Network analysis of Chinese air transport delay propagation

Zanin, Massimiliano; Belkoura, Seddik; Zhu, Yanbo

doi:10.1016/j.cja.2017.01.012

Cited by 54 publications

(45 citation statements)

References 27 publications

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“…The presence of a causal relation is assessed by means of statistical tests whose most famous example is the Granger causality metrics [9]. Indeed, it has been recently applied to airport networks [10], [11]. Here, a data driven approach is adopted to identify the channels through which the delay propagates and establish a network of causal relations, where a link between two airports is present if delay propagates from one to the other.…”

Section: Network Metricsmentioning

confidence: 99%

“…b) Granger causality network: Having established how to detect a causal relation, we can consider the network of airports where a link i → j is present if i 'Granger causes' j. This approach has already been considered in some recent works in Econometrics [18], [19] and in a recent analysis of the Chinese air transportation network [11]. Given N time series, representing the state of delay of the N airports in the network, Granger causality test is performed on all the possible M = N (N − 1) pairs.…”

Section: B Centrality Metricsmentioning

confidence: 99%

“…2) Application of Granger causality metrics to the US flights dataset: Time series of the state of delay for each airport are built for the period from January 1st 2015 to March 31st 2015. As suggested in [11], a Z-Score standardisation procedure is applied to reduce the non-stationarity of the time series caused by daily seasonality, which may result in a biased evaluation of the Granger causality metric. The standardised time series of airport i is calculated asx i,t = (x i,t −x t i )/σ t i wherex t i and σ t i are the mean and the standard deviation of the delay states of airport i recorded at hour t across all available days.…”

Section: B Centrality Metricsmentioning

confidence: 99%

See 2 more Smart Citations

New centrality and causality metrics assessing air traffic network interactions

Mazzarisi

Zaoli

Lillo

et al. 2020

Journal of Air Transport Management

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In ATM systems, the massive number of interacting 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 and identify the impact that innovations might bring for the different stakeholders, based on agent-based modelling and complex network science. By investigating a dataset of US flights, we first show that existing centrality and causality metrics are not suited in characterising the effect of delays in the system. We then propose generalisations of such metrics that we prove suited to ATM applications. Then, we introduce the Agent Based Model used in Domino to model scenarios mirroring different system innovations which change the agents' actions and behaviour. We focus on a specific innovation related to flight arrival coordination and we show the insights on its effects at the network level obtained by applying the proposed new metrics.

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Section: Network Metricsmentioning

confidence: 99%

Section: B Centrality Metricsmentioning

confidence: 99%

Section: B Centrality Metricsmentioning

confidence: 99%

See 1 more Smart Citation

New centrality and causality metrics assessing air traffic network interactions

Mazzarisi

Zaoli

Lillo

et al. 2020

Journal of Air Transport Management

View full text Add to dashboard Cite

show abstract

“…Therefore, the study of the expected delay propagation network in Europe and China have been conducted and compared to (in the European case) to the disrupted propagation triggered by severe perturbations. The case of China can be found in the Chinese Journal of Aeronautics (Impact Factor of 0.977) [ZBY16] while the European case has been published and presented in the 7th…”

Section: Main Contributions and Publicationsmentioning

confidence: 99%

“…However it will be propagated to another small airport by medium range flights (narrow bodies) or another regional flight (turbo prop). [ZBY16].…”

Section: Delay Propagation Through Airportsmentioning

confidence: 99%

Complex Networks and Data Mining : toward a new perspective for the understanding of Air Transportation

Belkoura¹

Self Cite

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Complex systems, i.e. systems composed of a large set of elements transporting and interchanging information in a non-linear way, are constantly found all around us. In the last decades, the approach toward their understanding has shifted progressively from a transportation to an information processing point of view. In other words, we are moving from a movement-based analysis (i.e. tracking the movement of items through time and space to reconstruct various metrics about their behaviour) to a higher-level approach, where individual movements are left aside to focus on the distribution, processing and flow of the information within the system. The information processing approach presents the main advantage of being data-based, that is, that no a priori knowledge about the interactions in the system is needed, hence the absence of costly simulations models. Such paradigm perfectly fits within the air transport system, where thematics as important as delay propagation (for its economical, environmental and safety related consequences) has been until now mainly analysed from a transportation micro-level perspective. Yet, the progressive rise in aviation of data analyses encourages a more data-centred path. We here present the first work that aims at fostering the combined use of the intuitive microscopic point of view with a higher-level information processing approach, yielding a more complete characterisation of the delay propagation process. Specifically, the here propose a threefold approach. First, we highlight the degree of subjectivity associated with network-based representations of the air transport system, which conditions the intelligence extracted from any information processing study. Secondly, we manufactur a new data mining technique to extract non-linear causality relationships, therefore enabling the creation of a more complete delay propagation network representation. Finally, we complement our results by a micro-level analysis, therefore ending up with a 360 • view of the delay propagation process. These analysis have been performed mainly on a European dataset, but expanded to other airspaces whenever data have been available.

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Is CSR linked to idiosyncratic risk? Evidence from the copula approach

2022

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In this paper, we extend the research on the effect of corporate social responsibility (CSR) on firm risk by analyzing the CSR–idiosyncratic risk nexus and how CSR can be integrated as insurance in a global risk management strategy. First, the causality between CSR and risk was tested. Second, copulas were estimated to strengthen the existing results on the structure of the dependence between the different dimensions of CSR activities and idiosyncratic risk levels. The empirical analysis was conducted on a sample of 254 European-listed firms over the 2018–2020 period. The main results showed a directional causality effect between CSR and idiosyncratic risk, and the dependences were modeled between CSR and realized idiosyncratic risk. This allows a better understanding of the risk implications of CSR for investors, corporate managers, and policy makers.

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Network analysis of Chinese air transport delay propagation

Cited by 54 publications

References 27 publications

New centrality and causality metrics assessing air traffic network interactions

New centrality and causality metrics assessing air traffic network interactions

Complex Networks and Data Mining : toward a new perspective for the understanding of Air Transportation

Is CSR linked to idiosyncratic risk? Evidence from the copula approach

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