Long-Range Connections in Transportation Networks

Abstract

Since its recent introduction, the small-world effect has been identified in several important real-world systems. Frequently, it is a consequence of the existence of a few long-range connections, which dominate the original regular structure of the systems and implies each node to become accessible from other nodes after a small number of steps, typically of order $\ell \propto \log N$. However, this effect has been observed in pure-topological networks, where the nodes have no spatial coordinates. In this paper, we present an alalogue of small-world effect observed in real-world transportation networks, where the nodes are embeded in a hree-dimensional space. Using the multidimensional scaling method, we demonstrate how the addition of a few long-range connections can suubstantially reduce the travel time in transportation systems. Also, we investigated the importance of long-range connections when the systems are under an attack process. Our findings are illustrated for two real-world systems, namely the London urban network (streets and underground) and the US highways network enhanced by some of the main US airlines routes.

References in corpus (8)

• Lethality and centrality in protein networks
• The diameter of the world wide web
• How Popular is Your Paper? An Empirical Study of the Citation Distribution
• The spatial structure of networks
• Modeling urban street patterns
• Optimal design of spatial distribution networks
• Designing optimal transport networks
• Improvement of the robustness on geographical networks by adding shortcuts

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