Eur. Phys. J. B 84, 589-600 (2011)
https://doi.org/10.1140/epjb/e2011-20208-9

Regular Article

Complexity in human transportation networks: a comparative analysis of worldwide air transportation and global cargo-ship movements

¹ Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, Illinois, USA
² Max-Planck-Institut für Dynamik und Selbstorganisation, Göttingen, Germany
³ ICBM, University of Oldenburg, 26111 Oldenburg, Germany
⁴ Department of Engineering Sciences and Applied Mathematics & Northwestern Institute on Complex Systems, Northwestern University, Evanston, Illinois, USA

^a e-mail: brockmann@northwestern.edu

Received: 17 March 2011
Received in final form: 28 September 2011
Published online: 8 December 2011

Abstract

We present a comparative network-theoretic analysis of the two largest global transportation networks: the worldwide air-transportation network (WAN) and the global cargo-ship network (GCSN). We show that both networks exhibit surprising statistical similarities despite significant differences in topology and connectivity. Both networks exhibit a discontinuity in node and link betweenness distributions which implies that these networks naturally segregate into two different classes of nodes and links. We introduce a technique based on effective distances, shortest paths and shortest path trees for strongly weighted symmetric networks and show that in a shortest path tree representation the most significant features of both networks can be readily seen. We show that effective shortest path distance, unlike conventional geographic distance measures, strongly correlates with node centrality measures. Using the new technique we show that network resilience can be investigated more precisely than with contemporary techniques that are based on percolation theory. We extract a functional relationship between node characteristics and resilience to network disruption. Finally we discuss the results, their implications and conclude that dynamic processes that evolve on both networks are expected to share universal dynamic characteristics.