Applying complexity science to air traffic management

Cook, Andrew J.; Blom, H.A.P.; Lillo, Fabrizio; Mantegna, Rosario N.; Miccichè, Salvatore; Rivas, Damiân; Vázquez, Rafael; Zanin, Massimiliano

doi:10.1016/j.jairtraman.2014.09.011

Cited by 95 publications

(54 citation statements)

References 52 publications

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“…Third, one should be aware that the flight network is only one of the many elements contributing to the dynamics of the system. For instance, the role of passengers has extensively 5% error threshold been studied within the context of delay propagation [46,47], as passengers represents another network of connections, which does not correspond well with the simple flight network. Therefore, the problem tackled in this contribution may be encountered when managing (sampled) passenger data, or indeed, not having such data at all.…”

Section: Discussionmentioning

confidence: 99%

On the multi-dimensionality and sampling of air transport networks

Belkoura

Cook

Peña

et al. 2016

Transportation Research Part E: Logistics and Transportation Re

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Complex network theory is a framework increasingly used in the study of air transport networks, thanks to its ability to describe the structures created by networks of flights, and their influence in dynamical processes such as delay propagation. While many works consider only a fraction of the network, created by major airports or airlines, for example, it is not clear if and how such sampling process bias the observed structures and processes. In this contribution, we tackle this problem by studying how some observed topological metrics depend on the way the network is reconstructed, i.e. on the rules used to sample nodes and connections. Both structural and simple dynamical properties are considered, for eight major air networks and different source datasets. Results indicate that using a subset of airports strongly distorts our perception of the network, even when just small ones are discarded; at the same time, considering a subset of airlines yields a better and more stable representation. This allows us to provide some general guidelines on the way airports and connections should be sampled.

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Section: Discussionmentioning

confidence: 99%

On the multi-dimensionality and sampling of air transport networks

Belkoura

Cook

Peña

et al. 2016

Transportation Research Part E: Logistics and Transportation Re

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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%

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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“…Poor predictability imposes excessive costs to operators. Predictability in the context of weather forecasts and traffic flow (Cook et al 2015), better "collaborative decision-making", better "management of reaction to bad weather conditions" and better "control of take-off times" (Desart et al 2010), or more efficient planning for flights and preventing extra fuel loading before the departure (Hao, Hansen, and Ryerson 2016), are have been related to flight predictability which its improvement reduces costs and fuel burns of aircraft. According to Hao, Hansen, and Ryerson (2016), a 1 minute deviation from flight's standard schedule can cause 1.66 minutes rise in the total contingency and alternate fuel loading.…”

Section: Aeronautical Information Servicesmentioning

confidence: 99%

Analysis of evolution of commercial air traffic CO2 emissions in the European Union

Aminzadeh¹

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The environmental sustainability of the air transport industry, as a key player in global social and economic development, has become a major issue in the agenda of the industry´s stakeholders during the past decade. On both international and local levels, policy makers, scientists and environmentalists, manufacturers and airlines have begun to collaborate on fuel efficiency improvements and greenhouse gas emissions reductions in the sector.This dissertation evaluates the structure and distribution of air traffic in European Union member states. The data presented here has been extracted from the statistical office of the European Union (Eurostat) and databases compiled by the The European Organization for the Safety of Air Navigation (EUROCONTROL). The focus of this study is on commercial air traffic and CO2 emissions in the European Union in 2010 and its evolution through 2013.The extracted data for all EU countries consist of variables such as: number of flights, number of passengers, freight tonnes, revenue passenger kilometers (RPKs) and revenue tonne kilometers (RTKs), fuel consumption and CO2 emissions. The majority of EU air transport traffic is concentrated in six countries: France, Germany, Italy, The Netherlands, Spain and The United Kingdom, which in 2010 represented approximately two thirds of the total EU flights. The data is then segmented per distance bands, at intervals of 500 km to see the concentration of traffic in different distance bands and compare their share with other modes of transport. The fuel efficiency trend in the six largest EU aviation markets from 2010 to 2013 is analyzed and the potential reasons for the changes are investigated. The evolution in the airline fleet composition during the last decade is presented as one of the reasons for the improvement in fuel efficiency, measured by fuel efficiency parameter measuring fuel consumption per RTK (passengers + freight). Additionally, the differences in terms of fuel efficiency among the various airline business models (network carriers, low cost carriers, etc.) and aircraft types are studied. Moreover, future CO2 emissions are projected, taking into consideration three different traffic growth and efficiency improvement scenarios.The results of this analysis indicate a slight reduction in the traffic, both for passengers and freight about (-0.8%) but more importantly, a reduction in CO2 emissions (-4.3%), which is the result of an improvement in fuel efficiency parameters (-3.5%) during these three years. There has been a relevant change in the fleet composition in the last ten years, with the replacement of older models for more efficient ones, and a shift to larger aircraft, particularly in the regional segment. Traffic has decreased in shorter distances (internal EU traffic), but increased in more efficient long-range flights (extra-EU traffic), resulting also in an improvement in the efficiency parameters as average aircraft size and stage length increases. This investigation concludes that technological efficiency in...

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Applying complexity science to air traffic management

Cited by 95 publications

References 52 publications

On the multi-dimensionality and sampling of air transport networks

On the multi-dimensionality and sampling of air transport networks

New centrality and causality metrics assessing air traffic network interactions

Analysis of evolution of commercial air traffic CO2 emissions in the European Union

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