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Nowadays, there is an increase in the demand for optimized services in urban environments. However, ground transportation in big urban centers has been facing challenges for many years (e.g., resilience and congestion) and new paradigms have been proposed, such as the Urban Air Mobility (UAM) concept. UAM aims to enhance the urban transportation system using manned and unmanned aerial vehicles (i.e., Electric Vertical Takeoff and Landing - eVTOL - vehicles). Although UAM offers many benefits (e.g., cost reduction and increase in transportation capacity), many challenges need to be faced to enable safe and efficient operations. Furthermore, trajectory planning is challenging in the National Airspace System (NAS) and UAM operations due to several factors. Finally, new initiatives concerning UAM trajectory planning can be accelerated with the support of an automatic what-if platform capable of evaluating trajectories feasibility and efficiency. This research aims to propose a simulation platform for enabling trajectory evaluation in UAM operations. This platform, named Trajectory-Based Urban Air Mobility Simulator (TUS), focuses on simulating trajectories in the urban aerial environment. TUS enables users to test new UAM algorithms (e.g., flow management strategies, real-time evaluation of maneuvers effectiveness, airspace configurations) and simulate both manned and unmanned vehicles. Furthermore, TUS operation relies on a set of inputs and evaluates the trajectories generated from efficiency and safety perspectives. This process is performed using a Discrete Event Simulation (DES) approach. The experiments performed showed that TUS can perform a realistic evaluation of UAM trajectories and can be effortlessly used to simulate hundreds of scenarios.

The identification of precursors to safety incidents in aviation data is a crucial task, yet extremely challenging. The main approach in practice leverages domain expertise to define expected toler...

The International Civil Aviation Organization identifies departure and arrival punctuality as on-time key performance indicators. However, these metrics assume a flight’s delay is a result of either the origin or destination airport, providing limited information on where delay should be mitigated in the U.S. National Airspace System (NAS). This study evaluates the relationship between delay propagation magnitude, delay causal factor, airport size, and charged facility (airport or Air Route Traffic Control Center), to examine if certain delays take longer to dissipate. First, using flights from July 2018, results show that most delay propagation chains originate at large-hub airports. However, these delays were the quickest to recover. Second, this study presents a regression model, predicting total propagated delay using fixed effects based on the weather region where the original delay occurred. Each additional flight affected by downstream delay adds 18.7 min on average to total arrival delay in a propagation chain. Additionally, if weather was the original causal factor, total propagated delay increased by 11.6 min compared with non-weather delays. Lastly, this study compares delay propagation in July 2018 and July 2020. Results show uneven impacts of the coronavirus disease 2019 (COVID-19) across the large-hub airports. Some of the investigated airports did not witness large improvements in average delay per delayed flight, warranting further research in the future. While delay and delay propagation have not been completely eradicated in the NAS during the COVID-19 pandemic, findings suggest that both have significantly declined on average.

Unmanned Aircraft System (UAS) growth in the past several years has been rising at a steady pace which has complicated the attempts to safely integrate them into the National Airspace System, as evidenced by an increasing number of UAS sighting reports being submitted to the Federal Aviation Administration. The analysis consisted of a mixed method approach using quantitative analysis of more than 9000 Federal Aviation Administration Unmanned Aircraft System Sighting reports from 2015 through 2019, as well as U.S. Census data, and weather data. The qualitative analysis focused on UAS regulation, and heatmap data of both population density and UAS sighting location density. The findings for the five states with the most and the least sighting reports show that major metropolitan areas, which have high population and population density, higher median household incomes, high percentage of college graduates, and are located in areas that have stable weather and negligible weather effects such as rain and high winds during the summer months, have both high and concentrated levels of UAS sightings.

En route inefficiency is measured in terms of extra distance flown by an aircraft, above a benchmark distance that relates to the theoretical shortest distance route (great circle route). In this paper, we have explored causal relations among en route inefficiency with multiple identified sources: convective weather, wind, miles-in-trail (MIT) restrictions, airspace flow programs (AFPs) and special activity airspace (SAA). We propose two mechanisms – strategic route choice and tactical reroute – to ascribe flight en route inefficiency to these factors. In our framework, we first propose an efficient trajectory clustering algorithm to identify nominal routes that reveal both air traffic flow patterns and air route structures in the national airspace system. Second, we develop a tree-based searching algorithm that matches different causal factors to the identified nominal routes in a 4-dimensional manner. Third, we employ a discrete choice modeling framework to quantitatively understand the flight route choice process, and establish a linear model to understand the tactical reroute process. Finally, we estimate the contributions of the identified causal factors to flight inefficiency through counterfactual analysis. Numerical experiments based on 8 representative airport pairs suggest strong negative impact of convection and headwind on route choice process, while MIT, AFP and SAA only demonstrate negative effects on some pairs. We have also shown that the contributions of convective weather, wind, traffic management initiatives, and SAA are up to 7.1%, 14.0%, 4.4%, 21.9%, respectively.

This paper aims to provide a resiliency management strategy of the air transportation network through network stability theory. To ensure the network to recover quickly from an upset condition, an ...

The authors would like to thank the following people for participating in the 2018 UAS Safety Symposium at Georgia Tech since that effort provided much of the driver for this research: Roy Burke, Julianna Burke, Anthony Mormino, Eric Richey, Chris Proudlove, Naira Hovakimyan, Jeong Jur, Greg Ourada, and Michael Miller. The authors are also thankful to Olivia Jagiella-Lodise and Philippe Monmousseau for their contributions in editing and formatting this paper.

Unmanned aircraft systems (UAS) collaborate with humans to operate in diverse, safety-critical applications. However, assurance technologies need to be integrated into the design process in order to guarantee safe behavior, thereby enabling UAS operations in the National Airspace System (NAS). In this paper, formal methods are integrated with learning-enabled systems representations. The generation and representation of knowledge are captured via monadic second-order logic rules in the cognitive architecture Soar. These rules are translated into timed automata, and a proof of correctness for the translation is provided so that safety and liveness properties can be checked in the formal verification environment Uppaal. This approach is agnostic to the learning mechanism used to generate the learned rules (e.g., chunking, etc.). An example of a fault-tolerant, learning-enabled UAS deciding which of four contingency procedures to execute under a lost link scenario while overflying an urban area is used to illustrate the approach.

Background: Considerable research is underway on how to integrate unmanned aircraft systems (UAS) safely and securely into the National Airspace System (NAS). While there is some ongoing research o...

Commercial space transportation is becoming more affordable and accessible. Consequently, we expect to see significant expansion of commercial space launch activities in the coming decade. As space vehicles travel through airspace during the launch and re-entry stages, they potentially disrupt the regular operations of traditional users. This paper estimates the potential economic and operational impacts of commercial space horizontal launch activities on airlines under various launch scenarios using predictive fast-time simulation modeling, focusing on Cecil Air and Space Port in Jacksonville (Florida) and the rules governing the national airspace system (NAS) in the United States. Our results indicate that the existing 4-hour airspace closure rule impacts a significant number of flights, resulting in flight time delays, additional flight distance and fuel burn, as well as other direct operating costs. Safely reducing the duration of airspace closures could serve as a simple solution to mitigate the impacts on airlines and other traditional NAS users. More importantly, treating our studied launch vehicle as an aircraft and opening its departure/arrival corridor to air traffic during a horizontal launch and return would potentially reduce the impacts on airlines significantly, depending on the location of the spaceport, planned flight paths and the trajectory of the launch.

In the past few years, there has been a growth in Unmanned Aircraft Systems (UAS) numbers in segregated airspace. However, although there is an interest in integrating large UAS in the National Airspace System (NAS), safety challenges regarding this insertion arise from the inclusion of new ways of reaching unsafe states. Although UAS may be used in different situations and brings several advantages to the airspace (e.g., efficiency), it may bring uncertainties due to the lack of familiarity of Air Traffic Controllers (ATCos) in these operations. Furthermore, the Terminal Maneuvering Area (TMA) is a critical control area generally established at the confluence of Air Traffic Service (ATS) routes in which the aircraft tend to be closer to each other. Besides, defining a final arrival segment for a set of aircraft in a complex environment is challenging. Thereupon, the main objective of this research is to propose a parallel swarm-based method for optimizing final aircraft arrival segment design (i.e., routes that connect the final sector to the Initial Approach Fix - IAF) considering the presence of aircraft of multiple Technology Maturity Levels (TML) - including the UAS. This is conducted from two perspectives: ATCo workload (which is related to safety) and sequencing duration (which is related to efficiency). Furthermore, different phases of UAS integration are considered using the Technology Maturity Levels (TMLs). Finally, the solutions consider airspace restrictions (e.g., minimum separation between aircraft and bad weather conditions). The experiments conducted show that this approach can build safe and efficient solutions, even in situations with many aircraft.

Unmanned aircraft systems (UAS) must comply with specific standards to operate in the National Airspace System (NAS). Among the requirements are the detect and avoid (DAA) capabilities, which include display, alerting, and guidance specifications. Previous studies have queried pilots for their subjective feedback of these display elements on earlier systems; the present study sought pilot evaluations with an initial iteration of the unmanned variant of a Next Generation Airborne Collision Avoidance System (ACAS XU). Sixteen participants piloted simulated aircraft with both standalone and integrated DAA displays. Their opinions were gathered using post-block and post-simulation questionnaires as well as guided debriefs. The data showed pilots had better understanding and comfort with the system when using an integrated display. Pilots also rated ACAS XU alerting and guidance as generally acceptable and effective. Implications for further development of ACAS XU and DAA displays are discussed.

The primary mission of the Federal Aviation Administration (FAA) is to maintain the safety of the National Airspace System (NAS). As part of this mission, the FAA is tasked with ensuring that future air traffic controllers are adequately trained to perform the high-risk job of directing air traffic. The FAA Academy curriculum for newly hired controllers involves 3-4 months of intensive lessons and performance assessments. It has been suggested that this training program is quite stressful, and successful trainees tend to be those who can better manage stress. To support ATC trainees, researchers at the Civil Aerospace Medical Institute (CAMI) have conducted operational research to develop and evaluate a stress management training to help trainees manage their stress during training at the FAA Academy.

Autonomous operation of unmanned aerial vehicles (UAVs) requires development of technologies that allow for safer flight control and response to various flight anomalies. Software for autonomous co...

Reliability of the air transportation system heavily depends on the performance of communication, navigation, and surveillance facilities in the National Airspace System (NAS). These facilities are prone to outages caused by convective weather, such as lightning. Current lightning safety standards and risk assessments focus solely on lightning occurrence and omit the effect of lightning intensity from hazard characterization. We propose methods that incorporate lightning intensity and occurrence parameters to better understand the impact of lightning strike on the NAS using the National Lightning Detection Network and Federal Aviation Administration NAS facilities and equipment outage databases. Spatial analysis and clustering reveal different exposure profiles for 436 U.S. airports. Kernel Density estimation and Hot Spot analysis show that regardless of lightning intensity, Southern state airports are the most exposed to lightning hazards. K-means clustering reveal five different lightning exposure profiles that mimic the spatial patterns produced by the Kernel Density estimation and Hot Spot analysis. A scoring system ranks all airports according to their exposure profile taking into consideration lightning occurrence and intensity. It is complemented with a rising trend exposure analysis, which identifies airports whose exposure could be underestimated under the current standards, identifying airports with fewer lightning occurrences but higher intensities. Finally, a comparison between the exposure patterns and lightning-induced outages provide insights into U.S. lightning impact patterns. Similar patterns between lightning exposure and outages indicate that the results of the proposed lightning hazard assessment provide useful information for prioritizing airport hardening investments at the national scale and reducing lightning risk.

The arrival sequencing and scheduling problem have been formulated in the urban air mobility (UAM) context for homogeneous and mixed fleets of electric vertical takeoff and landing (EVTOL) aircraft...

The main goal of this research is to evaluate the Air Traffic Controller (ATCo) workload considering the Unmanned Aircraft System (UAS) integration into the National Airspace System (NAS) through fast-time simulations, and including futuristic scenarios in which the ATCo is familiar with manned and unmanned aircraft, i.e., different ATCo mindsets are considered. As these professionals play an essential role in optimizing the airspace operation, maintaining their workload at an acceptable level is essential. However, the integration of new technologies, such as UAS, may present an impact on safety levels from the workload perspective. In this context, the Technology Maturity Level (TML), which is a systematic metric/measurement system that supports assessments of the familiarity of a particular aircraft with ATCos, is proposed. The experiments showed that the integration of UAS into the NAS should be conducted gradually.

When the Federal Aviation Administration (FAA) launched the System Wide Information Management (SWIM) initiative, the FAA had the goal of using the same portable, open infrastructure across all participating systems in the National Airspace System (NAS). Around 2008 for SWIM Segment 1, the FAA chose Iona Software's Free/Open Source Software (FOSS) based bundle, which was known and supported under the Fuse brand. The FAA obtained the licenses used by programs, including EnRoute Automation Modernization (ERAM), through Iona, which was later acquired by Progress and RedHat.

Metrics are the key tools to monitor the safety performance of complex systems like air traffic control. Developing risk metrics is a complex process, which involves prediction of risk and identification of different potential outcomes of undesired safety events. For instance, a metric that helps monitor the risk on the surface in an airport environment needs to detect and identify events such as runway excursions, runway incursions, and taxiway incidents and assign appropriate numerical indices proportional to the outcome of each event. An accident with a fatality, injury and aircraft damage will have a corresponding weight for each outcome and incident that involves no outcome can be assigned a severity weight based on its probability of becoming an accident. Models that support such metrics needs to be able to process data from diverse sources. In this paper, we discuss key aspects of two comprehensive metrics that the office of Safety in the Air Traffic Organization has deployed recently: 1) How to employ an automation to detect all relevant events from different data sources and 2) how to assign severity weights for different types of events (accidents and incidents).1. Detection of Relevant Events: one key element of supporting a comprehensive metrics is an automated detection of relevant events by categories. Unlike in aviation, the use of AI and ML have permeated in most industries. Aviation is a safety-critical domain in which there is very little tolerance for failures. The stringent requirements of aviation have contributed to the slow adoption of AI and ML in aviation, in general. However, many aviation sectors are increasingly adopting AI systems, ranging from automating simple, yet tedious and repetitive tasks to a more sophisticated application of a complex autonomous air traffic control system to de-conflict traffic. In this paper, we outline how machine learning models were employed to identify relevant accidents and incidents to support two metrics, a surface safety metric and an airborne safety metric.2. Severity Weighting Scheme: for a metric to be a comprehensive measure and indicate the overall performance of the system, it needs to account for various types of accidents and incidents (precursors) that occur in the system. This paper shows how a weighting scheme we developed to measure the outcome of accidents, such as injuries to people and damage to property, as well as probabilistically determine the severity of incidents with no outcome. The aggregation of all weights along with the frequency of occurrence in a given period represent the overall safety performance of the system.

Trust is a key issue in the development of autonomous systems. Humans and autonomous function or systems should be able to establish trust in each other’s actions. With advancing technology, innovations, and growth in Unmanned Aircraft System (UAS), ensuring trust in the use of automation and autonomy poses a major challenge. It is important that the automation in UAS operations is at an acceptable level of reliability, integrity, and redundancy. The bigger challenge is to approve the automation on the UAS and ground stations, which requires consideration of the complexity of operation, how automation is used in the operation, widely differing types of UAS, and the lack of standards to certify the automated elements. There is a need to examine relevant operational, technical, and procedural implications of automation in addition to analyzing system failures, contingent backup options in place for unforeseen circumstances, the role of the human, and training needs for the pilot or operator. It is important to account for these factors in defining standards and certification methods.With new concepts and technologies, and the tremendous emphasis on autonomy and automation, we assert a new method to prove the automation and transition plans for the required confidence level in automation. The method features a framework that accounts for new operations and interactions between all systems and stakeholders in the National Airspace System (NAS). Decision makers at all levels will find accelerated processing opportunities when using this framework to refine standards criteria, assess automation maturity, and focusing solution discussions with key stakeholder groups.The paper will describe the approach to classification of the automation role and human role leading to insights into the acceptable paths forward for automation inclusion. The strategic approach will enable the stakeholders to see the impact of UAS operating in widely varying environments, while ensuring operations remain safe and secure. The development of this comprehensive list with identified roles for automation and humans, as well as the interactions between humans and automation functions, will help with better planning, risk assessment, regulations, and policies as well as other integration activities.

Dynamic Airspace Configuration (DAC) has caught attention in tackling the increasing demand for flight operation in the National Airspace System (NAS). In recent years, sectorisation methods, such as the Voronoi diagram, dynamic programming, genetic algorithm, have been developed to find the optimal sector design. However, by restructuring the airspace, most design methods reduce the airline’s delay but increase the complexity of airspace configuration, which causes uncertainties to safe air traffic management. Furthermore, adaptive airspace reconfiguration schemes are also desired to accommodate the designs of different types of airspace. This paper presents a study exploring the concept of time-variant stability of airspace configuration transition in response to the assumed flight fluctuation. We exploited the airspace capacity in explaining the potential long-term effects caused by the graph-based airspace partitioning algorithm. According to the imbalanced distribution of air traffic, the aircraft counts of each divided sector are computed through time to estimate the workload of air traffic controllers (ATC).The number of flights in the sub-sector is computed through two months’ time length. Airspace designs from the Voronoi diagram are presented and evaluated by the airspace capacity of the ZJX ARTCC sector. Two configuration scenarios are chosen to present the algorithms’ evolving performance. The simulation results show that the existing graph partitioning method is not adapted to imbalanced traffic patterns, which does not show the potential of the reconfiguration algorithm’s robustness in response to the fluctuation of air traffic. As part of the necessary analysis we developed, we concluded the airspace sectorization for imbalanced traffic patterns and complex airspace characteristics, which provides the transferable features of airspace design between different ARTCC sectors.

Quantitative analysis is required as part of managing the automation systems which provide Air Traffic Management (ATM) services for the National Airspace System (NAS). The Federal Aviation Administration (FAA) invests significant levels of funding for such analyses for both post-hoc evaluation of tools/techniques, as well as prospective evaluation of proposed new capabilities. Statistical analysis is one quantitative analysis technique to assess the impact of a newly implemented automation capability. The general approach is to compare air traffic operations performance metrics (e.g., flight time/distance) before and after the activation of a new capability.

The last few years have seen an increased interest in deep learning (DL) due to its success in applications such as computer vision, natural language processing (NLP), and self-driving cars. Inspired by this success, this paper applied DL to predict flight demand and delays, which have been a concern for airlines and the other stakeholders in the National Airspace System (NAS). Demand and delay prediction can be formulated as a supervised learning problem, where, given an understanding of past historical demand and delays, a deep learning network can examine sequences of historic data to predict current and future sequences. With that in mind, we applied a well-known DL method, sequence to sequence (seq2seq), to solve the problem. Our results show that the seq2seq method can reduce demand prediction mean squared error (MSE) by 50%, compared to two classical baseline algorithms.

The development of future technologies for the National Airspace System (NAS) will be reliant on a new communications infrastructure capable of managing the limited available spectrum for communications among aircraft and ground systems. Emerging approaches to autonomous allocation of aviation spectrum mostly rely on machine learning techniques, where 4D (longitude, latitude, altitude, time) trajectory prediction is an important data input to enable real-time resource allocation. This study explores and evaluates effective data sources and deep recurrent neural network techniques when determining flight trajectories. Specifically, data are collected and evaluated in a 14-day period. Sources of data include NASA Sherlock Data Warehouse, MIT Lincoln Labs Corridor Integrated Weather Service (CIWS), and assorted NOAA weather datasets. Deep learning models for 4D predictions all utilize a hybrid-recurrent technique. A baseline model is considered via the convolutional-recurrent design from the existing literature. The modified design considers Gated Recurrent Units (GRU), Independently Recurrent Neural Networks (IndRNN), and stand-alone self-attention layers. Results indicate the effectiveness of Long-Short Term Memory (LSTM) and GRU cells for state-of-the-art data processing (interpolation). Attention mechanisms provide notable performance improvements to convolutional layers and may extend dimensional capabilities of a learning model. Finally, NOAA measurements provide only a supplemental value by improving predictions along a flight altitude; however, tailored products such as CIWS Echo Top remain the most holistically accurate.

The role of the en route air traffic control specialist (ATCS) is vital to maintaining safety and efficiency within the National Airspace System (NAS). ATCSs must vigilantly scan the airspace under their control and adjacent airspaces using an En Route Automation Modernization (ERAM) radar display. The intent of this research is to provide an understanding of the expert controller visual search and aircraft conflict mitigation strategies that could be used as scaffolding methods during ATCS training. Interviews and experiments were conducted to elicit visual scanning and conflict mitigation strategies from the retired controllers who were employed as air traffic control instructors. The interview results were characterized and classified using various heuristics. In particular, representative visual scanpaths were identified, which accord with the interview results of the visual search strategies. The highlights of our findings include: (1) participants used systematic search patterns, such as circular, spiral, linear or quadrant-based, to extract operation-relevant information, (2) participants applied an information hierarchy when aircraft information was cognitively processed (altitude -&gt, direction -&gt, speed), (3) altitude or direction changes were generally preferred over speed changes when imminent potential conflicts were mitigated. Potential applications exist in the implementation of the findings into the training curriculum of candidates.

Emergency landing is a critical safety feature for the expected increase of autonomous vehicles in the airspace. This paper presents Safe2Ditch, an autonomous crash management system for landing sm...

As air traffic management becomes increasingly automated, technologies for detecting and resolving conflicts are critical to maintain the safety of an airspace. There exists a need to understand ho...

By modeling compatibilities, benefits, and penalties of various technology packages at the subsystem level in a vehicle design tool, bottom-up assessments of projected technological benefits can be...

Adverse weather impacts the safety and efficiency of aviation. Convective storms, turbulence, and icing are aviation weather hazards that can lead to unpleasant rides and, in the worst case scenari...

Weather creates numerous operational and safety hazards within the National Airspace System (NAS). In 2014, extreme weather events attributed 4.3% to the total number of delay minutes recorded by the Bureau of Transportation Statistics. When factoring weather’s impact on the NAS delays and aircraft arriving late delays, weather was responsible for 32.6% of the total number of delay minutes recorded. Hourly surface meteorological aviation routine weather reports (METARs) at major airports can be used to provide valuable insight into the likely causes of weather delays at individual airports. When combined with the Federal Aviation Administration’s (FAA’s) Operations Network (OPSNET) delay data, METARs can be used to identify the major causes of delays and to create delay climatologies for a specific airport. Also, patterns for delays and cancellations for the study period of 2003–15 can be identified for the individual airports included in this study. These patterns can be useful for operators and airport planners to optimize performance in the future.

To achieve a level of safety equivalent to manned aircraft in the national airspace system, the pilot-in-command of small unmanned aircraft systems should be able to maintain situational awareness ...