Clusters and communities in air traffic delay networks

Abstract

Abstract-The air transportation system is a network of many interacting, capacity-constrained elements. When the demand for airport and airspace resources exceed the available capacities of these resources, delays occur. The state of the air transportation system at any time can be represented as a weighted directed graph in which the nodes correspond to airports, and the weight on each arc is the delay experienced by departures on that origin-destination pair. Over the course of any day, the state of the system progresses through a timeseries, where the state at any time-step is the weighted directed graph described above.This paper presents algorithms for the clustering of air traffic delay network data from the US National Airspace System, in order to identify characteristic delay states (i.e., weighted directed graphs) as well as characteristic types-ofdays (i.e., sequences of such weighted directed graphs) that are experienced by the air transportation system. The similarity of delay states during clustering are evaluated on the basis of not only the in-and out-degrees of the nodes (the total inbound and outbound delays), but also network-theoretic properties such as the eigenvector centralities, and the hub and authority scores of different nodes. Finally, the paper looks at community detection, that is, the grouping of nodes (airports) based on their similarities within a system delay state. The type of day is found to have an impact on the observed community structures.

Multi-Scale Data Mining for Air Transportation System Diagnostics

“…In this way, the size of the data reduces from the left to the right in the diagram, but the level of insight should increase if executed properly. The utility of this general framework is demonstrated in a range of relevant analysis case studies discussed at a high level in the next sections, but greater detail can be found in companion papers [1][2][3][4][5]. …”

“…In other words, disruptions propagate differently in air transport systems because of these characteristics. Our analysis of these issues is presented in more detail in [1].…”

“…The resulting models are readily interpretable (owing in large part to the reduction of dimensionality) and reflect the unique characteristics of air traffic networks identified above, namely, that they are weighted and directed networks which can adopt a wide range of nodal states. Clustering based on the delays and hub/authority scores were used to identify characteristic delay patterns, which can also help model timevarying network structures [1]. American Institute of Aeronautics and Astronautics Extensible analysis techniques: An important advantage of the network characterization method identified above is that it is fundamentally agnostic to the source or field considered in weighting the networks.…”

Cyber- Threat Assessment for the Air Traffic Management System: A Network Controls Approach

“…The described network-theoretic approach builds on a growing literature on graphand network-theoretic approaches to air traffic management [11,25,26]. Relative to this literature, the main innovation here is to study the spatiotemporal impacts of disruptions from a graph-theory and network-controls perspective, and to develop models and analyses for meshed cyber, traffic, and weather dynamics.…”

Mitigating infrastructure risk : reducing uncertainty in resilience modeling

“…Fleurquin et al (2013) analyzed the delay propagation in U.S. airport network as a consequence of technical, operational or meteorological issues (Fleurquin et al, 2013). They noted that there is "non-negligible" risk of systematic instability not only under disruption scenarios but also under normal operating conditions, highlighting the need to have predetermined post-hazard restoration strategies for immediate response and service recovery after cascading failure across the network.…”