An agent-based model for air transportation to capture network effects in assessing delay management mechanisms

Gurtner, G.; Delgado, L.; Valput, D.

doi:10.1016/j.trc.2021.103358

Cited by 14 publications

(8 citation statements)

References 21 publications

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“…Bouarfa et al (2018) use a multi-agent system to propose a disruption management policy for an airport operations centre. Gurtner et al (2021) propose an agent-based air traffic delay management model through four-dimensional trajectory adjustment in a European airport network. De La Vega et al (2022) propose to solve a short-term flight rescheduling problem to recover flight delays in the context where employees of a gas and oil company need to be transported to maritime units.…”

Section: Tactical Handling Of Airside Operationsmentioning

confidence: 99%

See 1 more Smart Citation

An ILP approach for tactical flight rescheduling during airport access mode disruptions

Scozzaro,

Mancel,

Delahaye

et al. 2023

Int Trans Operational Res

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Airport access mode disruptions, such as a subway shutdown, threaten the whole passenger door‐to‐door journey. When such a disruptive event occurs, knowledge of passengers' delays would help the airport operation centre to decide if a departure flight should be delayed. This paper proposes a tactical flight rescheduling at an airport to minimise the number of stranded passengers while considering operational constraints. An integer linear programming formulation of the problem is presented. Constraints such as terminal capacities, maximal runway throughput, minimum turnaround time or minimum transfer time for connecting passengers are considered. An exact and heuristic resolution is proposed and compared to a study case around Paris‐Charles de Gaulle airport. The new schedule satisfies the operational constraints and reduces up to 60% of the number of stranded passengers with moderate deviation from the initial planning.

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Section: Tactical Handling Of Airside Operationsmentioning

confidence: 99%

“…Gurtner et al. (2021) propose an agent‐based air traffic delay management model through four‐dimensional trajectory adjustment in a European airport network. De La Vega et al.…”

Section: Literature Reviewmentioning

confidence: 99%

An ILP approach for tactical flight rescheduling during airport access mode disruptions

Scozzaro,

Mancel,

Delahaye

et al. 2023

Int Trans Operational Res

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“…Individual passenger itineraries (with their connections) are modelled based on historical data from IATA's PaxIS and Global distribution Systems datasets, as in previous research projects [12]. Figure 5 shows the different passenger groups with connections at EDDF for the flight under study.…”

Section: Scenario and Case Studiesmentioning

confidence: 99%

“…A significant number of passengers (greater than 40) have a flight departing earlier than 12h. A first increment on passenger related costs is then expected around 10h20, which would correspond to the group of 20 passengers missing their connection (considering the minimum connecting time, if the flight arrives after 10h20 they won't be able to reach their connecting flight [12]). This can be observed in Figure 3.…”

Section: Scenario and Case Studiesmentioning

confidence: 99%

In-flight cost index optimisation upon weather forecast updates

Prats

Torre

Delgado

2022

2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC)

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This paper presents an optimisation framework to compute the altitude and speed profiles of a trajectory in the execution phase of the flight, such that the expected total cost (ETC) of the operation is minimised (i.e. modelling the expected cost of delay and fuel -including arrival uncertainties -at the arrival gate). This is achieved with a two-stage optimisation strategy: a trajectory optimiser that minimises a generalised direct operating cost function, for a given cost index; and an upper-level optimiser, which obtains the best cost index that minimises the ETC. Several case studies are presented for different departure delays, while considering the impact of two different weather forecast updates too: a region with relative high head-winds appearing half way across teh flight; and a cold atmosphere scenario, with a tropopause altitude lower than standard conditions. ETC savings with respect to following the operational flight plan increase with departure delay, as expected. Due to the non-linearity of the cost function, however, the benefits of considering the weather update depend on the actual value of the departure delay, showing the convenience of integrating the proposed approach into a crew decision support tool in order to avoid sub-optimal decisions.

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“…The characteristics of departure and arrival airports are also considered as shown in Table I. We used the size of the airports, and if it is used as a hub by the airline [15].…”

Section: A Input Featuresmentioning

confidence: 99%

Pre-tactical prediction of ATFM delay for individual flights

Mas-Pujol

Falco

Salamí

et al. 2022

2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC)

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Airlines develop an operation plan, during the day prior to operations (D-1), to identify potential network issues and prepare potential pre-tactical preventing measures such as aircraft tail swapping or crew reassignment to be applied on D0. Flights might experience discrepancies between their plan and execution due to many different factors, and in particular demand-capacity imbalances in the network leading to Air Traffic Flow Management (ATFM) regulations. Dispatcher3, a Clean Sky 2 innovation action, focuses on the use of machine learning techniques to support the airlines processes prior departure: dispatching, understood as the broad flight planning from the day prior to operations to the flight plan definition and selection, and advisories to pilot. This paper focuses on the estimation of ATFM delay for individual flights during the pre-tactical phase (D-1), which could help airspace users apply mitigation actions. Four machine learning models are developed to produce individual independent estimations with different level of granularity. The first two are binary classifier models that provide information on the probability of a given flight being affected by ATFM delay, and the reason for this delay (airport or airspace congestion). These models reported an accuracy between 75% and 88%. The later two models estimate the impact of the delay (amount of delay assigned to the flight if regulated), with a Mean Absolute Error close to 9.35 minutes.

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An agent-based model for air transportation to capture network effects in assessing delay management mechanisms

Cited by 14 publications

References 21 publications

An ILP approach for tactical flight rescheduling during airport access mode disruptions

An ILP approach for tactical flight rescheduling during airport access mode disruptions

In-flight cost index optimisation upon weather forecast updates

Pre-tactical prediction of ATFM delay for individual flights

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