronological evolution of a complex system such this, needed of an importance metric and a proper visualization treatment. Graph theory provides powerful mathematical tools in order to achieve such requirements. Several calculations (degree and centrality) can be performed on each node, in order to describe clearly the structure and evolution of the complex system formed by ports of call and routes performed between them. Besides this, new software representation tools, like Gephi and Tulip, allow the immediate and deep comprehension of the relations between all the elements of the graph computed, and the temporal evolution of the whole network. In this paper we will apply these methodologies to the entire database of containership and general cargo vessel positions in three periods: March 2008–February 2009, March 2009–February 2010 and March 2010–February 2011. The relevance of the time intervals for this analysis, in terms of length and immediacy, will lead us to an accurate and dynamic diagnostic for the evolution during the crisis years in the transport patterns of the two traffics considered. ► Graph Theory methods are able to give information about the global and local importance of a given transport mode. ► Containership activity has grown since the 2008–2010 demand crises, but is a slow and weak recovery. ► General Cargo Shipping activity has shown a great robustness during the 2008–2010 period. ► The South hemisphere is growing in its containerized traffic. ► The assertion that developed countries use more container logistic schemes than general cargo ones is false.