Uncertainty management at the airport transit view

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

Stochastic Delay Cost Functions to Estimate Delay Propagation under Uncertainty

We provide a mathematical formulation of flight-specific delay cost functions that enables a detailed tactical consideration of how a given flight delay will interact with all downstream constraints in the respective aircraft rotation. These functions are reformulated into stochastic delay cost functions to respect conditional probabilities and increasing uncertainty related to more distant operational constraints. Conditional probabilities are learned from historical operations data, such that typical delay propagation patterns can support the flight prioritization process as a part of tactical airline schedule recovery. A case study compares the impact of deterministic and stochastic cost functions on optimal recovery decisions during an airport constraint. We find that deterministic functions systematically overestimate potential disruption costs as well as optimal schedule recovery costs in high delay situations. Thus, an optimisation based on stochastic costs outperforms the deterministic approach by up to 15%, as it reveals ’hidden’ downstream recovery potentials. This results in different slot allocations and in fewer passengers missing their connections

Flight and passenger delay assignment optimization strategies

This paper compares different optimization strategies for the minimization of flight and passenger delays at two levels: pre-tactical, with on-ground delay at origin, and tactical, with airborne delay close to the destination airport. The optimization model is based on the ground holding problem and uses various cost functions. The scenario considered takes place in a busy European airport and includes realistic values of traffic. A passenger assignment with connections at the hub is modeled. Statistical models are used for passenger and connecting passenger allocation, minimum time required for turnaround and tactical noise; whereas uncertainty is also introduced in the model for tactical noise. Performance of the various optimization processes is presented and compared to ration by schedule results

Hub operations delay recovery based on cost optimisation - Dynamic cost indexing and waiting for passengers strategies

In this paper, two strategies for airlines’ operations at a hub are combined and analysed: dynamic cost indexing, to recover delay, and waiting for connecting passengers at the hub. Agent Based Modelling techniques have been used to model the airlines’ operations considering detailed passenger’s itineraries, an extended arrival manager operation with slot negotiation, and delay and uncertainty at different phases of the flights. Results show that, when optimising the total cost, there is a trade-off between connecting and non-connecting passengers with respect to the gate to gate trip time. Waiting for passengers arises as an interesting technique when minimising airline operating costs

A stochastic and dynamic model of delay propagation within an airport network for policy analysis

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 223-227).As demand for air travel increases over the years and many busy airports operate close to their capacity limits, congestion at some airports on any given day can quickly spread throughout the National Aviation System (NAS). It is therefore increasingly important to study the operation of large networks of airports as a group and to understand better the interactions among them under a wide range of conditions. This thesis develops a fundamental tool for this purpose, enhances it with several capabilities designed to address issues of particular interest, and presents some early insights and observations on the system-wide impacts of various scenarios of network-wide scope. We first describe an analytical queuing and network decomposition model for the study of delays and delay propagation in a large network of airports. The Airport Network Delays (AND) model aims to bridge the gap in the existing modeling tools between micro-simulations that track aircraft itineraries, but require extensive resources and computational effort, and macroscopic models that are simple to use, but typically lack aircraft itinerary tracking capabilities and credible queuing models of airport congestion. AND operates by iterating between its two main components: a queuing engine (QE), which is a stochastic and dynamic queuing model that treats each airport in the network as a M(t)/Ek(t)/1 queuing system and is used to compute delays at individual airports and a delay propagation algorithm (DPA) that updates flight schedules and demand rates at all the airports in the model in response to the local delays computed by the QE. We apply AND to two networks, one consisting of the 34 busiest airports in the United States and the other of the 19 busiest in Europe. As part of the development of AND, we perform a statistical analysis of the minimum ground turn-around times of aircraft, one of the fundamental variables that determine delay propagation. In addition, we show that the QE, with proper calibration, can model very accurately the airport departure process, predicting delays at two major US airports within 10% of observed values. We also validate the AND model on a network-wide scale against field data reported by the FAA. Finally, we present insights into the complex interactions through which delays propagate through a network of airports and the often-counterintuitive consequences. In the third part of the thesis, we present two important extensions of the AND model designed to expand its usability and applicability. First, in order to provide a more accurate representation of NAS operations, we develop an algorithm that replicates quite accurately the execution of Ground Delay Programs (GDPs). The algorithm operates consistently with the rules of the Collaborative Decision-Making (CDM) process under which GDPs are currently conducted in the United States. The second extension is the implementation in AND of a deterministic queuing engine (D(t)/D(t)/1) which can be used as an alternative to the original stochastic QE. This deterministic model can be used to study delay-related performance in a future system that operates at a higher level of predictability than the current one, as the one envisioned by FAA in the Next Generation Air Transportation System. In the final part of the thesis we describe a Mixed Integer optimization model for studying the impact of introducing slot controls at busy airports. The model generates new flight schedules at airports by reducing the number of available slots, while respecting all existing aircraft itineraries and preserving all passenger connections. We test the model at Newark Airport (EWR) and conclude that, with a small schedule displacement (less than 30 minutes for any flight during the day), it is possible to obtain a feasible schedule that obeys slot limits that are as low as the IFR capacity of the airport. We test the new schedule in AND and find that the local delay savings that would result from "slot-controlling" EWR in this way are of the order of 10% for arrivals and of 50% for departures, while we may also expect a reduction of 23% in propagated delays to the rest of the US network of airports.by Nikolaos Pyrgiotis.Ph.D

Resource-Constrained Airline Ground Operations: Optimizing Schedule Recovery under Uncertainty

Die zentrale europäische Verkehrsflusssteuerung (englisch: ATFM) und Luftverkehrsgesellschaften (englisch: Airlines) verwenden unterschiedliche Paradigmen für die Priorisierung von Flügen. Während ATFM jeden Flug als individuelle Einheit betrachtet, um die Kapazitätsauslastung aller Sektoren zu steuern, bewerten Airlines jeden Flug als Teilabschnitt eines Flugzeugumlaufes, eines Crew-Einsatzplanes bzw. einer Passagierroute. Infolgedessen sind ATFM-Zeitfenster für Flüge in Kapazitätsengpässen oft schlecht auf die Ressourcenabhängigkeiten innerhalb eines Airline-Netzwerks abgestimmt, sodass die Luftfahrzeug-Bodenabfertigung – als Verbindungselement bzw. Bruchstelle zwischen einzelnen Flügen im Netzwerk – als Hauptverursacher primärer und reaktionärer Verspätungen in Europa gilt. Diese Dissertation schließt die Lücke zwischen beiden Paradigmen, indem sie ein integriertes Optimierungsmodell für die Flugplanwiederherstellung entwickelt. Das Modell ermöglicht Airlines die Priorisierung zwischen Flügen, die von einem ATFM-Kapazitätsengpass betroffen sind, und berücksichtigt dabei die begrenzte Verfügbarkeit von Abfertigungsressourcen am Flughafen. Weiterhin werden verschiedene Methoden untersucht, um die errechneten Flugprioritäten vertraulich innerhalb von kooperativen Lösungsverfahren mit externen Stakeholdern austauschen zu können. Das integrierte Optimierungsmodell ist eine Erweiterung des Resource-Constrained Project Scheduling Problems und integriert das Bodenprozessmanagement von Luftfahrzeugen mit bestehenden Ansätzen für die Steuerung von Flugzeugumläufen, Crew-Einsatzplänen und Passagierrouten. Das Modell soll der Verkehrsleitzentrale einer Airline als taktische Entscheidungsunterstützung dienen und arbeitet dabei mit einer Vorlaufzeit von mehr als zwei Stunden bis zur nächsten planmäßigen Verkehrsspitze. Systemimmanente Unsicherheiten über Prozessabweichungen und mögliche zukünftige Störungen werden in der Optimierung in Form von stochastischen Prozesszeiten und mittels des neu-entwickelten Konzeptes stochastischer Verspätungskostenfunktionen berücksichtigt. Diese Funktionen schätzen die Kosten der Verspätungsausbreitung im Airline-Netzwerk flugspezifisch auf der Basis historischer Betriebsdaten ab, sodass knappe Abfertigungsressourcen am Drehkreuz der Airline den kritischsten Flugzeugumläufen zugeordnet werden können. Das Modell wird innerhalb einer Fallstudie angewendet, um die taktischen Kosten einer Airline in Folge von verschiedenen Flugplanstörungen zu minimieren. Die Analyseergebnisse zeigen, dass die optimale Lösung sehr sensitiv in Bezug auf die Art, den Umfang und die Intensität einer Störung reagiert und es folglich keine allgemeingültige optimale Flugplanwiederherstellung für verschiedene Störungen gibt. Umso dringender wird der Einsatz eines flexiblen und effizienten Optimierungsverfahrens empfohlen, welches die komplexen Ressourcenabhängigkeiten innerhalb eines Airline-Netzwerks berücksichtigt und kontextspezifische Lösungen generiert. Um die Effizienz eines solchen Optimierungsverfahrens zu bestimmen, sollte das damit gewonnene Steuerungspotenzial im Vergleich zu aktuell genutzten Verfahren über einen längeren Zeitraum untersucht werden. Aus den in dieser Dissertation analysierten Störungsszenarien kann geschlussfolgert werden, dass die flexible Standplatzvergabe, Passagier-Direkttransporte, beschleunigte Abfertigungsverfahren und die gezielte Verspätung von Abflügen sehr gute Steuerungsoptionen sind und während 95 Prozent der Saison Anwendung finden könnten, um geringe bis mittlere Verspätungen von Einzelflügen effizient aufzulösen. Bei Störungen, die zu hohen Verspätungen im gesamten Airline-Netzwerk führen, ist eine vollständige Integration aller in Betracht gezogenen Steuerungsoptionen erforderlich, um eine erhebliche Reduzierung der taktischen Kosten zu erreichen. Dabei ist insbesondere die Möglichkeit, Ankunfts- und Abflugzeitfenster zu tauschen, von hoher Bedeutung für eine Airline, um die ihr zugewiesenen ATFM-Verspätungen auf die Flugzeugumläufe zu verteilen, welche die geringsten Einschränkungen im weiteren Tagesverlauf aufweisen. Die Berücksichtigung von Unsicherheiten im nachgelagerten Airline-Netzwerk zeigt, dass eine Optimierung auf Basis deterministischer Verspätungskosten die taktischen Kosten für eine Airline überschätzen kann. Die optimale Flugplanwiederherstellung auf Basis stochastischer Verspätungskosten unterscheidet sich deutlich von der deterministischen Lösung und führt zu weniger Passagierumbuchungen am Drehkreuz. Darüber hinaus ist das vorgeschlagene Modell in der Lage, Flugprioritäten und Airline-interne Kostenwerte für ein zugewiesenes ATFM-Zeitfenster zu bestimmen. Die errechneten Flugprioritäten können dabei vertraulich in Form von optimalen Verspätungszeitfenstern pro Flug an das ATFM übermittelt werden, während die Definition von internen Kostenwerten für ATFM-Zeitfenster die Entwicklung von künftigen Handelsmechanismen zwischen Airlines unterstützen kann.:1 Introduction 2 Status Quo on Airline Operations Management 3 Schedule Recovery Optimization Approach with Constrained Resources 4 Implementation and Application 5 Case Study Analysis 6 ConclusionsAir Traffic Flow Management (ATFM) and airlines use different paradigms for the prioritisation of flights. While ATFM regards each flight as individual entity when it controls sector capacity utilization, airlines evaluate each flight as part of an aircraft rotation, crew pairing and passenger itinerary. As a result, ATFM slot regulations during capacity constraints are poorly coordinated with the resource interdependencies within an airline network, such that the aircraft turnaround -- as the connecting element or breaking point between individual flights in an airline schedule -- is the major contributor to primary and reactionary delays in Europe. This dissertation bridges the gap between both paradigms by developing an integrated schedule recovery model that enables airlines to define their optimal flight priorities for schedule disturbances arising from ATFM capacity constraints. These priorities consider constrained airport resources and different methods are studied how to communicate them confidentially to external stakeholders for the usage in collaborative solutions, such as the assignment of reserve resources or ATFM slot swapping. The integrated schedule recovery model is an extension of the Resource-Constrained Project Scheduling Problem and integrates aircraft turnaround operations with existing approaches for aircraft, crew and passenger recovery. The model is supposed to provide tactical decision support for airline operations controllers at look-ahead times of more than two hours prior to a scheduled hub bank. System-inherent uncertainties about process deviations and potential future disruptions are incorporated into the optimization via stochastic turnaround process times and the novel concept of stochastic delay cost functions. These functions estimate the costs of delay propagation and derive flight-specific downstream recovery capacities from historical operations data, such that scarce resources at the hub airport can be allocated to the most critical turnarounds. The model is applied to the case study of a network carrier that aims at minimizing its tactical costs from several disturbance scenarios. The case study analysis reveals that optimal recovery solutions are very sensitive to the type, scope and intensity of a disturbance, such that there is neither a general optimal solution for different types of disturbance nor for disturbances of the same kind. Thus, airlines require a flexible and efficient optimization method, which considers the complex interdependencies among their constrained resources and generates context-specific solutions. To determine the efficiency of such an optimization method, its achieved network resilience should be studied in comparison to current procedures over longer periods of operation. For the sample of analysed scenarios in this dissertation, it can be concluded that stand reallocation, ramp direct services, quick-turnaround procedures and flight retiming are very efficient recovery options when only a few flights obtain low and medium delays, i.e., 95% of the season. For disturbances which induce high delay into the entire airline network, a full integration of all considered recovery options is required to achieve a substantial reduction of tactical costs. Thereby, especially arrival and departure slot swapping are valuable options for the airline to redistribute its assigned ATFM delays onto those aircraft that have the least critical constraints in their downstream rotations. The consideration of uncertainties in the downstream airline network reveals that an optimization based on deterministic delay costs may overestimate the tactical costs for the airline. Optimal recovery solutions based on stochastic delay costs differ significantly from the deterministic approach and are observed to result in less passenger rebooking at the hub airport. Furthermore, the proposed schedule recovery model is able to define flight priorities and internal slot values for the airline. Results show that the priorities can be communicated confidentially to ATFM by using the concept of 'Flight Delay Margins', while slot values may support future inter-airline slot trading mechanisms.:1 Introduction 2 Status Quo on Airline Operations Management 3 Schedule Recovery Optimization Approach with Constrained Resources 4 Implementation and Application 5 Case Study Analysis 6 Conclusion

Domino D3.1 - Architecture definition

This deliverable presents the concept of operation of Domino. It includes a description of the systems, subsystems and processes that will be taken into account in the model, as well as the general scope of the model. For each of the mechanisms suggested to be modelled in the project, the deliverable provides a set of possible operational concepts and uptake/scope to be deployed

What cost reslience?

Air traffic management research lacks a framework for modelling the cost of resilience during disturbance. There is no universally accepted metric for cost resilience. The design of such a framework is presented and the modelling to date is reported. The framework allows performance assessment as a function of differential stakeholder uptake of strategic mechanisms designed to mitigate disturbance. Advanced metrics, cost- and non-cost-based, disaggregated by stakeholder subtypes, will be deployed. A new cost resilience metric is proposed

Passenger-Oriented Enhanced Metrics

We report on a project building the first European ATM simulation combining flight and passenger trip data. New propagation-centric and passenger-centric performance metrics are described. The new metrics will be compared with existing, classical metrics, to compare their respective intelligibility, sensitivity and consistency. The trade-offs in performance across the metrics under a range of flight and passenger prioritisation scenarios will be examined. The corresponding regulatory and socio-political contexts are described. Complexity science techniques demonstrate the need to extend flight-centric network representations to include the passenger perspective