Worth International Airport Research Articles

Deep-Learning Framework for Terminal Airspace Trajectory Prediction and In-Time Prognostics

Terminal airspace around an airport is the biggest bottleneck for commercial operations in the National Airspace System. To prognosticate the safety status of the terminal airspace, effective prediction of the airspace evolution is necessary. While there are fixed procedural structures for managing operations at an airport, the confluence of a large number of aircraft and the complex interactions between the pilots and air traffic controllers make it challenging to predict its evolution. Modeling the high-dimensional spatiotemporal interactions in the airspace given different environmental and infrastructural constraints is necessary for effective predictions of future aircraft trajectories that characterize the airspace state at any given moment. A novel deep-learning architecture using Graph Neural Networks is proposed to predict trajectories of aircraft 10 min into the future and estimate prognostic metrics for the airspace. The uncertainty in the future is quantified by predicting distributions of future trajectories instead of point estimates. The framework’s viability for trajectory prediction and prognosis is demonstrated with terminal airspace data from Dallas Fort Worth International Airport.

Evaluating airports' Sustainable Development Goals by using multi-criteria decision making methodologies.

The recent growth of the aviation industry, which poses significant environmental challenges, has heightened the pressure on the sustainability of airports. Airport sustainability requires a holistic approach that encompasses economic, social, environmental, and operational aspects. In this regard, the United Nations' 17 Sustainable Development Goals (SDGs) Agenda provides a roadmap for the aviation industry. However, despite recognizing the importance of SDGs, aviation authorities and airports often fail to effectively integrate them into their activities and annual reports. This study aims to evaluate the significance of SDGs for airports and select the airport that prioritizes SDGs the most using Multi-Criteria Decision Making (MCDM) methodologies. This study introduces a novel approach that integrates Step-wise Weight Assessment Ratio Analysis (SWARA) and Weighted Aggregated Sum Product Assessment (WASPAS) methods, which are MCDM techniques, to enhance airport sustainability. The SWARA method is employed to evaluate and assign weights to the SDGs in the context of airports. SDG 8 holds the highest level of significance among the goals concerning airports, while SDG 14 falls outside the scope of airport sustainability aspects. Then, five international airports that have been designated as green airports by aviation authorities and assessment organizations are selected, and the optimal alternative is determined using the WASPAS method, considering the weights obtained through SWARA. Dallas/Fort Worth International Airport is the top choice due to its successful implementations and reports aligning with the SDGs.

An examination of sUAS operations in proximity to a major U.S. Airport

The use of small, Unmanned Aircraft Systems (sUAS) has proliferated in the past several years. While operators are supposed to avoid operations in the immediate vicinity of airports, there are sometimes violations. In extreme cases, these violations can result in the complete grounding of flights at major airports around the world. These disruptions can lead to thousands of flights canceled and hundreds of thousands of passengers displaced. A compounding problem is the lack of empirical data to understand the amount of, and types of, sUAS operations around major airports. The purpose of this study was to assess the characteristics of sUAS operations near the Dallas-Fort Worth International Airport (DFW) by objectively analyzing data collected over a 36-month timeframe. The findings provide empirical data on the types of sUAS operations around DFW, yearly variance in operations, levels of UAS facility map compliance, types and times of sUAS flown, and sUAS launching points. The data indicates a growing trend in operations between 2018 and 2020, with a possible plateau in 2021 and beyond. sUAS operations in the airport vicinity are high, with approximately 70 % operating in the 400-foot grid levels or at points farther away from critical airport operations. Flights appear to follow seasonality and daily operation patterns, and growth seems to be in smaller vehicles with weights less than 250 g. In addition to the results, the authors discuss practical implications for National Airspace System (NAS) safety and the implications of integrating new operations into communities, such as drone deliveries and Advanced Air Mobility operations.

How Dallas Fort Worth International Airport is using modular construction to reduce development costs and timelines

Construction is one of the biggest challenges facing airports. Not only can construction negatively affect the airport experience for millions of passengers, it also can severely hamper airport operations and revenue. Yet, until recently, nearly allairport construction was performed in a similar fashion with whole sections of airports being shut down for months — often years — to build and/or renovate space from scratch. Modular construction represents a radical departure from this conventional building process. By constructing whole sections of a building off-site or landside, modular construction can dramatically minimise disruptions to airport operations and drive costs down by opening labour markets to expanded pools of talent. This paper will examine how modular construction has evolved, how it is currently being used in the renovation of Dallas Fort Worth International Airport’s Pier A and C expansion and how it could revolutionise amenity design and concession leasing.

Prediction of aircraft estimated time of arrival using a supervised learning approach

We present a novel data-driven approach for prediction of the estimated time of arrival (ETA) of aircraft in the terminal area via the implementation of a Random Forest regression model. The model uses data fused from a number of sources (flight track, weather, flight plan information, etc.) and provides predictions for the remaining flight time for aircraft landing at Dallas/Fort Worth (DFW) International Airport. The predictions are made when the aircraft is at a distance of 200-miles from the airport. The results show that the model is able to predict estimated time of arrival to within ± 5 min for 90% of the flights in the test data with the mean absolute error being lower at 145 seconds. This paper covers the entire pipeline of data collection, preprocessing, setup and training of the ML model, and the results obtained for DFW.

Data-driven simulation-based planning for electric airport shuttle systems: A real-world case study

Many airports are adopting battery electric buses in their shuttle fleets due to concerns over air quality and regulations. This study proposes a simulation-based optimization modeling framework to help airport shuttle operators effectively deploy electric buses. We evaluated a planned airport electric shuttle system with an event-driven simulator. Empirical data collected from existing systems were used to drive the simulations. We then proposed a simulation-based optimization model to determine the battery capacity, charging power, and number of chargers so that predefined objective(s) (e.g., minimizing total capital cost, minimizing emissions) are optimized. Compared to existing studies, the primary contribution of the proposed method is that it can model the real-world stochastic nature of operations in an electric bus system with much higher fidelity. To demonstrate the proposed modeling framework, we study a real-world shuttle system at the Dallas-Fort Worth International Airport, and present extensive numerical studies. When considering partial fleet electrification, the model can provide a set of Pareto optimal solutions. When considering full fleet electrification, the optimal solution requires a 50-kWh battery capacity and four 210-kW chargers, resulting in a total capital cost of $26,744,000. The results demonstrate that the proposed modeling framework can effectively optimize the planning of electric airport shuttle systems with partial or full fleet electrification.

From vision to reality: Dallas Fort Worth Airport's Integrated Operations Center

In 2017, after several high-profile incidents at domestic airports, the Secretary of the Department of Homeland Security and Transportation Security Administration Administrator, emphasised the need for integrated facilities, staffed by teams from both internal and external stakeholders, to work collaboratively on day-to-day operations, incidents and events. Consideration for the complexity of the air travel landscape and the opportunities presented with technological advances, Dallas Fort Worth International Airport (DFW) executive leadership developed a strategic initiative to create such a capability. In April of 2021, DFW's Integrated Operations Center (IOC) opened its doors. What it took to get from concept to reality is the focus of this paper. Lessons were learned along the way related to several factors, such as managing an operational and cultural change, balancing the human element with the technological and pivoting from an operational model based on reacting to events and situations to a model that is more predictive in nature.

De-risking the future — A case study: DFW Airport driving innovation and reducing risk on the road to autonomy

The coming wave of autonomous vehicles (AVs) presents a conundrum. Under the weight of millions of annual passengers, non-aeronautical revenue streams that feature parking and rental cars as top-line items and the business operation needs of today, airports risk erosion of revenue streams and upended operations as AVs come forward. When you consider the pervasive opportunity autonomous technology brings to passenger transport, cargo and ground support equipment, aircraft taxi bots, lawn mowers and delivery robots, the risks are widespread. How might this emerging technology be planned for in an environment that requires years-long planning for infrastructure? How might a future be envisioned where cars park themselves — or cease to require parking because they dutifully return home empty to their garage after a customer drop-off? The questions continue and the answers are unclear. What is clear is that the opportunity to take advantage of these technologies is immense. The time to initiate planning is now, even though solutions are still shrouded in ambiguity. Dallas-Fort Worth International Airport's (DFW) mission, ‘Travel. Transformed’ is not an endpoint, but rather a constantly evolving vision of what is possible. Innovation is a highly visible component of DFW's strategic plan, and a foundational element of its approach to meeting key results. This paper highlights DFW's efforts around autonomous technologies and their multi-pronged approach to innovation — an approach that features a discipled process, cross-functional programme management and a steadfast commitment to people.

Evaluating NOx emissions and their effect on O3 production in Texas using TROPOMI NO2 and HCHO

Abstract. The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite is a valuable source of information to monitor the NOx emissions that adversely affect air quality. We conduct a series of experiments using a 4×4 km2 Comprehensive Air Quality Model with Extensions (CAMx) simulation during April–September 2019 in eastern Texas to evaluate the multiple challenges that arise from reconciling the NOx emissions in model simulations with TROPOMI. We find an increase in NO2 (+17 % in urban areas) when transitioning from the TROPOMI NO2 version 1.3 algorithm to the version 2.3.1 algorithm in eastern Texas, with the greatest difference (+25 %) in the city centers and smaller differences (+5 %) in less polluted areas. We find that lightning NOx emissions in the model simulation contribute up to 24 % of the column NO2 in the areas over the Gulf of Mexico and 8% in Texas urban areas. NOx emissions inventories, when using locally resolved inputs, agree with NOx emissions derived from TROPOMI NO2 version 2.3.1 to within 20 % in most circumstances, with a small NOx underestimate in Dallas–Fort Worth (−13 %) and Houston (−20 %). In the vicinity of large power plant plumes (e.g., Martin Lake and Limestone) we find larger disagreements, i.e., the satellite NO2 is consistently smaller by 40 %–60 % than the modeled NO2, which incorporates measured stack emissions. We find that TROPOMI is having difficulty distinguishing NO2 attributed to power plants from the background NO2 concentrations in Texas – an area with atmospheric conditions that cause short NO2 lifetimes. Second, the NOx/NO2 ratio in the model may be underestimated due to the 4 km grid cell size. To understand ozone formation regimes in the area, we combine NO2 column information with formaldehyde (HCHO) column information. We find modest low biases in the model relative to TROPOMI HCHO, with −9 % underestimate in eastern Texas and −21 % in areas of central Texas with lower biogenic volatile organic compound (VOC) emissions. Ozone formation regimes at the time of the early afternoon overpass are NOx limited almost everywhere in the domain, except along the Houston Ship Channel, near the Dallas/Fort Worth International airport, and in the presence of undiluted power plant plumes. There are likely NOx-saturated ozone formation conditions in the early morning hours that TROPOMI cannot observe and would be well-suited for analysis with NO2 and HCHO from the upcoming TEMPO (Tropospheric Emissions: Monitoring Pollution) mission. This study highlights that TROPOMI measurements offer a valuable means to validate emissions inventories and ozone formation regimes, with important limitations.

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.

A discrete-event simulation tool for airport deicing activities: Dallas-Fort Worth International Airport

Aircraft deicing/anti-icing fluids (ADFs) are applied to remove and prevent icing on aircraft during taxi and takeoff. The Dallas-Fort Worth (DFW) International Airport uses deicing pads for deicing activities that collect and contain the spent deicing fluids for proper treatment or disposal. Local waterways receive ADF as “drip and shear” during the aircraft taxi on the runway and then takeoff. The glycol-based ADF serves as a nutrient for bacteria that grow exponentially, deplete dissolved oxygen (DO) from receiving waterways, and subsequently kill aquatic life. In this paper, we present a data-driven discrete-event simulation modeling process developed in collaboration with DFW Airport to assess aircraft assignment strategies to deicing pad locations by monitoring impact on DO. Our process consists of the following phases: (1) Data Collection, (2) Probability Distribution Modeling, and (3) State Transition Modeling. Both Phases (2) and (3) utilized data mining approaches, including treed regression and variable selection via false discovery rate. Detailed implementation of these phases is described for the DFW Airport case study, and the DFW Airport deicing activities simulation tool framework is presented. The actual data and simulation results were compared in terms of the DO levels in airport receiving waterways to verify the model validity after implementing the proposed model for DFW. Thus, the proposed model can be implemented by airports to control and minimize the adverse environmental effects resulting from deicing activities by optimizing the aircraft assignment to the pad locations.

ASPIRES: Airport Shuttle Planning and Improved Routing Event-driven Simulation

Most of the existing traffic simulation packages require significant calibration work to be able to reflect reality. To evaluate special operations including emerging technologies, a microscopic simulation that tracks detailed interactions of all the elements of the traffic systems is usually needed. This type of simulation is usually computationally demanding. This work developed an Airport Shuttle Planning and Improved Routing Event-driven Simulation (ASPIRES) package to simulate and evaluate current, potential, and future airport shuttle operations. The simulation was driven by data and thus did not require much calibration effort. The discrete-event simulation nature of ASPIRES makes the simulation computationally efficient. Simulating 1 day of shuttle operations takes less than 2 s. The study site of this work is the Dallas/Fort Worth International Airport in the U.S. The shuttle service that connects the five terminals of the airport and the rental car center was studied. Travel times, dwell times, and passenger arrivals were simulated using empirical distributions derived mainly from real data to capture the stochastic nature of the rental car center shuttle bus operations. Data on bus miles traveled, bus energy consumption, passenger wait times, and passengers left behind at stops were collected to study the trade-off between energy use and passenger experience. Electric bus and on-demand bus operations were also included. The simulation outputs can show passengers statistics at terminals, shuttles statistics, and charging station statistics. ASPIRES cannot be used to model a generic traffic system but is well-suited for fleet systems.

International airports as agents of resilience

AbstractThe Dallas‐Fort Worth International Airport is used as an example of how traditional approaches of hardening isolated components of critical infrastructure against specific threats leaves critical assets exposed to significant, expensive, and unacceptable levels of cascading failure. Optimizing the entire airport supply chain requires the development of an interdisciplinary approach that incorporates the complexities of the airports' supply chain including numerous dependent, interdependent and independent relationships. Far greater return‐on‐investment will be yielded through resilience‐focused approaches that address the entire life cycle of disruptions including planning, absorption, recovery and adaptation.

A modeling framework for designing and evaluating curbside traffic management policies at Dallas-Fort Worth International Airport

Emerging mobility technologies are changing the transportation system landscape. This is especially evident at airports, such as the Dallas-Fort Worth International Airport (DFW). Without careful analysis, these changes could lead to inefficient and costly airport operations. This paper presents a modeling framework that integrates travel mode encoding, demand projection, and microsimulation to enable airports to develop, simulate, and evaluate curbside traffic managements policies and measure their impact. The framework is utilized to analyze several traffic scenarios and policies for DFW: a baseline scenario which represents DFW traffic pattern as observed in 2018 and projected to 2045, a transit network company (TNC) electrification policy, a TNC queuing policy, a policy that increased transit ridership, a bus-only policy which considers the use of only buses inside DFW, an autonomous vehicle (AV) policy which investigates the impact of autonomous vehicle (AV) adoption on airport operations, and an example COVID-19 scenario which models the impact of the COVID19 pandemic. The simulations’ results demonstrate that: increasing the DFW transit ridership postpones the need for airport curbside expansion the most; encouraging shared-mobility with the bus-only policy produces the most savings in curbside congestion delays; automation and electrification for all passenger vehicle trips to/from DFW generates the most saving in fuel consumption and emissions; and uncontrolled AV adoption incurs the highest increase in fuel consumption, delay, and emissions and could require immediate airport capacity extension. Without policy intervention or investment in additional infrastructure capacity, these results predict the current operations would face significant congestion on high demand days starting as early as 2028. While derived in close partnership with DFW, the methodology presented here can be generalized to any airport.

Joint Modeling of Access Mode and Parking Choice of Air Travelers Using Revealed Preference Data

Airport ground access mode choice is distinct from everyday mode choice decisions, necessitating context-specific choice model estimation. Understanding airport ground access mode choice decisions is not only important for developing infrastructure planning strategies, but also for assessing the impacts of emerging modes on airport revenues, particularly from parking. However, parking choice is an often-overlooked dimension in airport ground access choice modeling. This paper addresses this gap through the development of a joint model of airport access mode and parking option choice using a passenger survey conducted at Dallas-Fort Worth (DFW) International Airport in 2015. Compared with a traditional conditional logit model that does not consider parking options available at DFW airport, the joint model of mode and parking decisions was found to generate more realistic values of travel time and was shown to have better predictive performance, both of which are critical for obtaining better airport parking revenue estimates and identifying traveler cohorts who may respond more strongly to potential policies targeting curb congestion and parking demand.

A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement.Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.

Route optimization for energy efficient airport shuttle operations – A case study from Dallas Fort worth International Airport

The objective of this research is to reduce energy consumption from intra airport shuttle operations by optimizing routes and schedules, without compromising on passenger travel experience. To achieve this objective, we propose an optimization model that generates optimal airport shuttle routes for a given set of constraints and a discrete-event simulator that evaluates the optimal shuttle routes in a stochastic environment to understand the tradeoffs between the amount of time passengers wait for shuttles, and shuttle energy consumption. The proposed optimization model and stochastic simulation are tested using shuttle route data provided by the Dallas Fort Worth International Airport. Results indicate that optimized routes can lead to a 20% energy reduction in shuttle operations with a modest 2-min increase in average shuttle wait times. The optimization model and simulator presented here are designed to be generalizable and can be adapted to optimize shuttle operations at any major airport.

Impact of Off-Block Time Uncertainty on the Control of Airport Surface Operations

Congestion at major airports worldwide results in increased taxi times, fuel burn, and emissions. Regulating the pushback of aircraft from their gates, also known as departure metering, is a promising approach to mitigating surface congestion. Departure metering algorithms require models of airport surface traffic and knowledge of when a flight would be to be ready for pushback, which is called the earliest off-block time (EOBT). While EOBTs are known to be inaccurate due to several reasons, there has been little prior research on characterizing EOBT uncertainty and its impact on departure metering. We present a new class of queuing network models for the airport surface that are capable of capturing congestion at multiple locations. We demonstrate our modeling approach using operational data from three major U.S. airports: Newark Liberty International Airport, Dallas/Fort Worth International Airport, and Charlotte Douglas International Airport. We analyze the current levels of uncertainty in the EOBT information published by the airlines and conduct a parametric analysis of the reduction in departure metering benefits due to errors in the EOBT information. Our analysis indicates that the current levels of EOBT uncertainty lead to a 50% reduction in benefits at some airports when compared with an ideal case with no EOBT uncertainty. Two approaches to departure metering are considered: the National Aeronautics and Space Administration’s Airspace Technology Demonstration-2 logic and a new optimal control approach. We show that our queuing network models can help design and evaluate both approaches and that the optimal control approach is more effective in accommodating EOBT uncertainty while maintaining runway utilization.

Downtown To DFW

The new 27.2 mi long TEXRail commuter rail line facilitates travel between downtown Fort Worth, Texas, and the busy Dallas/Fort Worth International Airport. Challenges to be overcome during the project included working in and/or around existing rail stations and lines, myriad utilities, and airport facilities. Since beginning operations last year, the $1.034-billion project has substantially improved mobility in the Fort Worth metropolitan region.

Cultural Resources Survey of the DPS Outdoor Bomb and Gun Range Project, Dallas Fort Worth International Airport, Tarrant County, Texas

This report documents the substantive findings and management recommendations of a cultural resources survey conducted by Integrated Environmental Solutions, LLC (IES) for the DPS Outdoor Bomb and Gun Range project located within the Dallas Fort Worth International Airport (DFW) property in Tarrant County, Texas. The proposed project pertains to improvements of the current facility within approximately 71.8 acres located between Texas State Highway (SH) 360 and West Airfield Drive. Approval from the Federal Aviation Administration (FAA) will be required to modify the Airport Layout Plan (ALP) to reflect the permanent alterations on the DFW property. Since the ALP is considered a federal action, the project will require compliance with the National Environmental Policy Act (NEPA) and Section 106 of the National Historic Preservation Act (NHPA). Additionally, as the DFW is a political subdivision of the State of Texas, the project will be subjected to the provisions of the Antiquities Code of Texas (ACT). The goal of the survey was to locate, identify, and assess any cultural resources that could be adversely affected by the proposed development, and to evaluate such resources for their potential eligibility for listing as a State Antiquities Landmark (SAL) and eligibility for listing in the National Register of Historic Places (NRHP). The cultural resources inventory survey was conducted on 16 December 2019 and 08 January 2020 by Project Archeologists Anne Gibson and Thomas Chapman, and Staff Archeologist Josh McCormick. All work conformed to 13 Texas Administrative Code 26, which outlines the regulations for implementing the ACT, and was conducted under Texas Antiquities Permit No. 9161. During the survey, one previously recorded historic-age archeological site (41TR87) was revisited within the APE. Based on the lack of association with historically important individuals or events, absence of significant architectural features, the degree of prior disturbance, and lack of contextual integrity, site 41TR87 is recommended to remain not eligible for listing in the NRHP or designation as a SAL. Although multi-component site 41TR18 was partially located within the APE, the site was not evaluated during the survey. However, the results from a previous IES investigation in 2015 indicate the site should remain ineligible for NRHP listing. Project records will be permanently curated at the Center for Archeological Research (CAR) at The University of Texas at San Antonio (UTSA). It is the recommendation of IES that the DPS Outdoor Bomb and Gun Range Project be permitted to continue without the need for further cultural resources investigations. However, if any cultural resources, other than those discussed in this report, are encountered during construction, the operators should stop construction activities in the vicinity of the inadvertent discovery, and immediately contact the project cultural resources representative to initiate coordination with the Texas Historical Commission (THC) prior to resuming construction activities.