Global air transport complex network: multi-scale analysis

Connecting intercity mobility with urban welfare

While significant effort has been devoted to understand the role of intraurban characteristics on sustainability and growth, much remains to be understood about the effect of interurban interactions and the role cities have in determining each other's urban welfare. Here we consider a global mobility network of population flows between cities as a proxy for the communication between these regions, and analyze how it correlates with socioeconomic indicators. We use several measures of centrality to rank cities according to their importance in the mobility network, finding PageRank to be the most effective measure for reflecting these prosperity indicators. Our analysis reveals that the characterization of the welfare of cities based on mobility information hinges on their corresponding development stage. Namely, while network-based predictions of welfare correlate well with economic indicators in mature cities, for developing urban areas additional information about the prosperity of their mobility neighborhood is needed. We develop a simple generative model for the allocation of population flows out of a city that balances the costs and benefits of interaction with other cities that are successful, finding that it provides a strong fit to the flows observed in the global mobility network and highlights the differences in flow patterns between developed and developing urban regions. Our results hint towards the importance of leveraging interurban connections in service of urban development and welfare.

Characterizing neural phase-space trajectories via Principal Louvain Clustering

BACKGROUND

With the growing size and richness of neuroscience datasets in terms of dimension, volume, and resolution, identifying spatiotemporal patterns in those datasets is increasingly important. Multivariate dimension-reduction methods are particularly adept at addressing these challenges.

NEW METHOD

In this paper, we propose a novel method, which we refer to as Principal Louvain Clustering (PLC), to identify clusters in a low-dimensional data subspace, based on time-varying trajectories of spectral dynamics across multisite local field potential (LFP) recordings in awake behaving mice. Data were recorded from prefrontal cortex, hippocampus, and parietal cortex in eleven mice while they explored novel and familiar environments.

RESULTS

PLC-identified subspaces and clusters showed high consistency across animals, and were modulated by the animals' ongoing behavior.

CONCLUSIONS

PLC adds to an important growing literature on methods for characterizing dynamics in high-dimensional datasets, using a smaller number of parameters. The method is also applicable to other kinds of datasets, such as EEG or MEG.

Cascading dominates large-scale disruptions in transport over complex networks

The core functionality of many socio-technical systems, such as supply chains, (inter)national trade and human mobility, concern transport over large geographically-spread complex networks. The dynamical intertwining of many heterogeneous operational elements, agents and locations are oft-cited generic factors to make these systems prone to large-scale disruptions: initially localised perturbations amplify and spread over the network, leading to a complete standstill of transport. Our level of understanding of such phenomena, let alone the ability to anticipate or predict their evolution in time, remains rudimentary. We approach the problem with a prime example: railways. Analysing spreading of train delays on the network by building a physical model, supported by data, reveals that the emergence of large-scale disruptions rests on the dynamic interdependencies among multiple 'layers' of operational elements (resources and services). The interdependencies provide pathways for the so-called delay cascading mechanism, which gets activated when, constrained by local unavailability of on-time resources, already-delayed ones are used to operate new services. Cascading locally amplifies delays, which in turn get transported over the network to give rise to new constraints elsewhere. This mechanism is a rich addition to some well-understood ones in, e.g., epidemiological spreading, or the spreading of rumours and opinions over (contact) networks, and stimulates rethinking spreading dynamics on complex networks. Having these concepts built into the model provides it with the ability to predict the evolution of large-scale disruptions in the railways up to 30-60 minutes up front. For transport systems, our work suggests that possible alleviation of constraints as well as a modular operational approach would arrest cascading, and therefore be effective measures against large-scale disruptions.

A study of the U.S. domestic air transportation network: temporal evolution of network topology and robustness from 2001 to 2016

The U.S. air transportation network (ATN) is critical to the mobility and the functioning of the United States. It is thus necessary to ensure that it is well-connected, efficient, robust, and secure. Despite extensive research on its topology, the temporal evolution of the network’s robustness remains largely unexplored. In the present paper, a methodology is proposed to identify long-term trends in the evolution of the network’s topology and robustness over time. The study of the U.S. domestic ATN’s robustness was performed based on annual flight data from 1996 to 2016 and network analytics were used to examine the effects of restructuring that followed the 9/11 events. Centrality measures were computed and a node deletion method was applied to assess the network’s tolerance to a targeted attack scenario. The outcome of this study indicated that the 9/11 terrorist attacks triggered vast restructuring of the network, in terms of efficiency and security. Air traffic expanded, as new airports and air routes were introduced, allowing the network to recover rapidly and become more efficient. Security concerns resulted in significant improvement of the network’s robustness. Since 2001, the global traffic and topological properties of the U.S. ATN have displayed continuous growth, due to the network’s expansion. On the other hand, the results suggest that although the system’s ability to sustain its operational level under extreme circumstances has lately improved, its tolerance to targeted attacks has deteriorated. The presented methodology has shown its potential to be applied on different network levels or different transportation networks, in order to provide a general perspective of the system’s vulnerabilities.

Agent-Based Modeling (ABM): Support for Emphasizing the Air Transport Infrastructure Dependence of Space Systems

Critical infrastructure is foundational for the prosperity and quality of life in any society. By definition, its destruction or disruption would cause severe damage and possibly loss of life. Within this understanding, space systems are a new category of critical infrastructure, emerging as an enabler of new applications which are critical within the wider system-of-systems. This paper presents the results of a modeling exercise validating the proof of concept regarding the idea of the global, air-transport-critical infrastructure’s dependence on space systems. By using an open-source application, the authors constructed a complex system made up of 18 airports for which six scenarios were modeled that represent either the exposure to specific space phenomena or the effects of a partial or total critical space infrastructure disruption. Despite the limitations and assumptions made in the building of this model, its results suggest that a significant impact would result from disruptive events, with the potential for cascading disruptions within the system, beyond the system under analysis, and into the wider system-of-systems. Tools such as this model are useful to policy- and decision-makers, not only to protect existing, critical infrastructures, but also to adequately source future risks, vulnerabilities, and threats, and design and build new infrastructures.