Towards new metrics assessing air traffic network interactions

In ATM systems, the massive number of interactin entities makes it difficult to predict the system-wide effects that innovations might have. Here, we present the approach proposed by the project Domino to assess such effects and identify the impact that innovations might bring for the different stake-holders, based on agent-based modelling and complex network science. Domino will model scenarios mirroring different system innovations which change the agents’ actions and behaviour. Suitable network metrics are needed to evaluate the effect of innovations on the network functioning. We review existing centrality and causality metrics and show their limitations in characterising the network by applying them to a dataset of US flights. We finally suggest improvements that should be introduced to obtain new metrics answering to Domino’s needs

New centrality and causality metrics assessing air traffic network interactions

In ATM systems, the massive number of interacting entities makes it difficult to identify critical elements and paths of disturbance propagation, as well as to predict the system-wide effects that innovations might have. To this end, suitable metrics are required to assess the role of the interconnections between the elements and complex network science provides several network metrics to evaluate the network functioning. Here we focus on centrality and causality metrics measuring, respectively, the importance of a node and the propagation of disturbances along links. By investigating a dataset of US flights, we show that existing centrality and causality metrics are not suited to characterise the effect of delays in the system. We then propose generalisations of such metrics that we prove suited to ATM applications. Specifically, the new centrality is able to account for the temporal and multi-layer structure of ATM network, while the new causality metric focuses on the propagation of extreme events along the system

Domino D5.1 - Metrics and analysis approach

This deliverable presents the metrics proposed to assess the impact of innovations in the ATM system and a stylized ABM model, called a ‘toy model’, to be used as a test ground for the metrics. Existing network metrics are reviewed and their limitations are highlighted by applying them to real data. New metrics are then suggested to overcome these limitations. Their better results in measuring interconnections and causal relationships between the elements of the ATM system are shown for empirical case studies. The design of the toy model is presented and preliminary results of its baseline implementation are shown

Domino D5.3 Final tool and model description, and case studies results

This deliverable presents the final results obtained from the Domino project. It presents the corresponding metrics, the model, and a detailed analysis of two case studies. The main modifications to the model with respect to the previous version are highlighted, including curfew management. The calibration of the model is presented, which is similar to the previous version, with more in-depth analyses and further effort dedicated to the calibration process. Two case studies are defined in this deliverable, using previous definitions of the three base mechanisms: 4D trajectory adjustments, flight prioritisation, and flight arrival coordination. The case studies are defined to have a focused insight into the efficiency of the mechanisms in specific environments. The two case studies are run by the model and analysed using metrics previously defined, including centrality and causality metrics. The results show different levels of efficiency for the three mechanisms, highlight the degree of robustness to the propagation of negative effects (such as delay) in the system, demonstrate various trade-offs between the indicators, and support a discussion of the limit of the mechanisms

Domino D5.2 - Investigative case studies results

This deliverable presents the results from the analysis of the model executing the investigative case studies. The document focuses on the validation activities and the results for the three mechanisms modelled in Domino in the unitary case studies. The three mechanism are: 4D Trajectory Adjustment, which focuses on the use of dynamic cost indexing and wait-for-passengers rules; Flight Prioritisation, which considers the possibility of slot swapping at ATFM regulations; and Flight Arrival Coordination, which models different optimisation approaches E-AMAN could consider. Each mechanism has three levels of implementation: Level 0 (with current capabilities), Level 1 (with more advanced features) and Level 2 (more explorative). The traffic is set on a given day (12 September 2014) considering flights and passengers’ itineraries. Two levels of delay are considered: default and stressed. In total 14 scenarios have been modelled and analysed. This deliverable presents the use of classical and network metrics (centrality and causality) on the outcome of the whole European level agent-based model. The model still requires further development and adjustment, but results show that it is already capable of capturing complex interactions among the ATM elements. Finally, the network metrics are already presenting their potential to capture non-direct interactions between elements in the system. The results have been shared with experts and airspace users at two workshops. The feedback obtained and the results of the analysis and validation activities will be considered for the final version of Domino

Domino D3.3 - Adaptive case studies description

This deliverable presents the improvement planned to be performed until the end of the project regarding the model (implementation changes, recalibration and the simulation outputs), plus the metrics and scenarios that will be re-run with the model. These changes are based on the insights gathered through the analysis activities performed in the scope of investigative case studies (see D3.2 Investigative case studies description and D5.2 Investigative case studies results) and the feedback obtained from experts and stakeholders on the different workshops activities performed (see D6.3 Workshop results summary). These insights highlighted missing features of the model and potential improvements, as well as some gaps and shortcomings. The scenarios for this analysis have been chosen highly selectively in order to prioritise the depth of the analysis and methodology development over a large number of scenarios, as these have already been analysed in the scope of the investigative case studies

Betweenness centrality for temporal multiplexes

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 3c 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

Tail Granger causalities and where to find them: Extreme risk spillovers vs spurious linkages

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

Network-wide assessment of ATM mechanisms using an agent-based model

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

Structural characterization of a Cu/MgO (001) interface using CS-corrected HRTEM

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