Data-Driven Modeling of the Airport Configuration Selection Process

The runway configuration is the set of the runways at an airport that are used for arrivals and departures at any time. While many factors, including weather, expected demand, environmental considerations, and coordination of flows with neighboring airports, influence the choice of runway configuration, the actual selection decision is made by air traffic controllers in the airport tower. As a result, the capacity of an airport at any time is dependent on the behavior of human decision makers. This paper develops a statistical model to characterize the configuration selection decision process using empirical observations. The proposed approach, based on the discrete-choice modeling framework, identifies the influence of various factors in terms of the utility function of the decision maker. The parameters of the utility functions are estimated through likelihood maximization. Correlations between different alternatives are captured using a multinomial “nested logit” model. A key novelty of this study is the quantitative assessment of the effect of inertia, or the resistance to configuration changes, on the configuration selection process. The developed models are used to predict the runway configuration 3 h ahead of time, given operating conditions such as wind, visibility, and demand. Case studies based on data from Newark (EWR) and LaGuardia (LGA) airports show that the proposed model predicts runway configuration choices significantly better than a baseline model that only considers the historical frequencies of occurrence of different configurations.National Science Foundation (U.S.) (Grant 1239054

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

Adoption of Industry 4.0 technologies in airports -- A systematic literature review

Airports have been constantly evolving and adopting digital technologies to improve operational efficiency, enhance passenger experience, generate ancillary revenues and boost capacity from existing infrastructure. The COVID-19 pandemic has also challenged airports and aviation stakeholders alike to adapt and manage new operational challenges such as facilitating a contactless travel experience and ensuring business continuity. Digitalisation using Industry 4.0 technologies offers opportunities for airports to address short-term challenges associated with the COVID-19 pandemic while also preparing for future long-term challenges that ensue the crisis. Through a systematic literature review of 102 relevant articles, we discuss the current state of adoption of Industry 4.0 technologies in airports, the associated challenges as well as future research directions. The results of this review suggest that the implementation of Industry 4.0 technologies is slowly gaining traction within the airport environment, and shall continue to remain relevant in the digital transformation journeys in developing future airports

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

Airspace analysis for greener operations: towards more adoptability and predictability of continuous descent approach (cda)

Continuous Descent Approach (CDA), also known as Optimized Profile Descent (OPD), is the advanced flight technique for commercial aircraft to descend continuously from cruise altitude to Final Approach Fix (FAF) or touchdown without level-offs and with- or near-idle thrust setting. Descending using CDA, aircraft stays as high as possible for longer time thereby expanding the vertical distance between aircraft\u27s sources of noise and ground, and thus significantly reducing the noise levels for populated areas around airports. Also, descending with idle engines, fuel burn is reduced resulting in reduction of harmful emissions to the environment and fuel consumption to air carriers. Due to safety considerations, CDA procedures may require more separation between aircraft, which could reduce the full utilization of runway capacity. Thus, CDA has been limited to low to moderate traffic levels at airports. Several studies in literature have used various approaches to present solutions to the problem of increasing the CDA implementation during periods of high traffic at airports. However, insufficient attention was given to define thresholds that would help Air Traffic Controllers (ATC) to manage and accommodate more CDA operations, strategically and tactically. Bridging this gap is the main intent of this work. This research focus is on increasing CDA operations at airports during high traffic levels by considering factors that impact its CDA adoption as they relate to airports\u27 demographics, and airspace around them {known as terminal maneuvering area (TMA)}. To capture the effect of these factors on CDA Adoptability (CDA-A), in general, and CDA Predictability (CDA-P), at the operational level, two (2) approaches are introduced. The CDA-A model defines and captures the maximum level of traffic threshold for CDA adoption. The model captures the factors affecting CDA in a single measure, which are designated collectively as the Probability of Blocking. It is defined as the fraction of time an aircraft\u27s request to embark on CDA is denied. The denial could emanate from safety concerns as well as other operational conditions, such as the congestion of the stacking space within the TMA. This metric should enhance ATC on the strategic level to increasing CDA operations during possibly higher traffic than normally the case. The other approach is for a CDA-P. This model is developed based on data-driven system approach. It extracts traffic features, such as aircraft type and speed, altitude, and rate of descent; from actual flights data to aid in further operational utilization of CDA in real time. By accurately predicting CDA instances during high traffic at airports, the CDA-P model should assist ATC manage adopting more CDA operations during periods of high demand. Through its framework, the CDA-P model utilizes Feature Engineering and Hierarchal Clustering Analysis, to facilitate descent profile visualization and labeling, for building, training, testing, and validation of CDA predictive models using Decision Trees with AdaBoost and Support Vector Machines (SVM). The CDA-P model is validated using actual flight data operated at Nashville Int\u27l Airport (BNA)

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

Future Transportation

Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others

Data-Driven Modeling of the Airport Configuration Selection Process

The runway configuration is the set of the runways at an airport that are used for arrivals and departures at any time. While many factors, including weather, expected demand, environmental considerations, and coordination of flows with neighboring airports, influence the choice of runway configuration, the actual selection decision is made by air traffic controllers in the airport tower. As a result, the capacity of an airport at any time is dependent on the behavior of human decision makers. This paper develops a statistical model to characterize the configuration selection decision process using empirical observations. The proposed approach, based on the discrete-choice modeling framework, identifies the influence of various factors in terms of the utility function of the decision maker. The parameters of the utility functions are estimated through likelihood maximization. Correlations between different alternatives are captured using a multinomial “nested logit” model. A key novelty of this study is the quantitative assessment of the effect of inertia, or the resistance to configuration changes, on the configuration selection process. The developed models are used to predict the runway configuration 3 h ahead of time, given operating conditions such as wind, visibility, and demand. Case studies based on data from Newark (EWR) and LaGuardia (LGA) airports show that the proposed model predicts runway configuration choices significantly better than a baseline model that only considers the historical frequencies of occurrence of different configurations. National Science Foundation (U.S.) (Grant 1239054) Document type: Articl