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An environmentally and economically sustainable air traffic management system must rely on fast models to assess and compare various alternatives and decisions at the different flight planning levels. Due to the numerous interactions between flights, mathematical models to manage the traffic can be computationally time-consuming when considering a large number of flights to be optimised at the same time. Focusing on demand–capacity imbalances, this paper proposes an approach that permits to quickly obtain an approximate but acceptable solution of this problem. The approach consists in partitioning flights into subgroups that influence each other only weakly, solving the problem independently in each subgroup, and then aggregating the solutions. The core of the approach is a method to build a network representing the interactions among flights, and several options for the definition of an interaction are tested. The network is then partitioned with existing community detection algorithms. The results show that applying a strategic flight planning optimisation algorithm on each subgroup independently reduces significantly the computational time with respect to its application on the entire European air traffic network, at the cost of few and small violations of sector capacity constraints, much smaller than those actually observed on the day of operations.
Silvia Zaoli; Giovanni Scaini; Lorenzo Castelli. Community Detection for Air Traffic Networks and Its Application in Strategic Flight Planning. Sustainability 2021, 13, 8924 .
AMA StyleSilvia Zaoli, Giovanni Scaini, Lorenzo Castelli. Community Detection for Air Traffic Networks and Its Application in Strategic Flight Planning. Sustainability. 2021; 13 (16):8924.
Chicago/Turabian StyleSilvia Zaoli; Giovanni Scaini; Lorenzo Castelli. 2021. "Community Detection for Air Traffic Networks and Its Application in Strategic Flight Planning." Sustainability 13, no. 16: 8924.
This paper presents results from the SESAR ER3 Domino project. Three mechanisms are assessed at the ECAC-wide level: 4D trajectory adjustments (a combination of actively waiting for connecting passengers and dynamic cost indexing), flight prioritisation (enabling ATFM slot swapping at arrival regulations), and flight arrival coordination (where flights are sequenced in extended arrival managers based on an advanced cost-driven optimisation). Classical and new metrics, designed to capture network effects, are used to analyse the results of a micro-level agent-based model. A scenario with congestion at three hubs is used to assess the 4D trajectory adjustment and the flight prioritisation mechanisms. Two different scopes for the extended arrival manager are modelled to analyse the impact of the flight arrival coordination mechanism. Results show that the 4D trajectory adjustments mechanism succeeds in reducing costs and delays for connecting passengers. A trade-off between the interests of the airlines in reducing costs and those of non-connecting passengers emerges, although passengers benefit overall from the mechanism. Flight prioritisation is found to have no significant effects at the network level, as it is applied to a small number of flights. Advanced flight arrival coordination, as implemented, increases delays and costs in the system. The arrival manager optimises the arrival sequence of all flights within its scope but does not consider flight uncertainties, thus leading to sub-optimal actions.
Luis Delgado; Gérald Gurtner; Piero Mazzarisi; Silvia Zaoli; Damir Valput; Andrew Cook; Fabrizio Lillo. Network-wide assessment of ATM mechanisms using an agent-based model. Journal of Air Transport Management 2021, 95, 102108 .
AMA StyleLuis Delgado, Gérald Gurtner, Piero Mazzarisi, Silvia Zaoli, Damir Valput, Andrew Cook, Fabrizio Lillo. Network-wide assessment of ATM mechanisms using an agent-based model. Journal of Air Transport Management. 2021; 95 ():102108.
Chicago/Turabian StyleLuis Delgado; Gérald Gurtner; Piero Mazzarisi; Silvia Zaoli; Damir Valput; Andrew Cook; Fabrizio Lillo. 2021. "Network-wide assessment of ATM mechanisms using an agent-based model." Journal of Air Transport Management 95, no. : 102108.
Betweenness centrality quantifies the importance of a vertex for the information flow in a network. The standard betweenness centrality applies to static single-layer networks, but many real world networks are both dynamic and made of several layers. We propose a definition of betweenness centrality for temporal multiplexes. This definition accounts for the topological and temporal structure and for the duration of paths in the determination of the shortest paths. We propose an algorithm to compute the new metric using a mapping to a static graph. We apply the metric to a dataset of $$\sim 20$$ ∼ 20 k European flights and compare the results with those obtained with static or single-layer metrics. The differences in the airports rankings highlight the importance of considering the temporal multiplex structure and an appropriate distance metric.
Silvia Zaoli; Piero Mazzarisi; Fabrizio Lillo. Betweenness centrality for temporal multiplexes. Scientific Reports 2021, 11, 1 -9.
AMA StyleSilvia Zaoli, Piero Mazzarisi, Fabrizio Lillo. Betweenness centrality for temporal multiplexes. Scientific Reports. 2021; 11 (1):1-9.
Chicago/Turabian StyleSilvia Zaoli; Piero Mazzarisi; Fabrizio Lillo. 2021. "Betweenness centrality for temporal multiplexes." Scientific Reports 11, no. 1: 1-9.
The most fundamental questions in microbial ecology concern the diversity and variability of communities. Their composition varies widely across space and time, as it is determined by a non-trivial combination of stochastic and deterministic processes. The interplay between non-linear community dynamics and environmental fluctuations determines the rich statistical structure of community variability, with both rapid temporal dynamics fluctuations and non-trivial correlations across habitats. Here we analyze long time-series of gut microbiome and compare intra- and inter-community dissimilarity. Under a macroecological framework we characterize their statistical properties. We show that most taxa have large but stationary fluctuations over time, while a minority is characterized by quick changes of average abundance which cluster in time, suggesting the presence of alternative stable states. We disentangle inter-individual variability in a major stochastic component and a deterministic one, the latter recapitulated by differences in the carrying capacities of taxa. Finally, we develop a model which includes environmental fluctuations and alternative stable states. This model quantitatively predicts the statistical properties of both intra- and inter-individual community variability, therefore summarizing variation in a unique macroecological framework.
Silvia Zaoli; Jacopo Grilli. A macroecological description of alternative stable states reproduces intra- and inter-host variability of gut microbiome. 2021, 1 .
AMA StyleSilvia Zaoli, Jacopo Grilli. A macroecological description of alternative stable states reproduces intra- and inter-host variability of gut microbiome. . 2021; ():1.
Chicago/Turabian StyleSilvia Zaoli; Jacopo Grilli. 2021. "A macroecological description of alternative stable states reproduces intra- and inter-host variability of gut microbiome." , no. : 1.
Identifying risk spillovers in financial markets is of great importance for assessing systemic risk and portfolio management. Granger causality in tail (or in risk) tests whether past extreme events of a time series help predicting future extreme events of another time series. The topology and connectedness of networks built with Granger causality in tail can be used to measure systemic risk and to identify risk transmitters. Here we introduce a novel test of Granger causality in tail which adopts the likelihood ratio statistic and is based on the multivariate generalization of a discrete autoregressive process for binary time series describing the sequence of extreme events of the underlying price dynamics. The proposed test has very good size and power in finite samples, especially for large sample size, allows inferring the correct time scale at which the causal interaction takes place, and it is flexible enough for multivariate extension when more than two time series are considered in order to decrease false detections as spurious effect of neglected variables. An extensive simulation study shows the performances of the proposed method with a large variety of data generating processes and it introduces also the comparison with the test of Granger causality in tail by Hong et al. (2009). We report both advantages and drawbacks of the different approaches, pointing out some crucial aspects related to the false detections of Granger causality for tail events. An empirical application to high frequency data of a portfolio of US stocks highlights the merits of our novel approach.
Piero Mazzarisi; Silvia Zaoli; Carlo Campajola; Fabrizio Lillo. Tail Granger causalities and where to find them: Extreme risk spillovers vs spurious linkages. Journal of Economic Dynamics and Control 2020, 121, 104022 .
AMA StylePiero Mazzarisi, Silvia Zaoli, Carlo Campajola, Fabrizio Lillo. Tail Granger causalities and where to find them: Extreme risk spillovers vs spurious linkages. Journal of Economic Dynamics and Control. 2020; 121 ():104022.
Chicago/Turabian StylePiero Mazzarisi; Silvia Zaoli; Carlo Campajola; Fabrizio Lillo. 2020. "Tail Granger causalities and where to find them: Extreme risk spillovers vs spurious linkages." Journal of Economic Dynamics and Control 121, no. : 104022.