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NASA NTRS2004

National Airspace System Delay Estimation Using Weather Weighted Traffic Counts

Assessment of National Airspace System performance, which is usually measured in terms of delays resulting from the application of traffic flow management initiatives in response to weather conditions, volume, equipment outages and runway conditions, is needed both for guiding flow control decisions during the day of operations and for post operations analysis. Comparison of the actual delay, resulting from the traffic flow management initiatives, with the expected delay, based on traffic demand and other conditions, provides the assessment of the National Airspace System performance. This paper provides a method for estimating delay using the expected traffic demand and weather. In order to identify the cause of delays, 517 days of National Airspace System delay data reported by the Federal Aviation Administration s Operations Network were analyzed. This analysis shows that weather is the most important causal factor for delays followed by equipment and runway delays. Guided by these results, the concept of weather weighted traffic counts as a measure of system delay is described. Examples are given to show the variation of these counts as a function of time of the day. The various datasets, consisting of aircraft position data, enroute severe weather data, surface wind speed and visibility data, reported delay data and number of aircraft handled by the Centers data, and their sources are described. The procedure for selecting reference days on which traffic was minimally impacted by weather is described. Different traffic demand on each reference day of the week, determined by analysis of 42 days of traffic and delay data, was used as the expected traffic demand for each day of the week. Next, the method for computing the weather weighted traffic counts using the expected traffic demand, derived from reference days, and the expanded regions around severe weather cells is discussed. It is shown via a numerical example that this approach improves the dynamic range of the weather weighted traffic counts considerably. Time histories of these new weather weighted traffic counts are used for synthesizing two statistical features, six histogram features and six time domain features. In addition to these enroute weather features, two surface weather features of number of major airports in the United States with high mean winds and low mean visibility are also described. A least squares procedure for establishing a functional relation between the features, using combinations of these features, and system delays is explored using 36 days of data. Best correlations between the estimated delays using the functional relation and the actual delays provided by the Operations Network are obtained with two different combinations of features: 1) six time domain features of weather weighted traffic counts plus two surface weather features, and 2) six histogram features and mean of weather weighted traffic counts along with the two surface weather features. Correlation coefficient values of 0.73 and 0.83 were found in these two instances.

Chatterji, Gano B.↗

NASA NTRS2016

Benefit Opportunities for Integrated Surface and Airspace Departure Scheduling: A Study of Operations at Charlotte-Douglas International Airport

NASA is collaborating with the FAA and aviation industry to develop and demonstrate new capabilities that integrate arrival, departure, and surface air-traffic operations. The concept relies on trajectory-based departure scheduling and collaborative decision making to reduce delays and uncertainties in taxi and climb operations. The paper describes the concept and benefit mechanisms aimed at improving flight efficiency and predictability while maintaining or improving operational throughput. The potential impact of the technology is studied and discussed through a quantitative analysis of relevant shortfalls at the site identified for initial deployment and demonstration in 2017: Charlotte-Douglas International Airport. Results from trajectory analysis indicate substantial opportunity to reduce taxi delays for both departures and arrivals by metering departures at the gate in a manner that maximizes throughput while adhering to takeoff restrictions due mostly to airspace constraints. Substantial taxi-out delay reduction is shown for flights subject to departure restrictions stemming from traffic flow management initiatives. Opportunities to improve the predictability of taxi, takeoff, and climb operations are examined and their potential impact on airline scheduling decisions and air-traffic forecasting is discussed. In addition, the potential to improve throughput with departure scheduling that maximizes use of available runway and airspace capacity is analyzed.

air traffic management↗

NASA NTRSDate not supplied

NASA’s Secured Airspace for Urban Air Mobility (UAM)

The Urban Air Mobility (UAM) architecture is leveraged from the Unmanned Traffic Management (UTM) concept of operations. Within the UAM environment, UAM operators work collaboratively to manage aerial vehicles in the urban environment. Providers of Services for UAM (PSU), UAM operators, and Supplemental Data Service Providers (SDSP) provide services to support flight operations within that environment. As a recognized need, various views of UAM flight information are provided to the public and public safety entities. To accomplish this, among other goals, the Federal Aviation Administration (FAA) can coordinate flight information between the FAA controlled National Airspace System (NAS) and the UAM environments through the FAA-Industry Data Exchange Protocol (FIDXP). This concept of UAM proposes to develop short-range, point-to-point transportation systems in metropolitan areas using vertical take-off and landing (VTOL) or short take-off and landing (STOL) aircraft to overcome increasing surface congestion. To garner the support of UAM and to realize its potential, an assurance of cybersecurity is critical for public acceptance. Understanding the various components communicating with one-another cybersecurity, like in other industries, has come to the forefront highlighting the need to protect these networks and systems from cyberattacks. With the planned growth and reach of UAM systems, it’s clear that the associated data exchange and service interactions will be at risk due to numerous types of cybersecurity attacks. Consequently, as these threats evolve, the UAM cybersecurity capabilities must adapt to these changes as well. While learning is always the goal, the overall intent of this workshop is to make recommendations on the following: (1) how future UAM environments can be protected against cyber-attacks, and (2) what mechanisms should be put in place to detect attacks against UAM environments.

UAM↗

DOE OSTI2025

Templates for Risk Informed Assurance with Curvature Embeddings (TRACE)

We investigate recovery of geometric structure from networks embedded in manifolds with spatially varying curvature, extending the constant-curvature framework of Lubold et al. (2023). Our work supports cascade risk assessment in critical infrastructure through the Templates for Risk-informed Assurance with Curvature Embeddings (TRACE) framework. Simulations on a bi-modal Gaussian surface show that constant-curvature methods yield weighted averages shaped by clique patterns, while hierarchical clustering identifies distinct regimes. Localized estimation, however, reveals boundary contamination in transitional regions. To address heterogeneity, we develop distance metrics for graphs with edge and node features, proving their metric validity, and validate them via deterministic graph generation from canonical tilings. We further propose a diffusion-based anomaly detection approach that treats networks as glued manifolds, using curvature discontinuities to detect structural anomalies. Employing the carré-du-champ operator and scalar curvature, we achieve robust anomaly discrimination, demonstrated on the Singapore Water Treatment (SWaT) dataset with joint network-traffic and sensor features. Integration with TRACE reveals how curvature shapes cascade dynamics: positive curvature impedes, while negative curvature accelerates propagation. This geometric perspective provides interpretable risk metrics and visualization tools for critical infrastructure managers. While full validation remains ongoing, our contributions establish a rigorous foundation for geometric analysis of network resilience and cascade vulnerability.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

The Design and Evaluation of Zero Trust Architecture for Electric Vehicle Charging Infrastructure: EVs @ Scale Series on EV Charging Station Cybersecurity

Implementing a zero trust architecture can significantly bolster the security of electric vehicle (EV) charging infrastructure. EV charging infrastructure includes numerous networked interfaces, each of which can present potential vulnerabilities. When these vulnerabilities are exploited, they can compromise the entire system, leading to severe operational and security risks. Zero trust is a security model that operates on the principle of "never trust, always verify," which helps manage the attack surface and limit the scope of any potential compromises. Fundamentally, this model ensures that no entity, whether inside or outside the network, is trusted by default. The design principles of zero trust include continuous verification, strict deny-by-default access controls, and micro-segmentation. Continuous verification ensures that every request is thoroughly checked, regardless of its origin. Strict access controls enforce the principle of least privilege, allowing users and devices only the minimum necessary access to perform their functions. Micro-segmentation involves dividing the network into smaller, isolated segments to prevent lateral movement in case of a breach. In the context of EV charging infrastructure, zero trust can be implemented through various strategies. For example, multi-factor authentication (MFA) can be required for engineers to access the management interfaces and control systems of charging stations. Real-time monitoring and analysis of network traffic can help detect and respond to anomalies. Systems that do not need to communicate with each other can be micro-segmented to enhance security. All communications should adhere to predefined policies to be permitted. Additionally, encrypting communications can protect sensitive information exchanged between chargers and management systems. This paper presents a zero trust architecture specifically designed for EV charging infrastructure. Implementing zero trust not only mitigates risks but also builds a resilient infrastructure capable of withstanding and quickly recovering from cyber threats. The architecture addresses six defined security objectives. A comprehensive test plan is developed to assess the architecture against these objectives, and the results of the evaluation are reported. This approach is essential for maintaining the reliability and integrity of EV charging services in an increasingly interconnected and vulnerable digital landscape. This is the first in a planned series of papers exploring the implementation of zero trust in EV charging infrastructure. Each paper will delve into different aspects and applications of zero trust, highlighting how various work processes and requirements can lead to distinct architectural designs. These architectures will be tailored to address specific security challenges and operational needs within the EV charging ecosystem, ensuring a robust and adaptable security framework.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1995

Mode Transitions in Glass Cockpit Aircraft: Results of a Field Study

One consequence of increased levels of automation in complex control systems is the presence of modes. A mode is a particular configuration of a control system that defines how human command inputs are interpreted. In complex systems, modes also often determine a specific allocation of control authority between the human and automated systems. Even in simple static devices (e.g., electronic watches, word processors), the presence of modes has been found to cause problems in either-the acquisition or production of skilled performance. Many of these problems arise due to the fact that the selection of a mode causes device behavior to be mediated by hidden internal state information. For these simple systems, many of these interaction problems can be solved by the design of appropriate feedback to communicate internal state information to the human operator. In complex dynamic systems, however, the design issues associated with modes seem to trancend the problem of merely communicating internal state information via displayed feedback. In complex supervisory control systems (e.g., aircraft, spacecraft, military command and control), a key function of modes is the selection of a particular configuration of control authority between the human operator and automated control systems. One mode may result in full manual control, another may result in a mix of manual and automatic control, while a third may result in full automatic control over the entire system. The human operator selects an appropriate mode as a function of current goals, operating conditions, and operating procedures. Thus, the operator is put in a position of essentially trying to control two coupled dynamic systems: the target system itself, and also a highly complex suite of automation controlling the target system. From a historical perspective, it should probably not come as a surprise that very little information is available to guide the design of mode-oriented control systems. The topic of function allocation (i.e., the proper division of control authority among human and computer) has a long history in human-machine systems research. Although this research has produced some relevant guidelines, a design approach capable of defining appropriate allocations of control function between the human and automation is not yet available. As a result, the function allocation decision itself has been allocated to the operator, to be performed in real-time, in the operation of mode-oriented control systems. A variety of documented aircraft accidents and incidents suggest that the real-time selection and monitoring of control modes is a weak link in the effective operation of complex supervisory control systems. Research in human-machine systems and human-computer interaction has barely scraped the surface of the problem of understanding how operators manage this task.The purpose of this paper is to present the results of a field study which examined how operators manage mode selection in a complex supervisory control system. Data on mode engagements using the Boeing B757/767 auto-flight system were collected during approach and descent into four major airports in the East Coast of the United States. Protocols documenting mode selection, automatic mode changes, pilot actions, quantitative records of flight-path variables, and verbal reports during and after mode engagements were collected by an observer from the jumpseat. Observations were conducted on two typical trips between three airports. Each trip was be replicated 11 times, which yielded a total of 22 trips and 66 legs on which data were collected. All data collected concerned the same flight numbers, and therefore, the same time of day, same type of aircraft, and identical operational environments (e.g., ATC facilities, weather patterns, traffic flow etc.)

Degani, Asaf↗

NASA NTRS2013

Precision Departure Release Capability (PDRC): NASA to FAA Research Transition

After takeoff, aircraft must merge into en route (Center) airspace traffic flows which may be subject to constraints that create localized demand-capacity imbalances. When demand exceeds capacity, Traffic Management Coordinators (TMCs) and Frontline Managers (FLMs) often use tactical departure scheduling to manage the flow of departures into the constrained Center traffic flow. Tactical departure scheduling usually involves use of a Call for Release (CFR) procedure wherein the Tower must call the Center to coordinate a release time prior to allowing the flight to depart. In present-day operations release times are computed by the Center Traffic Management Advisor (TMA) decision support tool based upon manual estimates of aircraft ready time verbally communicated from the Tower to the Center. The TMA-computed release time is verbally communicated from the Center back to the Tower where it is relayed to the Local controller as a release window that is typically three minutes wide. The Local controller will manage the departure to meet the coordinated release time window. Manual ready time prediction and verbal release time coordination are labor intensive and prone to inaccuracy. Also, use of release time windows adds uncertainty to the tactical departure process. Analysis of more than one million flights from January 2011 indicates that a significant number of tactically scheduled aircraft missed their en route slot due to ready time prediction uncertainty. Uncertainty in ready time estimates may result in missed opportunities to merge into constrained en route flows and lead to lost throughput. Next Generation Air Transportation System plans call for development of Tower automation systems capable of computing surface trajectory-based ready time estimates. NASA has developed the Precision Departure Release Capability (PDRC) concept that improves tactical departure scheduling by automatically communicating surface trajectory-based ready time predictions and departure runway assignments to the Center scheduling tool. The PDRC concept also incorporates earlier NASA and FAA research into automation-assisted CFR coordination. The PDRC concept reduces uncertainty by automatically communicating coordinated release times with seconds-level precision enabling TMCs and FLMs to work with target times rather than windows. NASA has developed a PDRC prototype system that integrates the Center's TMA system with a research prototype Tower decision support tool. A two-phase field evaluation was conducted at NASA's North Texas Research Station in Dallas-Fort Worth. The field evaluation validated the PDRC concept and demonstrated reduced release time uncertainty while being used for tactical departure scheduling of more than 230 operational flights over 29 weeks of operations.

ATM↗

NASA NTRSDate not supplied

Lessons Learned: Using UTM paradigm for Urban Air Mobility Operations

Urban Air Mobility (UAM) aims to reduce congestion on the roads and highways by offering air taxi as an alternative to driving on surface roads. Integration of UAM operations in the National Airspace System (NAS) has been the focus of the research conducted at NASA Ames Research Center. A simulation was performed in collaboration with Uber Technologies Inc to investigate if NASA’s UTM architecture and its implementation as demonstrated in the 2019 UTM field tests were extensible for UAM operations, and if the data exchange between multiple operators as planned under UTM were adequate for UAM operations in the shared airspace. In order to explore these research questions, three Use Cases were defined to investigate different airspace management challenges. This paper will describe the lessons learned from exercising the uses cases and the airspace management services including scheduling and separation developed to facilitate initial UAM operations.

Urban Air Mobility↗

NASA NTRS2020

Lessons Learned: Using UTM Paradigm for Urban Air Mobility

Urban Air Mobility↗

NASA NTRSDate not supplied

Development of Candidate Airspace Procedures for Urban Air Mobility in the Dallas Area

As the concept of on-demand electric air taxis gains popularity and begins to take shape, many research efforts are underway to address the challenges of integrating this new class of passenger-carrying air vehicles into the already congested U.S. National Airspace System. Through collaborations between the National Aeronautics and Space Administration (NASA), the Federal Aviation Administration, and industry, this concept, commonly referred to as Urban Air Mobility (UAM), envisions a safe, reliable, and efficient mode of transportation traversing metropolitan and urban areas. Among the many challenges being tackled, airspace procedures and information requirements are critical areas of research that need to be addressed. In a joint effort between NASA and Joby Aviation, a human-in-the-loop study was conducted at the NASA Ames Research Center to evaluate initial and midterm operations with air traffic controllers and on-board UAM pilots in the Dallas area. This area was chosen due to its complex Class Bravo Airspace that extends to the surface over a relatively large area. To achieve the goals and objectives of the study, multiple preceding efforts were conducted to develop the candidate procedures and information requirements. This paper provides an overview of the operational concept used for the study, the process that was followed, and the findings from the two tabletop meetings and a shakedown activity. The combination of these led to a set of airspace procedures, letters of agreement, and information requirements that were evaluated in the human-inthe-loop study.

Urban Air Mobility↗

DOE OSTI2025

Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance

Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.

42 ENGINEERING↗

NASA NTRS2008

NASA Tech Briefs, September 2008

Topics covered include: Nanotip Carpets as Antireflection Surfaces; Nano-Engineered Catalysts for Direct Methanol Fuel Cells; Capillography of Mats of Nanofibers; Directed Growth of Carbon Nanotubes Across Gaps; High-Voltage, Asymmetric-Waveform Generator; Magic-T Junction Using Microstrip/Slotline Transitions; On-Wafer Measurement of a Silicon-Based CMOS VCO at 324 GHz; Group-III Nitride Field Emitters; HEMT Amplifiers and Equipment for their On-Wafer Testing; Thermal Spray Formation of Polymer Coatings; Improved Gas Filling and Sealing of an HC-PCF; Making More-Complex Molecules Using Superthermal Atom/Molecule Collisions; Nematic Cells for Digital Light Deflection; Improved Silica Aerogel Composite Materials; Microgravity, Mesh-Crawling Legged Robots; Advanced Active-Magnetic-Bearing Thrust- Measurement System; Thermally Actuated Hydraulic Pumps; A New, Highly Improved Two-Cycle Engine; Flexible Structural-Health-Monitoring Sheets; Alignment Pins for Assembling and Disassembling Structures; Purifying Nucleic Acids from Samples of Extremely Low Biomass; Adjustable-Viewing-Angle Endoscopic Tool for Skull Base and Brain Surgery; UV-Resistant Non-Spore-Forming Bacteria From Spacecraft-Assembly Facilities; Hard-X-Ray/Soft-Gamma-Ray Imaging Sensor Assembly for Astronomy; Simplified Modeling of Oxidation of Hydrocarbons; Near-Field Spectroscopy with Nanoparticles Deposited by AFM; Light Collimator and Monitor for a Spectroradiometer; Hyperspectral Fluorescence and Reflectance Imaging Instrument; Improving the Optical Quality Factor of the WGM Resonator; Ultra-Stable Beacon Source for Laboratory Testing of Optical Tracking; Transmissive Diffractive Optical Element Solar Concentrators; Delaying Trains of Short Light Pulses in WGM Resonators; Toward Better Modeling of Supercritical Turbulent Mixing; JPEG 2000 Encoding with Perceptual Distortion Control; Intelligent Integrated Health Management for a System of Systems; Delay Banking for Managing Air Traffic; and Spline-Based Smoothing of Airfoil Curvatures.

Source record↗

NASA NTRS2013

Effects of Airport Tower Controller Decision Support Tool on Controllers Head-Up Time

Despite that aircraft positions and movements can be easily monitored on the radar displays at major airports nowadays, it is still important for the air traffic control tower (ATCT) controllers to look outside the window as much as possible to assure safe operations of traffic management. The present paper investigates whether an introduction of the NASA's proposed Spot and Runway Departure Advisor (SARDA), a decision support tool for the ATCT controller, would increase or decrease the controllers' head-up time. SARDA provides the controller departure-release schedule advisories, i.e., when to release each departure aircraft in order to minimize individual aircraft's fuel consumption on taxiways and simultaneously maximize the overall runway throughput. The SARDA advisories were presented on electronic flight strips (EFS). To investigate effects on the head-up time, a human-in-the-loop simulation experiment with two retired ATCT controller participants was conducted in a high-fidelity ATCT cab simulator with 360-degree computer-generated out-the-window view. Each controller participant wore a wearable video camera on a side of their head with the camera facing forward. The video data were later used to calculate their line of sight at each moment and eventually identify their head-up times. Four sessions were run with the SARDA advisories, and four sessions were run without (baseline). Traffic-load levels were varied in each session. The same set of user interface - EFS and the radar displays - were used in both the advisory and baseline sessions to make them directly comparable. The paper reports the findings and discusses their implications.

airport surface operations↗

DOE OSTI2025

Giant Undulations Driven by Pitch‐Angle Scattering of Time Domain Structures Modulated by Plasmapause Surface Wave

Abstract Plasmapause surface waves (PSWs) near the plasmapause boundary are regarded to be the magnetospheric source of ionospheric auroral giant undulations (GUs) located at the equatorward boundary of diffuse aurora. However, the observational evidence of wave‐particle interaction connecting PSWs and GUs is absent. In this letter, we demonstrate GUs are driven by pitch‐angle scattering of time domain structures modulated by the PSWs, based on the conjugated ionospheric and magnetospheric observations. Specifically, ionospheric GUs are lighted by the pitch‐angle scattering of <1 keV thermal electron and ions and energetic ions with energy up to dozens of keV near the plasmapause. Further, the total fluxes during one PSW period and energy of scattered electron and ions determine the size and luminosity of GUs. Our research provides observational evidence that PSWs cause periodic electron precipitation via modulating the time domain structures rather than the previously predicted chorus or electron cyclotron harmonic waves.

Zhou, Yi‐Jia [Weihai Institute for Interdisciplina↗

NASA NTRS2018

Comparison of First-Come First-Served and Optimization Based Scheduling Algorithms for Integrated Departure and Arrival Management

Korea Aerospace Research Institute (KARI) and National Aeronautics and Space Administration (NASA) are investigating scheduling algorithms that will be a part of an integrated arrival and departure management system. Inha University, one of the Korean collaborators of KARI, developed an Extended First-Come First-Served (EFCFS) algorithm that is robust and efficient. However, since the EFCFS algorithm sequentially computes the schedule based on priority, the end results may not be optimal for system efficiency. The approach based on Mixed Integer Linear Programming (MILP) originally developed by NASA and modified by KARI is known to produce better schedules at the expense of computational cost. In this paper, the two different scheduling approaches are compared using common traffic scenarios and constraints at Incheon International Airport. Capabilities to apply weight class based wake turbulence runway separation minima and Miles-in-Trail (MIT) restrictions at selected meter fixes are added to the previously developed EFCFS scheduler. Based on historic data, 40 departures and 20 arrivals are chosen in a one-hour period and 100 scenarios were created by randomly assigning gate numbers, gate departure times, and runway landing times. With the current runway separation requirements, MILP resulted in about ten to twenty percent smaller average delays depending on the constraints. With artificially increased separation minima, the difference between MILP and EFCFS became more noticeable. However, the EFCFS was about ten times faster with smaller variations among different scenarios and constraints. The comparison suggests that the MILP-based algorithm has a small advantage at the current traffic level; however, has potential to be more effective in higher demand or severe weather situations. The EFCFS algorithm may be better suited for real-time applications or investigating larger scale scheduling problems.

air traffic optimization↗