Hartsfield-Jackson Atlanta International Airport Research Articles

Prediction of Hourly Airport Operational Throughput with a Multi-Branch Convolutional Neural Network

Extensive research in predicting annual passenger throughput has been conducted, aiming at providing decision support for airport construction, aircraft procurement, resource management, flight scheduling, etc. However, how airport operational throughput is affected by convective weather in the vicinity of the airport and how to predict short-term airport operational throughput have not been well studied. Convective weather near the airport could make arrivals miss their positions in the arrival stream and reduce airfield efficiency in terms of the utilization of runway capacities. This research leverages the learning-based method (MB-ResNet model) to predict airport hourly throughput and takes Hartsfield–Jackson Atlanta International Airport (ATL) as the case study to demonstrate the developed method. To indicate convective weather, this research uses Rapid Refresh model (RAP) data from the National Oceanic and Atmospheric Administration (NOAA). Although it is a comprehensive and powerful weather data product, RAP has not been widely used in aviation research. This study demonstrated that RAP data, after being carefully decoded, cleaned, and pre-processed, can play a significant role in explaining airfield efficiency variation. Applying machine learning/deep learning in air traffic management is an area worthy of the attention of aviation researchers. Such advanced artificial intelligence techniques can make use of big data from the aviation sector and improve the predictability of the national airspace system and, consequently, operational efficiency. The short-term airport operational throughput predicted in this study can be used by air traffic controllers and airport managers for the allocations of resources at airports to improve airport operations.

Air traffic density prediction using Bayesian ensemble graph attention network (BEGAN)

Air traffic density prediction is crucial to aviation safety and air traffic management (ATM). Understanding the complex spatial–temporal varying traffic patterns and the inter-dependencies of air traffic networks is especially demanding. To address these challenges, a novel knowledge-based deep learning framework, Bayesian ensemble graph attention network (BEGAN), is proposed to leverage the interactions of traffic density at different airspace grids and predict stochastic traffic density near the terminal. The core concept of BEGAN is to divide the large airspace into small overlapped gridded sections for individual training, and then assemble posterior distributions of density forecasts for large-scale prediction. The ensemble method improves the scalability of handling heterogeneous air traffic flow patterns. Algorithm-wise, a weighted directed graph interleaves the connection between different gridded airspaces. A parallel graph attention network (P-GAT) block followed by the long short-term memory (LSTM) network is designed to learn the spatial–temporal dependencies of the graph. Notably, P-GAT can incorporate existing domain knowledge into data-driven learning, such as historical trajectory paths, future flight plans, and airspace restrictions. The inclusion of existing knowledge and rules proves beneficial for air traffic density prediction in heavily regulated and controlled airspace. We evaluate BEGAN with real-world data collected from Hartsfield-Jackson Atlanta International Airport (KATL) and Orlando International Airport (KMCO). Extensive experiments show that the proposed framework can make accurate and robust predictions, outperforming nine baseline models. Based on the proposed study, limitations and future research directions are given.

Airport Ground Access Choice Between Transportation Network Companies and Parking: A Case Study of Hartsfield-Jackson Atlanta International Airport

Airport Ground Access Choice Between Transportation Network Companies and Parking: A Case Study of Hartsfield-Jackson Atlanta International Airport

4D flight trajectory prediction using a hybrid Deep Learning prediction method based on ADS-B technology: A case study of Hartsfield–Jackson Atlanta International Airport (ATL)

At the core of any flight schedule is the four dimensional (4D) trajectories which are comprised of three spatial dimensions with time added as the fourth dimension. Each trajectory contains spatial and temporal features that are associated with uncertainties that make the prediction process complex. Because of the increasing demand for air transportation, airports and airlines must have optimized schedules to best use the airports’ infrastructure potential. This is possible using advanced trajectory prediction methods. This paper proposes a novel hybrid deep learning model to extract spatial and temporal features considering the uncertainty for Hartsfield–Jackson Atlanta International Airport (ATL). Automatic Dependent Surveillance-Broadcast (ADS–B) with a vast amount of spatial and temporal flight attribute data, are used in this paper as input to the models. This research is conducted in three steps: (a) data preprocessing; (b) prediction by a hybrid Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) along with a three dimensional (3D-CNN) model; (c) The third and last step is the comparison of the model's performance with the proposed model by examining the experimental results. The deep model uncertainty is considered using Mont-Carlo dropout (MC-Dropout). Mont-Carlo dropouts are added to the network layers to enhance the model's prediction performance by a robust approach of switching off between different neurons. The results show that the proposed model has low error measurements compared to the other models (i.e., 3D CNN, CNN-GRU). The model with MC-dropout reduces the error further by an average of 21 %.

Airport surface movement prediction and safety assessment with spatial–temporal graph convolutional neural network

Collisions during airport surface operations can create risk of injury to passengers, crew or airport personnel and damage to aircraft and ground equipment. A machine learning model that is able to predict the trajectories of ground objects can help to diminish the occurrences of such collision events. In this paper, we pursue this objective by building a spatial–temporal graph convolutional neural network (STG-CNN) model to predict the movement of objects/vehicles on the airport surface. The methodology adopted in this paper consists of three steps: (1) Raw data processing: leverage Apache Spark to parse a large volume of raw data in Flight Information Exchange Model (FIXM) format streamed from the Surface Movement Event Service (SMES) for the purpose of deriving historical trajectory associated with each object on the ground; (2.1) Graph-based representations of ground object movements: build graph-based representations to characterize the movements of ground objects over time, where graph edges are used capture the spatial relationships of ground objects with each other explicitly; (2.2) Trajectory forecasts of all ground objects: combine STG-CNN with Time-Extrapolator Convolution Neural Network (TXP-CNN) to forecast the future trajectories of all the ground objects as a whole; and (3) Separation distance-based safety assessment: define a probabilistic separation distance-based metric to assess the safety of airport surface movements. The performance of the developed model for trajectory prediction of ground objects is validated at two airports with varying scales: Hartsfield-Jackson Atlanta International Airport and LaGuardia airport, under two different scenarios (peak hour and off-peak hour). Two quantitative performance metrics–Average Displacement Error (ADE) and Final Displacement Error (FDE) are used to compare the prediction performance of the proposed model with an alternative method. The computational results indicate that the developed method has an ADE within the range 7.55,9.33, and it significantly outperforms an alternative approach that combines a STG-CNN with Convolutional Long Short-Term Memory (ConvLSTM) neural network with an ADE of [15.79,16.89] in airport surface movement prediction, thus facilitating more accurate safety assessment during airport surface operations.

Impact of Airspace Restrictions on Urban Air Mobility Airport Shuttle Service Route Feasibility

Initial urban air mobility (UAM) operations are anticipated to include airport shuttle services between airport terminals and city centers. UAM airport shuttle operations have the potential to improve the customer experience and provide airports with new revenue streams but risk interfering with current airport operations. In this paper, we develop a discrete, heuristics-based optimization framework to explore the impact of airspace constraints and the placement of vertiports in the airport terminal area on the feasibility of UAM shuttle operations. The framework is applied to the terminal areas of Hartsfield-Jackson Atlanta International Airport (ATL), Dallas Fort Worth International Airport (DFW), and Los Angeles International Airport (LAX). Small-scale UAM operations are found to be feasible at all three airports with current terminal-area procedures, particularly if UAM aircraft are classified as helicopters. Scaled-up operations are found to be most feasible at ATL because of the possibility of segregating UAM aircraft from commercial traffic, whereas scaled-up operations at LAX and DFW are expected to require the adoption of new technologies, procedures, and regulations.

Structural relationships between safety attachment, experiential co-creation and future experiential intentions in the tourism industry

ABSTRACT This paper aims to explore the relationships between the dimensions of safety attachment, the dimensions of experiential co-creation and future experiential intentions. A convenience sample of 505 tourists who had departed from Hartsfield–Jackson Atlanta International Airport via direct flights to a destination outside the USA during the 2019-nCoV (COVID-19) outbreak was surveyed. The results will assist tourism managers in developing and implementing market-orientated service strategies to increase safety identity, safety dependence, safety affect, safety social bonding, experiential trust, experiential commitment and experiential connection in order to enable tourists to have future intentions to experience the destination during the COVID-19 outbreak. Findings are generalizable only to the tourism industry investigated.

Artificial neural network models for airport capacity prediction

This paper proposes artificial neural network models to predict the arrival/departure capacity of airports. Multilayer perceptron (MLP), recurrent neural networks (RNN), and long short-term memory (LSTM) models have been trained using capacity and meteorological data from Hartsfield–Jackson Atlanta International Airport (ATL) from 2013 to 2017. The models’ predictive performances were validated against the observed capacity of ATL in 2018. The qualitative and quantitative analysis of the trained models confirmed that the artificial neural networks approach is effective in predicting airport capacity. In addition, the transferability of the models for Boston Logan International Airport (BOS) is examined. Capacity prediction performance for BOS measures the transferability of the models trained with the ATL data. MLP showed good transferability without taking any other measures, and RNN and LSTM were able to predict the BOS capacity well after fine-tuning.

Efficiency drivers of international airports: A worldwide benchmarking study

The main purpose of these work was to identify operational efficiency drivers of international airports. In order to achieve this, a two-step quantitative-qualitative research approach was conducted. In the first step, a two-stage DEA model was used, and it was possible to identify, within a sample of the 60 biggest airports from the six Airport Council International (ACI) regions, the most efficient airports of the world: Beijing International Airport, Heathrow International Airport, Dubai International Airport, and Hartsfield-Jackson Atlanta International Airport. In the second step of the research, four airports were selected in order to search for the efficiency drivers. In this context, Heathrow International Airport was considered to be a benchmark and presented most of the identified efficiency drivers (27 of 31). Standing out were Just-in-Time (JIT) and Total Quality Management (TQM) principles and also Passenger boarding optimization, besides the presence of almost all of the most relevant efficiency drivers, according to airport managers.

SE-DEA-based Approach to Determining the Hub Growth Potential of Airports

Determining which airports can grow to hubs in a transportation system is a basis of hub-and-spoke network design. From the perspective of airport operation efficiency, this paper presented a SE-DEA (super-efficiency data envelopment analysis)-based approach to identify those airports that possess the hub growth potential. Firstly, the urban population, urban GDP, and the number of navigation points were considered as input variables. The throughput, the proportion of transit passengers, the proportion of international passengers, the normal rate of airport release and the proportion of base airline were treated as output variables. An empirical analysis with the data of several main American airports in 2018 was solved by DEA-SOLVER Pro 5.0 software. The results show us: (1) the top three airports are LAS (McCarran International Airport), ATL (Hartsfield-Jackson Atlanta International Airport) and MIA (Miami International Airport), (2) the results are consistent with the reality and this proposed method could lay theoretical basis on finding airports which have the potential to become hub ones.

Direct Economic Impact Analysis of the World’s Top Five Busiest Airports in 2018

The purpose of this study is to analyze the direct economic impacts of the world’s top five busiest airports in 2018 as they contribute to the economic well-being of the larger communities they serve. This study uses a descriptive case-study methodology since the direct economic impacts of the world’s top five busiest airports are going to be studied in a case-by-case with an intention of reporting the research findings that are not related to specific variables. Amongst the 17,678 commercial service airports in the world, Hartsfield-Jackson Atlanta International Airport (USA), Beijing Capital International Airport (Peoples Republic of China), Dubai International Airport (United Arab Emirates), Los Angeles International Airport (USA), and Tokyo Haneda International Airport (Japan) have been ranked respectively as the top five busiest airports in the world on the basis of passenger volume handled in 2018. The research findings indicate that these airports have tremendously benefited their respective communities in terms of employment generation, income generation, and total direct economic impacts. These airports have generated a total of $181.4 billion worth of direct economic benefits to their respective communities and regions.

Predictive Modelling: Flight Delays and Associated Factors, Hartsfield–Jackson Atlanta International Airport

Nowadays, a downside to traveling is the delays that are constantly being advertised to passengers resulting in a decrease in customer satisfaction and causing costs. Consequently, there is a need to anticipate and mitigate the existence of delays helping airlines and airports improving their performance or even take consumer-oriented measures that can undo or attenuate the effect that these delays have on their passengers. This study has as main objective to predict the occurrence of delays in arrivals at the international airport of Hartsfield-Jackson. A Knowledge Discovery Database (KDD) methodology was followed, and several Data Mining techniques were applied. Historical data of the flight and weather, information of the airplane and propagation of the delay were gathered to train the model. To overcome the problem of unbalanced datasets, we applied different sampling techniques. To predict delays in individual flights we used Decision Trees, Random Forest and Multilayer Perceptron. Finally, each model’s performance was evaluated and compared. The best model proved to be the Multilayer Perceptron with 85% of accuracy.

Increasing Walking in the Hartsfield-Jackson Atlanta International Airport: The Walk to Fly Study.

To test the effectiveness of a point-of-decision intervention to prompt walking, versus motorized transport, in a large metropolitan airport. We installed point-of-decision prompt signage at 4 locations in the airport transportation mall at Hartsfield-Jackson Atlanta International Airport (Atlanta, GA) at the connecting corridor between airport concourses. Six ceiling-mounted infrared sensors counted travelers entering and exiting the study location. We collected traveler counts from June 2013 to May 2016 when construction was present and absent (preintervention period: June 2013-September 2014; postintervention period: September 2014-May 2016). We used a model that incorporated weekly walking variation to estimate the intervention effect on walking. There was an 11.0% to 16.7% relative increase in walking in the absence of airport construction where 580 to 810 more travelers per day chose to walk. Through May 2016, travelers completed 390 000 additional walking trips. The Walk to Fly study demonstrated a significant and sustained increase in the number of airport travelers choosing to walk. Providing signage about options to walk in busy locations where reasonable walking options are available may improve population levels of physical activity and therefore improve public health.

FORECASTING FUTURE DEMAND IN TWO OF THE BUSIEST US AIRPORTS USING SIMPLIFIED MODELS

In airport planning process, there is always a crucial need for good and reliable air traffic demand estimate. It is equally important for airport authorities and airline carriers, for in the case of under estimated demand, there would be frequent congestions and delays. Conversely; overestimates would lead to unjustified expenditure which may lead to financial problems to all parties. The accurate and reliable models are not highly sophisticated in nature. In this paper, two econometric models were developed to forecast the passenger enplanements in two of the busiest airports in the United States of America. Many national and local socioeconomic variables were analyzed to come up with simple, yet accurate models. It was found that the total aggregate variables including per capita gross domestic production and population, have more influence on demand in the Hartsfield-Jackson Atlanta International Airport than the local variables, while the same factors, but in their regional scale, proofed to be more influencing in the case of John F Kennedy International Airport.

Airport Capacity Prediction with Explicit Consideration of Weather Forecast Uncertainty

This paper describes a stochastic analytical model for predicting airport capacity with a look-ahead horizon suitable for strategic traffic flow management. The model extends previous research on airport capacity estimation by explicitly integrating the impact of terminal weather and its uncertainty. Different types of weather forecast inputs are explored, including deterministic forecasts, deterministic forecasts with forecast error models, and ensemble forecasts, to produce distributions of predicted arrival and departure capacity for each runway configuration at an airport. The paper introduces a mathematical capacity prediction model and weather data sources supported by a proof-of-concept prototype implementation, including results of validation studies at Hartsfield–Jackson Atlanta International Airport. These results are compared with standard airport benchmark capacities and actual observed throughputs. Results show that our analytical airport capacity model accurately predicts maximum available airport capacity for Hartsfield–Jackson Atlanta International Airport for different weather conditions. The validation studies reveal a limited impact of forecast uncertainty representation on the accuracy of airport capacity predictions.

Estimates of non-traditional secondary organic aerosols from aircraft SVOC and IVOC emissions using CMAQ

Abstract. Utilizing an aircraft-specific parameterization based on smog chamber data in the Community Multiscale Air Quality (CMAQ) model with the volatility basis set (VBS), we estimated contributions of non-traditional secondary organic aerosols (NTSOA) for aircraft emissions during landing and takeoff (LTO) activities at the Hartsfield–Jackson Atlanta International Airport. NTSOA, formed from the oxidation of semi-volatile and intermediate volatility organic compounds (S/IVOCs), is a heretofore unaccounted component of fine particulate matter (PM2.5) in most air quality models. We expanded a prerelease version of CMAQ with VBS implemented for the Carbon Bond 2005 (CB05) chemical mechanism to use the Statewide Air Pollution Research Center 2007 (SAPRC-07) chemical mechanism and added species representing aircraft S/IVOCs and corresponding NTSOA oxidation products. Results indicated that the maximum monthly average NTSOA contributions occurred at the airport and ranged from 2.4 ng m−3 (34 % from idle and 66 % from non-idle aircraft activities) in January to 9.1 ng m−3 (33 and 67 %) in July. This represents 1.7 % (of 140 ng m−3) in January and 7.4 % in July (of 122 ng m−3) of aircraft-attributable PM2.5 compared to 41.0–42.0 % from elemental carbon and 42.8–58.0 % from inorganic aerosols. As a percentage of PM2.5, impacts were higher downwind of the airport, where NTSOA averaged 4.6–17.9 % of aircraft-attributable PM2.5 and, considering alternative aging schemes, was as high as 24.0 % – thus indicating the increased contribution of aircraft-attributable SOA as a component of PM2.5. However, NTSOA contributions were generally low compared to smog chamber results, particularly at idle, due to the considerably lower ambient organic aerosol concentrations in CMAQ compared to those in the smog chamber experiments.

PM emissions measurements of in-service commercial aircraft engines during the Delta-Atlanta Hartsfield Study

This paper describes the results of the physical characterization of aircraft engine PM emission measurements conducted during the Delta-Atlanta Hartsfield Study at the Hartsfield-Jackson Atlanta International Airport. Engine exit plane PM emissions were sampled from on-wing engines on several in-service commercial transport aircraft from the fleet of Delta Airlines. The size distributions were lognormal in nature with a single mode. The geometric mean diameter was found to increase with increasing engine thrust, ranging from 15 nm at idle to 40 nm at takeoff. PM number- and mass-based emission indices were observed to be higher at the idle conditions (4% and 7%), lowest at 15%–30% thrust, and then increase with increasing thrust. Emissions measurements were also conducted during an advected plume study where over 300 exhaust plumes generated by a broad mix of commercial transports were sampled 100–350 m downwind from aircraft operational runways during normal airport operations. The range of values measured at take-off for the different engine types in terms of PM number-based emission index was between 7 × 1015–9 × 1017 particles/kg fuel burned, and that for PM mass-based emission index was 0.1–0.6 g/kg fuel burned. PM characteristics of aircraft engine specific exhaust were found to evolve over time as the exhaust plume expands, dilutes with ambient air, and cools. The data from these measurements will enhance the emissions inventory development for a subset of engines operating in the commercial fleet and improve/validate current environmental impact predictive tools with real world aircraft engine specific PM emissions inputs.

Evaluation of Fuel Benefits Depending on Continuous Descent Approach Procedures

In this work arrival traffic following a continuous descent approach profile into the Hartsfield-Jackson Atlanta International Airport was simulated based on actual recorded traffic data. The fuel burn for the simulated traffic was estimated using the Base of Aircraft Data (BADA) Thrust Specific Fuel Consumption model and compared to a baseline simulation of the as-flown trajectories, where the continuous descent approach profile was not followed. Several sources of variability that impact the fuel savings were examined, including separation minima, types of maneuver to absorb the delays, and composition of aircraft weight category in the fleet mix. The results indicate that fuel savings depended on a number of factors, including aircraft weight, number of step-downs, and the type of air traffic control maneuver used to impart delay to the aircraft. These effects are estimated using simulation.

Flight delay performance at Hartsfield-Jackson Atlanta International Airport

Purpose: The main objective of this paper is to determine the annual cyclical flight delays at Hartsfield-Jackson Atlanta International Airport. Then using other data such as annual precipitation, passenger and aircraft traffic volumes and other factors, we attempted to correlate these factors with overall delays. These data could assist airport management in predicting periods of flight delay. Design/methodology/approach: Data were taken and analyzed from the data base “Research and Innovation Technology Administration” (RITA) for the years 2005-2011 for Hartsfield-Jackson Atlanta International Airport. The data included 2.8 million flights originating and departing from this airport. Data were also gathered from the National Oceanic and Atmospheric Administration (NOAA) showing precipitation. Additional data were gathered from the FAA regarding delay causes, number and types of delays and changes to the infrastructure of ATL airport Findings: There is a repeatable annual pattern of delays at ATL that can be modeled using delay data from the Bureau of Transportation Statistics. This pattern appears to be caused primarily by the frequency and amount of precipitation that falls at ATL and by the amount of flights that arrive and depart at ATL. Originality/value: This information could assist airport operations personnel, FAA air traffic controllers and airlines in anticipating and mitigating delays at specific times of the year.

Airport Capacity Enhancement and Flight Predictability

Justifications for enhancements to airport capacity are often framed in relation to flight delay reductions, but improvements to flight predictability also offer substantial benefits to the health of the aviation system. “Predictability” is defined in this paper as block time adherence and is measured as the difference between scheduled and actual block time. This research, using historical data, quantifies the impact on flight predictability of one airport's enhancement of infrastructure capacity. A case study using statistical methodologies, including cluster analysis of national airspace days and quantile regression of flights, identifies how deployment of a fifth runway at Hartsfield–Jackson Atlanta International Airport in Georgia affected the predictability of flight arrivals. The analysis identifies four scenarios—defined according to the level of national airspace strain and terminal airspace weather disruption—for which inclusion of the fifth runway in the runway configuration is associated with either improvement or degradation in predictability. If broad gains are to be made in improving predictability for the national airspace, then capacity enhancements may offer a limited contribution to what must be a multifaceted solution.