Ground Delay Research Articles

How Fast You Can Actually Fly: A Comparative Investigation of Flight Airborne Time in China and the U.S.

Actual airborne time (AAT) is the time between wheels-off and wheels-on of a flight. Understanding the behavior of AAT is increasingly important given the ever growing demand for air travel and flight delays becoming more rampant. As no research on AAT exists, this paper performs the first empirical analysis of AAT behavior, comparatively for the U.S. and China. The focus is on how AAT is affected by scheduled block time (SBT), origin-destination (OD) distance, and the possible pressure to reduce AAT from other parts of flight operations. Multiple econometric models are developed. The estimation results show that in both countries AAT is highly correlated with SBT and OD distance. Flights in the U.S. are faster than in China. On the other hand, facing ground delay prior to takeoff, a flight has limited capability to speed up. The pressure from short turnaround time after landing to reduce AAT is immaterial. Sensitivity analysis of AAT to flight length and aircraft utilization is further conducted. Given the more abundant airspace, flexible routing networks, and efficient ATFM procedures, a counterfactual that the AAT behavior in the U.S. were adopted in China is examined. We find that by doing so significant efficiency gains could be achieved in the Chinese air traffic system. On average, 11.8 minutes of AAT per flight would be saved, coming from both reduction in SBT and reduction in AAT relative to the new SBT. Systemwide fuel saving would amount to over 300 million gallons with direct airline operating cost saving of nearly $1.3 billion nationwide in 2016.

Predictive and Prescriptive Analytics toward Passenger-centric Ground Delay Programs

Ground Delay Programs (GDP) comprise the main interventions to optimize flight operations in congested air traffic networks. The core GDP objective is to minimize flight delays, but this may result in optimal outcomes for passengers — especially with connecting itineraries. This paper proposes an original passenger-centric approach to GDP by leveraging data on passenger itineraries in flight networks. First, we identify analytical drivers of passenger-centric operations in transportation systems. Second, we develop an integer program that balances flight delays and passenger delays in large-scale GDP operations. A rolling procedure decomposes the problem while ensuring global feasibility, enabling the model’s implementation in short computational times. Third, we propose statistical learning models to predict passenger itineraries and optimize GDP operations accordingly, enabling the model’s implementation when passenger itineraries are unknown by air traffic managers. Computational results based on real-world data suggest that our modeling and computational framework can reduce passenger delays significantly at small increases in flight delay costs, and that these benefits are robust to imperfect knowledge of passenger itineraries. Results highlight two major levers of passenger-centric operations: (i) delay allocation (which flights to delay vs. prioritize), and (ii) delay introduction (whether to deliberately hold flights to avoid passenger misconnections).

STAM-based methodology to prevent concurrence events in a Multi-Airport System (MAS)

Pre-tactically managing the imbalances of the network and organizing the resources of the airspace is one of the main objectives of Dynamic Demand & Capacity Balancing (dDCB). Introducing Short-term Air Traffic Flow and Capacity Management (STAM) measures such as minor ground delays or slight speed adjustments applied to a selected number of flights on the day of operations, supports the dDCB process by reducing the traffic complexity and the probability of Air Traffic Controllers interventions. This paper introduces a rolling horizon methodology for detecting concurrence events, analysing their trajectory interdependencies and applying a strategic mitigation measure that shifts the Estimated Take-Off Time (ETOT) within the assigned Calculated Take-Off Time (CTOT) window. The methodology proposes a collaborative decision-making process for a better coordination of departures from airports feeding airspace volumes with high air traffic demand. The experimental part of this paper aims at showing the potential benefits of such a STAM procedure by applying the methodology to distribute the demand of a sector located in the London Terminal Manoeuvring Area (LTMA). Experiments have considered regulated traffic scenarios where ETOT were allocated in order to reduce departure time uncertainties. Lastly, it is analysed the impact of the parameters used by the methodology to calculate the mitigation measures: safe separation criteria, look-ahead time and number of coordinated airports.

An intergenerational investigation of air passengers’ emotions during tarmac delay

ABSTRACTFlight delay and its various forms have attracted the attention of scholars, media and airline regulators. Besides understanding the financial and productivity impact of such delays to the airline industry, the need to examine the emotional aspect of the affected airline passengers cannot be overlooked. Hence, the major intent of this qualitative study is to describe the emotions elicited by tarmac delays among a select group of air passengers from an intergenerational perspective. Emotional expressions (n = 1728) indicated by the 250 respondents were visually summarized via tag cloud analysis. The dominant emotions surfaced in this study across generations are valuable inputs in crafting successful affective customer relationship and ground delay programmes in the spirit of customization, consistency and creativity.

Using machine learning to analyze air traffic management actions: Ground delay program case study

We model the impact of weather and arrival demand on ground delay program (GDP) incidence. We use Support Vector Machine (SVM) to analyze how regional convective weather affects GDP incidence and find the impact depends on both distance and direction of convective activity from the airport. We then train and compare the performance of logistic regression (LR) and random forest (RF) in predicting GDP incidence using an SVM-generated regional weather variable, local weather and arrival demand. Generally, RF outperforms LR. Convective weather is the most important factor in predicting GDP incidence at Atlanta International Airport (ATL), while arrival demand has greater impact for the other airports studied. We also examined model transferability across different airports. Lastly, we build GDP duration prediction models to enable a user to assess how long a GDP is likely to continue, if it is in effect in a given hour.

Development of Hybrid Simulation Model of Air Traffic Management in the Terminal Control Area

The air traffic density at the Terminal Control Area can lead to several problems related to the safety and efficiency of flight operation if not adequately managed. These problems include the increased risk of crashes between aircraft, airborne and ground delays, waste of fuel and environmental problems caused by gas emission and noise. One of the analysis tools to solve these problems is the hybrid simulation model. The socio technic characteristics of the system can be observed more intact using a hybrid simulation model so that the results of the analysis more precise and comprehensive. This article describes the research framework and progress on the development of the hybrid simulation model of an ATM system in the Terminal Control Area. The final model will integrate of agent-based simulation, discrete event simulation, and system dynamics models. An agent-based simulation model that defines the movement of aircraft in the Terminal Control Area has been developed in this early research phase. The initial model can describe the rules for minimum separation between aircraft that are applied to ensure the safety of flight operations. The following aircraft will reduce its speed so that an adequate separation distance is obtained with the aircraft in front of it. The model then will be improved by integrating a discrete event simulation and system dynamics models on the initial model to obtain a complete hybrid simulation model.

A Combinatorial model to optimize air traffic flow management problems

In this paper we introduce a new 0–1 mathematical formulation for the Air Traffic Flow Management problem. The model is based on a 4D-graph, which allows us to consider the problem not as general combinatorial one, but as a set of shortest path problems with common capacity constraints, a fact that introduces several neat features. Among the decisions considered in the model are ground and air delays, changes in the speed of the aircraft and alternative routes. The proposed model, in comparison with the current state of the art, is shown to be an easy way to model different real complex situations (e.g., more realistic representation of costs and decisions involved, as well as dynamic sector configuration). The rapidity with which the computations are performed in this model shows the applicability of our proposal to the industry.

Prediction of Weather Impacts on Airport Arrival Meter Fix Capacity

<div class="section abstract"><div class="htmlview paragraph">This paper introduces a data driven model for predicting airport arrival capacity with 2-8 hour look-ahead forecast data. The model is suitable for air traffic flow management by explicitly investigating the impact of convective weather on airport arrival meter fix throughput. Estimation of the arrival airport capacity under arrival meter fix flow constraints due to severe weather is an important part of Air Traffic Management (ATM). Airport arrival capacity can be reduced if one or more airport arrival meter fixes are partially or completely blocked by convective weather. When the predicted airport arrival demands exceed the predicted available airport’s arrival capacity for a sustained period, Ground Delay Program (GDP) operations will be triggered by ATM system. Severe imbalances between demand and capacity occur most frequently when the airport capacity is severely degraded due to either bad airport terminal surface weather or inclement convective weather around airport arrival fixes. A model that predicts the weather-impacted airport arrival meter fix throughput may help ATM personnel to plan GDP operations more efficiently. This paper identifies the characteristics of air traffic flow across arrival meter fixes at Newark Liberty International Airport (EWR). The proposed approach, based on machine-learning methods, is developed to predict the weather impacted EWR arrival Meter Fix (MF) throughput. Sector forecast coverage is used to envision the weather impact on airport arrival MF flow, and the validation is accomplished by using Convective Weather Avoidance Model (CWAM) 0.5 to 2-hour and Collaborative Convective Forecast Product (CCFP) 4 to 8-hour look-ahead forecast data for the period of April-September in 2014. Furthermore, the regression tree ensemble learning of random forests approach for translating a sector forecast coverage model to EWR arrival meter fix throughput is examined. The results suggest that ATM decision makers in charge of MF flow control and GDP planning may benefit from adopting the airport arrival meter capacity prediction models to estimate the inclement weather impacts.</div></div>

Linear Holding for Airspace Flow Programs: A Case Study on Delay Absorption and Recovery

This paper presents a method to introduce linear holding to flights affected by the airspace flow program (AFP) initiatives. Trajectories are optimized at their planning stage in such a way that the program performance is improved in terms of delay absorption before the congested area, and delay recovery at the destination airport. This recovery process is studied by comparing the case where the same fuel consumption is fixed as the nominal flight, with several cases where some extra fuel allowances are considered at the flight planning stage. The effects for AFP delayed flights are thoroughly discussed in a case study followed by a sensitivity analysis on possible influential factors. Results suggest that using the proposed method could partially recover part of the AFP delay, even with no extra fuel allowances (e.g., reducing 3.3 min of ground delay and 1.7 min of arrival delay for a typical short-haul flight). When extra fuel is allowed, however, the maximum delay recovery increases up to 10 min for the studied case, which also proves to be more cost-efficient than current operations, when flight speed is increased after experiencing all delay on the ground.

Predicting and planning airport acceptance rates in metroplex systems for improved traffic flow management decision support

Efficient planning of Airport Acceptance Rates (AARs) is key for the overall efficiency of Traffic Management Initiatives such as Ground Delay Programs (GDPs). Yet, precisely estimating future flow rates is a challenge for traffic managers during daily operations as capacity depends on a number of factors/decisions with very dynamic and uncertain profiles. This paper presents a data-driven framework for AAR prediction and planning towards improved traffic flow management decision support. A unique feature of this framework is to account for operational interdependency aspects that exist in metroplex systems and affect throughput performance. Gaussian Process regression is used to create an airport capacity prediction model capable of translating weather and metroplex configuration forecasts into probabilistic arrival capacity forecasts for strategic time horizons. To process the capacity forecasts and assist the design of traffic flow management strategies, an optimization model for capacity allocation is developed. The proposed models are found to outperform currently used methods in predicting throughput performance at the New York airports. Moreover, when used to prescribe optimal AARs in GDPs, an overall delay reduction of up to 9.7% is achieved. The results also reveal that incorporating robustness in the design of the traffic flow management plan can contribute to decrease delay costs while increasing predictability.

Comprehensive Evaluation of Airspace and Ground Operation Simulation in Large-Scale Airport

The evaluation model of airport airspace and ground operation was constructed by using analytic hierarchy process (AHP). 5 airspace operation indexes were selected: potential impact to military aviation, airspace demand range, number of arriving and departure routes, flow equilibrium of entry and exit point, arrival air delays time. 7 evaluation indexes of ground operation were selected: number of daily departure and arrival flights, equilibrium of runway usage, departure ground delays time, ground taxiing time, usage equilibrium of main taxiway, turnover frequency of contact gate, air bridge ratio. The index scale method was used to determine the weight of each index, which had better ordering, consistency and uniformity. Taking Beijing New Airport as an example, the airspace and ground operational efficiency was evaluated comprehensively. The results show that the “3+1” runway configuration is better than “4+0”.

Exploration of the Evolution of Airport Ground Delay Programs

This study introduces a novel method of merging disparate but complementary datasets and applying machine learning techniques to ground delay program (GDP) data. More specifically, it aims to characterize GDPs with respect to changing weather forecasts, GDP plan parameters, and operational performance. The analysis aims to gain insights into GDP usage patterns (implementation and revisions), with respect to these key dimensions. It also aims to gain insights into how GDP cancelations and revisions correlate with operational efficiency and predictability. The results could be used to help traffic managers and air carriers understand complex patterns in the evolution of GDPs, so that they might, for example, better anticipate or even plan a response to a change in weather conditions. The focus is on GDPs at Newark Liberty International Airport (EWR), from 2010 through 2014. A master dataset was generated by merging several datasets on GDPs, weather forecasts, and individual flight information. Several scenarios of GDP evolution were then identified by reducing the dimensionality of the master GDP dataset, then applying cluster analysis on the lower dimensional data. It was found that GDPs at EWR can be categorized into 10 types based on weather forecasts, realized weather, GDP scope, arrival rates, and duration. The characteristics of these 10 GDP clusters were further explored by examining the relationships between GDP scenarios and their performance. It was found that GDPs under stable, low-severity weather and with large scope may score higher on the efficiency metric than expected. When GDPs called in the same weather conditions have high program rates, medium durations, and narrow scopes, capacity utilization was higher than expected—less affected flights lead to fewer cancelations and more arrivals (albeit delayed), and therefore, higher capacity utilization. Results also suggest that program rates are set more conservatively than needed for some poor weather conditions that end earlier than expected. GDPs with fewer revisions were associated with a higher predictability score but lower efficiency score. These findings can provide greater insights and knowledge about GDPs for future planning purposes. More specifically, the findings could, for example, be used to support discussion around, or even future guidance regarding, how to set and adjust GDP program rates. In future work additional data could be utilized to provide a more comprehensive operational picture of GDPs, and a wider range of performance metrics could be considered. It is also recommended that the patterns of how GDPs evolve over their lifetimes be further explored using other machine learning techniques that may provide new and useful insights.

Failures of critical systems at airports: Impact on aircraft operations and safety

Failures of critical systems at airports can significantly impact aircraft operations and cause substantial safety risks. This study develops a generalized method for measuring the impacts of unscheduled communication, navigation and surveillance systems outages on airport operational performance (i.e. throughput) and safety. The proposed method consists of two separate models – the aggregate and the disaggregate model. The aggregate model is a regression model that analyzes flight data, which are averaged per quarter hour and obtained from the Aviation System Performance Metrics database, and investigates the reductions in airport throughput due to an outage. The disaggregate model analyzes individual flights using the Performance Data Analysis and Reporting System database to investigate air traffic controllers’ terminal airspace traffic management procedures during an outage by examining the time-separation (i.e. headways) between consecutive arriving aircraft on each runway. More specifically, the model calculates the changes in the probability of a loss of separation and assesses the necessity of enacting a Traffic Management Initiative. The proposed method is tested on the case of two localizer outages at Los Angeles International Airport. The case-study results reveal that the throughput decreased after air traffic controllers enacted a Ground Delay Program during the first observed outage. The probability of a loss of separation doubled and the smaller aircraft were diverted to other airports. Further results indicate that arriving headways can be better described by a combination of two probability distributions, rather than a single distribution, in order to more accurately capture a complexity of air traffic controllers’ actions. The method can help aviation regulators: (1) make better decisions about the value of equipment and redundancies, (2) analyze operational and safety degradations caused by equipment failures, and (3) develop appropriate traffic management procedures and improve existing ones.

Stochastic models of Ground Delay Program implementation for prediction, simulation, and insight

We use historical data to build two types of stochastic model of hourly Ground Delay Program (GDP) implementation for the three main airports near New York City: Newark Liberty International, LaGuardia, and John F. Kennedy International. The models predict the probability that a GDP will be initial ized or canceled in a given hour based on hundreds of features describing the situation at the airport, including features describing forecasted weather conditions and scheduled traffic. One is a regularized logistic regression model that ignores system dynamics and the other model is based on inverse reinforcement learning, so it considers system dynamics and the impact that GDP implementation actions have over time on some metrics. We evaluate the models based on two objectives: their ability to predict and simulate GDP implementation decisions in historical test data sets. As is expected based on the motivation for and objective of each type of model, regularized logistic regression models make superior predictions while simulations controlled by inverse reinforcement learning models produce average metric values that more closely match those produced by historical GDP implementation decisions. Finally, we draw insights about GDP implementation from the trained model parameters. For example, parameters of both models suggest while weather conditions and values of performance metrics such as airborne delay play a role in GDP decision making, parameters related to the predictability or continuity of existing GDP plans are also important.

Predicting the initiation of a ground delay program

Predicting the initiation of a ground delay program

Airline-driven ground delay programs: A benefits assessment

Three decades of research studies in ground delay program (GDP) decision-making, and air traffic flow management in general, have produced several analytical models and decision support tools to design GDPs with minimum delay costs. Most of these models are centralized, i.e., the central authority almost completely decides the GDP design by optimizing certain centralized objectives. In this paper, we assess the benefits of an airline-driven decentralized approach for designing GDPs. The motivation for an airline-driven approach is the ability to incorporate the inherent differences between airlines when prioritizing, and responding to, different GDP designs. Such differences arise from the airlines’ diverse business objectives and operational characteristics. We develop an integrated platform for simulating flight operations during GDPs, an airline recovery module for mimicking the recovery actions of each individual airline under a GDP, and an algorithm for fast solution of the recovery problems to optimality. While some of the individual analytical components of our framework, model and algorithm share certain similarities with those used by previous researchers, to the best of our knowledge, this paper presents the first comprehensive platform for simulating and optimizing airline operations under a GDP and is the most important technological contribution of this paper. Using this framework, we conduct detailed computational experiments based on actual schedule data at three of the busiest airports in the United States. We choose the recently developed Majority Judgment voting and grading method as our airline-driven decentralized approach for GDP design because of the superior theoretical and practical benefits afforded by this approach as shown by multiple recent studies. The results of our evaluation suggest that adopting this airline-driven approach in designing the GDPs consistently and significantly reduces airport-wide delay costs compared to the state-of-the-research centralized approaches. Moreover, the cost reduction benefits of the resultant airline-driven GDP designs are equitably distributed across different airlines.

Stable two-sided matching of slot allocation in airport collaborative decision making by top trading cycles mechanism

The problem of capacity shortage in some airports needs to be dealt with sustainable solutions including a more efficient use of the existing runway slots at the airports. The Collaborative Decision Making (CDM) is an important approach applied to Air Traffic Management (ATM) to achieve this efficient use of the slots allocation. Using the Matching approach for two-sided markets of Game theory, the Top Trading Cycle CDM (TTC-CDM) algorithm developed in this research is an extension of the CDM approach aggregating the Ground Delay Program (GDP) of the air sector. The paper compared the developed TTC-CDM model to the existing models such as the conventional Compression algorithm in CDM, the Trade Cycle algorithm and the Deferred Acceptance CDM (DA-CDM) model to evaluate the performance of the proposed model. Through a case study, the results show the effective application of TTC-CDM model to slot allocation in ATM and also presents the advantage of considering the preferences of airport managers beside ATC controllers and airlines in the decision processing.

Risk of travel associated tuberculosis

Tuberculosis has spread by human movements throughout history. There have been reports indicating tuberculosis transmission on all travel vehicles, including aircrafts, ground vehicles and vessels until today. However, due to the ever increasing of air transportation and air travelling among countries with low and high tuberculosis incidence, transmission risk especially in aircrafts has become an important issue worldwide. But in many of the studies conducted in this regard, transmission of tuberculosis in aircrafts was found very low. The case of active tuberculosis has not yet been reported. This is due to the fact that in modern aircrafts, there are ventilation systems that provide hepa filtered laminar air flow and change the air 15-20 times per hour. The guidelines for the prevention of tuberculosis infection in aircrafts published by the World Health Organization "Tuberculosis and Air Travel, 2008" and European Centre for Disease Prevention and Control "RAGIDA-TB, 2014" confirm each other. According to these guidelines, air travelling of patients with active pulmonary tuberculosis should be prohibited until smears of two consecutive sputum samples become negative for drug susceptible cases, and cultures of two consecutive of sputum samples become negative for multidrug or extensively drug resistant cases. When it is reported that a tuberculosis patient has travelled by the aircraft, it is recommended that the exposed passengers should be investigated for tuberculosis infection if the flight duration equal to or exceeding eight hours including ground delays and the time elapsed between flight and diagnosis of the case is no longer than three months. Contact screening is only recommended for passengers sitting in the same row, two rows ahead and two rows behind the index case. Tuberculin skin test or interferon gamma release assay can be used for investigation. It is very difficult to determine the risk of tuberculosis transmission in ground vehicles like buses, subways and trains. The reason is that it is often not possible to access the information of the passengers travelling in these vehicles. Because the ventilation systems in ground vehicles are not as reliable as in aircrafts and the crowded environment in the ground vehicles, the risk of tuberculosis transmission is theoretically higher. In modelling studies, the transmission risk in the buses was found to be higher than the trains. In the case of regular travelling with an index case such as school bus riders, the risk increases significantly. The increased human population travelling all over the world nowadays has also raised concerns about travel-related tuberculosis risk. However, because of the limited evidence, it may be more efficient to spend time and resources for the other actions in order to prevent tuberculosis. In this review article, the transmission risk of tuberculosis in vehicles has been discussed.

DEMATEL-AHP technique to minimise departure delays due to airspace congestion: a case in Mactan-Cebu International Airport

Airspace congestion has a direct effect on departure schedules of aircraft. In events when there is a constrained airspace due to various factors such as adverse weather, departure delays may be potentially serious. Due to the complexity of the problem of choosing the best possible option in addressing airspace congestion, a combined multiple criteria decision making (MCDM) approach is used for the decision-making process. Furthermore, this paper incorporates the views of experts representing air traffic service and airlines management which is significant in choosing a viable solution to both stakeholders operating air transport. It has been found that both air traffic service and airlines management give most priority to safety, thus, prefer to apply for ground delay program during airspace congestion.

DEMATEL-AHP technique to minimise departure delays due to airspace congestion: a case in Mactan-Cebu International Airport

Airspace congestion has a direct effect on departure schedules of aircraft. In events when there is a constrained airspace due to various factors such as adverse weather, departure delays may be potentially serious. Due to the complexity of the problem of choosing the best possible option in addressing airspace congestion, a combined multiple criteria decision making (MCDM) approach is used for the decision-making process. Furthermore, this paper incorporates the views of experts representing air traffic service and airlines management which is significant in choosing a viable solution to both stakeholders operating air transport. It has been found that both air traffic service and airlines management give most priority to safety, thus, prefer to apply for ground delay program during airspace congestion.