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Filar, Jerzy A.; Manyem, Prabhu; White, Kevin

doi:10.1023/a:1016079600083

Cited by 56 publications

(4 citation statements)

References 36 publications

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“…Faced with rapidly growing demand in air traffic, researchers have addressed aggravating congestion problems from various perspectives [1][2][3][4][5][6][7]. Airport congestion related to departing aircraft on the ground, i.e., surface congestion, is known to be a serious problem, and its relationship to airport performance has been recently investigated [8,9].…”

Section: Introductionmentioning

confidence: 99%

Potential global jamming transition in aviation networks

Ezaki

Nishinari

2014

Phys. Rev. E

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In this paper, we propose a nonlinear transport model for an aviation network. The takeoff rate from an airport is characterized by the degree of ground congestion. Due to the effect of surface congestion, the performance of an airport deteriorates because of inefficient configurations of waiting aircraft on the ground. Using a simple transport model, we performed simulations on a U. S. airport network and found a global jamming transition induced by local surface congestion. From a physical perspective, the mechanism of the transition is studied analytically and the resulting aircraft distribution is discussed considering system dynamics. This study shows that the knowledge of the relationship between a takeoff rate and a congestion level on the ground is vital for efficient air traffic operations.

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Section: Introductionmentioning

confidence: 99%

Potential global jamming transition in aviation networks

Ezaki

Nishinari

2014

Phys. Rev. E

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“…The air transport network is a "time-varying network", where the links between nodes (i.e., airports) are represented by flights on a timetable [6]. Most elements in the network are subject to stochastic influence [25,26]. The time-varying and uncertain nature of this network creates a set of "dynamics" that affect the way airlines, air navigation service providers and airports manage their operations [8,27,28].…”

Section: Background and Contributionmentioning

confidence: 99%

Uncertainty Management at the Airport Transit View

et al. 2018

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Air traffic networks, where airports are the nodes that interconnect the entire system, have a time-varying and stochastic nature. An incident in the airport environment may easily propagate through the network and generate system-level effects. This paper analyses the aircraft flow through the Airport Transit View framework, focusing on the airspace/airside integrated operations. In this analysis, we use a dynamic spatial boundary associated with the Extended Terminal Manoeuvring Area concept. Aircraft operations are characterised by different temporal milestones, which arise from the combination of a Business Process Model for the aircraft flow and the Airport Collaborative Decision-Making methodology. Relationships between factors influencing aircraft processes are evaluated to create a probabilistic graphical model, using a Bayesian network approach. This model manages uncertainty and increases predictability, hence improving the system's robustness. The methodology is validated through a case study at the Adolfo Suárez Madrid-Barajas Airport, through the collection of nearly 34,000 turnaround operations. We present several lessons learned regarding delay propagation, time saturation, uncertainty precursors and system recovery. The contribution of the paper is two-fold: it presents a novel methodological approach for tackling uncertainty when linking inbound and outbound flights and it also provides insight on the interdependencies among factors driving performance. Aerospace 2018, 5, 59 2 of 31The Airport Transit View (ATV) concept describes the "visit" of an aircraft to the airport [9]. This framework connects inbound and outbound flights, providing a tool to optimise airport operations and to enable more efficient and cost-effective deployment of operator resources. It integrates airside operations (landing, taxiing, turnaround and take-off) and surrounding airspace operations (holding, final approach and initial climb) [3,10,11]. Moreover, the Airport Operations Plan (AOP) guarantees a common, agreed operational strategy between local stakeholders, providing knowledge about the current situation and detecting deviations [9]. It aims to achieve early decision-making and efficient management of the aircraft processes. In this sense, the Airport Collaborative Decision-Making (A-CDM) concept ensures that common situation awareness is reached between stakeholders [9]. Moreover, the implementation of the 4D-trajectory operational concept in future Air Traffic Management (ATM) systems will impose the compliance of very accurate arrival times over designated points on aircraft, including Controlled Times of Arrival (CTAs) at airports [12][13][14].Uncertainty of operational conditions (e.g., runway configuration, aircraft performance, air traffic regulations, airline business models, ground services, meteorological conditions) makes airspace/airside integrated operations a stochastic phenomenon [15][16][17][18][19]. It is therefore necessary to define methodological frameworks to improve predictability and reliabi...

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“…Therefore, there is a trade-off between approach efficiency and runway throughput (capacity). Arrival operations also depend on the times when flights are scheduled (Wu, 2012) and, as such, constitute a time-varying system (Jani c, 1997;Filar et al, 2001). The combined stochastic and time-varying nature of the arrival system (Ciruelos et al, 2015) creates a set of "dynamics" that affect the way airlines, ANSPs (Air Navigation Service Providers) and airports manage their operations (Pyrgiotis et al, 2013;Cook et al, 2015).…”

Section: Introduction and Problem Statementmentioning

confidence: 99%

Impact of weather conditions on airport arrival delay and throughput

Rodríguez–Sanz

Cano

Fernández

2021

AEAT

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Purpose Weather events have a significant impact on airport arrival performance and may cause delays in operations and/or constraints in airport capacity. In Europe, almost half of all regulated airport traffic delay is due to adverse weather conditions. Moreover, the closer airports operate to their maximum capacity, the more severe is the impact of a capacity loss due to external events such as weather. Various weather uncertainties occurring during airport operations can significantly delay some arrival processes and cause network-wide effects on the overall air traffic management (ATM) system. Quantifying the impact of weather is, therefore, a key feature to improve the decision-making process that enhances airport performance. It would allow airport operators to identify the relevant weather information needed, and help them decide on the appropriate actions to mitigate the consequences of adverse weather events. Therefore, this research aims to understand and quantify the impact of weather conditions on airport arrival processes, so it can be properly predicted and managed. Design/methodology/approach This study presents a methodology to evaluate the impact of adverse weather events on airport arrival performance (delay and throughput) and to define operational thresholds for significant weather conditions. This study uses a Bayesian Network approach to relate weather data from meteorological reports and airport arrival performance data with scheduled and actual movements, as well as arrival delays. This allows us to understand the relationships between weather phenomena and their impacts on arrival delay and throughput. The proposed model also provides us with the values of the explanatory variables (weather events) that lead to certain operational thresholds in the target variables (arrival delay and throughput). This study then presents a quantification of the airport performance with regard to an aggregated weather-performance metric. Specific weather phenomena are categorized through a synthetic index, which aims to quantify weather conditions at a given airport, based on aviation routine meteorological reports. This helps us to manage uncertainty at airport arrival operations by relating index levels with airport performance results. Findings The results are computed from a data set of over 750,000 flights on a major European hub and from local weather data during the period 2015–2018. This study combines delay and capacity metrics at different airport operational stages for the arrival process (final approach, taxi-in and in-block). Therefore, the spatial boundary of this study is not only the airport but also its surrounding airspace, to take both the arrival sequencing and metering area and potential holding patterns into consideration. Originality/value This study introduces a new approach for modeling causal relationships between airport arrival performance indicators and meteorological events, which can be used to quantify the impact of weather in airport arrival conditions, predict the evolution of airport operational scenarios and support airport decision-making processes.

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Untitled

Cited by 56 publications

References 36 publications

Potential global jamming transition in aviation networks

Potential global jamming transition in aviation networks

Uncertainty Management at the Airport Transit View

Impact of weather conditions on airport arrival delay and throughput

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