Jamming transition in air transportation networks

TL;DR

This paper models air transportation as a complex network to identify a critical jamming transition point where congestion suddenly occurs, providing insights into traffic flow and potential optimization strategies.

Contribution

It introduces a network-based model of air traffic that captures the jamming transition and demonstrates its presence in real-world scale-free airline networks.

Findings

Jamming transition occurs at a critical load threshold.

Fluctuations peak around the congestion point, indicating criticality.

The transition is observed in both random and real-world airline networks.

Abstract

In this work we present a model of an air transportation traffic system from the complex network modelling viewpoint. In the network, every node corresponds to a given airport, and two nodes are connected by means of flight routes. Each node is weighted according to its load capacity, and links are weighted according to the Euclidean distance that separates each pair of nodes. Local rules describing the behavior of individual nodes in terms of the surrounding flow have been also modelled, and a random network topology has been chosen in a baseline approach. Numerical simulations describing the diffusion of a given number of agents (aircraft) in this network show the onset of a jamming transition that distinguishes an efficient regime with null amount of airport queues and high diffusivity (free phase) and a regime where bottlenecks suddenly take place, leading to a poor aircraft diffusion (congested phase). Fluctuations are maximal around the congestion threshold, suggesting that the transition is critical. We then proceed by exploring the robustness of our results in neutral random topologies by embedding the model in heterogeneous networks. Specifically, we make use of the European air transportation network formed by 858 airports and 11170 flight routes connecting them, which we show to be scale-free. The jamming transition is also observed in this case. These results and methodologies may introduce relevant decision making procedures in order to optimize the air transportation traffic.