Control and optimization algorithms for air transportation systems

Modern air transportation systems are complex cyber-physical networks that are critical to global travel and commerce. As the demand for air transport has grown, so have congestion, flight delays, and the resultant environmental impacts. With further growth in demand expected, we need new control techniques, and perhaps even redesign of some parts of the system, in order to prevent cascading delays and excessive pollution. In this survey, we consider examples of how we can develop control and optimization algorithms for air transportation systems that are grounded in real-world data, implement them, and test them in both simulations and in field trials. These algorithms help us address several challenges, including resource allocation with multiple stakeholders, robustness in the presence of operational uncertainties, and developing decision-support tools that account for human operators and their behavior. Keywords: Air transportation; Congestion control; Large-scale optimization; Data-driven modeling; Human decision processe

Stochastic Modelling of Aircraft Queues: A Review

In this paper we consider the modelling and optimal control of queues of aircraft waiting to use the runway(s) at airports, and present a review of the related literature. We discuss the formulation of aircraft queues as nonstationary queueing systems and examine the common assumptions made in the literature regarding the random distributions for inter-arrival and service times. These depend on various operational factors, including the expected level of precision in meeting pre-scheduled operation times and the inherent uncertainty in airport capacity due to weather and wind variations. We also discuss strategic and tactical methods for managing congestion at airports, including the use of slot controls, ground holding programs, runway configuration changes and aircraft sequencing policies

Deep Learning Prediction Models for Runway Configuration Selection and Taxi Times Based on Surface Weather

Growth in air traffic demand in the United States has led to an increase in ground delays at major airports in the nation. Ground delays, including taxi time delays, directly impacts the block time and block fuel for flights which affects the airlines operationally and financially. Additionally, runway configuration selection at an airport significantly impacts the airport capacity, throughput, and delays as it is vital in directing the flow of air traffic in and out of an airport. Runway configuration selection is based on interrelated factors, including weather variables such as wind and visibility, airport facilities such as instrument approach procedures for runways, noise abatement procedures, arrival and departure demand, and coordination of ATC with neighboring airport facilities. The research problem of this study investigated whether runway configuration selection and taxi out times at airports can be predicted with hourly surface weather observations. This study utilized two sequence-to-sequence Deep Learning architectures, LSTM encoderdecoder and Transformer, to predict taxi out times and runway configuration selection for airports in MCO and JFK. An input sequence of 12 hours was used, which included surface weather data and hourly departures and arrivals. The output sequence was set to 6 hours, consisting of taxi out times for the regression models and runway configuration selection for the classification models. For the taxi out times models, the LSTM encoder-decoder model performed better than the Transformer model with the best MSE for output Sequence 2 of 41.26 for MCO and 45.82 for JFK. The SHAP analysis demonstrated that the Departure and Arrival variables had the most significant contribution to the predictions of the model. For the runway configuration prediction tasks, the LSTM encoder-decoder model performed better than the Transformer model for the binary classification task at MCO. The LSTM encoder-decoder and Transformer models demonstrated comparable performance for the multiclass classification task at JFK. Out of the six output sequences, Sequence 3 demonstrated the best performance with an accuracy of 80.24 and precision of 0.70 for MCO and an accuracy of 77.26 and precision of 0.76 for JFK. The SHAP analysis demonstrated that the Departure, Dew Point, and Wind Direction variables had the most significant contribution to the predictions of the model

Applications of stochastic modeling in air traffic management : Methods, challenges and opportunities for solving air traffic problems under uncertainty

In this paper we provide a wide-ranging review of the literature on stochastic modeling applications within aviation, with a particular focus on problems involving demand and capacity management and the mitigation of air traffic congestion. From an operations research perspective, the main techniques of interest include analytical queueing theory, stochastic optimal control, robust optimization and stochastic integer programming. Applications of these techniques include the prediction of operational delays at airports, pre-tactical control of aircraft departure times, dynamic control and allocation of scarce airport resources and various others. We provide a critical review of recent developments in the literature and identify promising research opportunities for stochastic modelers within air traffic management

Resource allocation in congested queueing systems with time-varying demand:An application to airport operations

Motivated by the need to develop time-efficient methods for minimizing operational delays at severely congested airports, we consider a problem involving the distribution of a common resource between two sources of time-varying demand. We formulate this as a dynamic program in which the objective is based on second moments of stochastic queue lengths and show that, for sufficiently high volumes of demand, optimal values can be well-approximated by quadratic functions of the system state. We identify conditions which enable the strong performance of myopic policies and develop approaches to the design of heuristic policies by means of approximate dynamic programming (ADP) methods. Numerical experiments suggest that our ADP-based heuristics, which require very little computational effort, are able to improve substantially upon the performances of more naive decision-making policies, particularly if exogenous system parameters vary considerably as functions of time

A System Level Study of New Wake Turbulence Separation Concepts and Their Impact on Airport Capacity

The air transportation industry continues to grow worldwide, but demand is often limited by available airspace and airport capacity. This thesis focuses on evaluating new air traffic procedures: specifically, new and emerging wake turbulence separation rules that could potentially increase runway capacity based on today’s knowledge of wake vortex turbulence and technological capabilities. While legacy wake separation rules establish aircraft-classes based on weight of aircraft, these new separation rules can define separation standards by considering other aircraft parameters and dynamic wind conditions. A fast-time runway system model is developed for studying these wake separation rules, using Monte-Carlo simulations, to provide accurate and realistic runway capacity estimates based on the randomness of arrival and departure operations. A total of nine new proposed wake separation rules are analyzed in detail, which include both distance-based and time-based methods, as well as static and dynamic concepts. Seven of the busiest and most delayed U.S. airports are selected as case studies for the illustration of runway capacity benefits enabled by these new wake separation rules: Boston (BOS), New York J.F. Kennedy (JFK), New York LaGuardia (LGA), Newark (EWR), San Francisco (SFO), Los Angeles (LAX), and Chicago O’Hare (ORD). For a detailed capacity analysis, the new wake separation rules are tested under the most constraining runway configurations at each of these airports. The results indicate that increasing the number of aircraft wake categories can increase runway capacity, but the added benefits become smaller with each new category added. A five-or six-category wake separation system can capture most of the runway capacity that can be achieved with a static pair-wise system. Additionally, shifting wake category boundaries between airports as a function of local fleet mix can provide additional runway capacity benefits, meaning that airport specific wake separation rules can increase capacity over a universal separation rule system. Among the new wake separation rules, the results indicate that reducing wake separations further from current minimum separations (separation values of 2NM or less) can shift the operational bottleneck from the approach path to the runway, as runway occupancy time becomes the limiting factor for inter-arrival separations. The findings from the time-based separation rule demonstrate that switching from distance-based separations to time-based separations in strong headwind conditions can recover significant lost capacity. Time-based separation rules can be of great value 4 to increase operational reliability and capacity predictability at airports in all weather conditions. Moreover, the results also indicate that a reduction in minimum separations enabled by dynamic wind and aircraft information can offer marginal runway capacity benefits over the capacity enabled by static pair-wise wake separations, as more and more aircraft pairs become limited by runway occupancy time. Therefore, a joint effort is needed for reducing both wake separations and runway occupancy in order to accommodate future air traffic demand.This project was funded under the FAA NEXTOR II Center of Excellence

La capacità aeroportuale: valutazione, gestione ed ottimizzazione

Il presente lavoro affronta il tema della capacità aeroportuale. Si forniscono alcuni degli strumenti normativi e gestionali che aiutano a ripartire la capacità tra vettori nei casi in cui essa non sia in grado di soddisfare completamente la domanda. Si descrivono alcuni strumenti che consentono la valutazione della capacità dell’infrastruttura (lato airside) ed un metodo di ottimizzazione, caratterizzato da un adattamento dinamico della capacità alla domanda di trasport

Air traffic flow management at airports : a unified optimization approach

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 137-140).The cost of air traffic delays is well documented, and furthermore, it is known that the significant proportion of delays is incurred at airports. Much of the air traffic flow management literature focuses on traffic flows between airports in a network, and when studies have focused on optimizing airport operations, they have focused largely on a single aspect at a time. In this thesis, we fill an important gap in the literature by proposing unified approaches, on both strategic and tactical levels, to optimizing the traffic flowing through an airport. In particular, we consider the entirety of key problems faced at an airport: a) selecting a runway configuration sequence; b) determining the balance of arrivals and departures to be served; c) assigning flights to runways and determining their sequence; d) determining the gate-holding duration of departures and speedcontrol of arrivals; and e) routing flights to their assigned runway and onwards within the terminal area. In the first part, we propose an optimization approach to solve in a unified manner the strategic problems (a) and (b) above, which are addressed manually today, despite their importance. We extend the model to consider a group of neighboring airports where operations at different airports impact each other due to shared airspace. We then consider a more tactical, flight-by-flight, level of optimization, and present a novel approach to optimizing the entire Airport Operations Optimization Problem, made up of subproblems (a) - (e) above. Until present, these have been studied mainly in isolation, but we present a framework which is both unified and tractable, allowing the possibility of system-optimal solutions in a practical amount of time. Finally, we extend the models to consider the key uncertainties in a practical implementation of our methodologies, using robust and stochastic optimization. Notable uncertainties are the availability of runways for use, and flights' earliest possible touchdown/takeoff times. We then analyze the inherent trade-off between robustness and optimality. Computational experience using historic and manufactured datasets demonstrates that our approaches are computationally tractable in a practical sense, and could result in cost benefits of at least 10% over current practice.by Michael Joseph Frankovich.Ph.D

Estimation and tactical allocation of airport capacity in the presence of uncertainty

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, February 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 209-215).Major airports in the United States and around the world have seen an increase in congestion-related delays over the past few years. Because airport congestion is caused by an imbalance between available capacity and demand, the efficient use of available capacity is critical to mitigating air traffic delays. A frequently-adopted traffic management initiative, the Ground Delay Program (GDP), is initiated when an airport expects congestion, either because of very high demand or a reduction in its capacity. The GDP is designed to efficiently allocate the limited airport capacity among the scheduled flights. However, contemporary GDP practice allocates delays to arrivals independent of departures, and relies on deterministic capacity forecasts. This thesis designs and evaluates a GDP framework that simultaneously allocates arrival and departure delays, and explicitly accounts for uncertainty in capacity forecasts. Efficient capacity allocation requires the accurate estimation of available airport capacity. The first module of this thesis focuses on the modeling of airport capacity and its dynamics. A statistical model based on quantile regression is developed to estimate airport capacity envelopes from empirical observations of airport throughput. The proposed approach is demonstrated through a case study of the New York metroplex system that estimates arrival-departure capacity tradeoffs, both at individual airports and between pairs of airports. The airport capacity envelope that is valid at any time depends on the prevailing weather (visibility) and the runway configuration. This thesis proposes a discrete choice framework for modeling the selection of airport runway configurations, given weather and demand forecasts. The model is estimated and validated for Newark (EWR) and LaGuardia (LGA) airports using archived data. The thesis also presents a methodology for quantifying the impact of configuration switches on airport capacity, and applies it to EWR and Dallas Fort Worth (DFW) airports. The second module of this thesis extends two existing stochastic ground-holding models from literature, the static and the dynamic, by incorporating departure capacity considerations to existing arrivals-only formulations. These integer stochastic formulations aim to minimize expected system delay costs, assuming uniform unit delay costs for all flights. The benefits of the integrated stochastic framework are demonstrated through representative case studies featuring real-world GDP data. During GDPs, the Collaborative Decision-Making framework provides mechanisms, termed intra-airline substitution and compression, which allow airlines to redistribute slots assigned by ground-holding models to their flights, depending on flight-specific delay costs. The final part of this dissertation considers collaborative decision-making during GDPs in stochastic settings. The analysis reveals an inherent trade-off between the delay costs achieved by the static and the dynamic stochastic models before and after the application of the CDM mechanisms. A hybrid stochastic ground-holding model that combines the desirable properties of the static and dynamic models is then proposed. The performance of the three stochastic ground-holding models under CDM are evaluated through real-world case studies, and the robustness of the final system delay cost reduction achieved by the hybrid model for a range of operating scenarios is demonstrated.by Varun Ramanujam.Ph.D