Results for “traffic optimization” — Page 25

NASA NTRS2015

NASA's Aeroacoustic Tools and Methods for Analysis of Aircraft Noise

Aircraft community noise is a significant concern due to continued growth in air traffic, increasingly stringent environmental goals, and operational limitations imposed by airport authorities. The ability to quantify aircraft noise at the source and ultimately at observers is required to develop low noise aircraft designs and flight procedures. Predicting noise at the source, accounting for scattering and propagation through the atmosphere to the observer, and assessing the perception and impact on a community requires physics-based aeroacoustics tools. Along with the analyses for aero-performance, weights and fuel burn, these tools can provide the acoustic component for aircraft MDAO (Multidisciplinary Design Analysis and Optimization). Over the last decade significant progress has been made in advancing the aeroacoustic tools such that acoustic analyses can now be performed during the design process. One major and enabling advance has been the development of the system noise framework known as Aircraft NOise Prediction Program2 (ANOPP2). ANOPP2 is NASA's aeroacoustic toolset and is designed to facilitate the combination of acoustic approaches of varying fidelity for the analysis of noise from conventional and unconventional aircraft. The toolset includes a framework that integrates noise prediction and propagation methods into a unified system for use within general aircraft analysis software. This includes acoustic analyses, signal processing and interfaces that allow for the assessment of perception of noise on a community. ANOPP2's capability to incorporate medium fidelity shielding predictions and wind tunnel experiments into a design environment is presented. An assessment of noise from a conventional and Hybrid Wing Body (HWB) aircraft using medium fidelity scattering methods combined with noise measurements from a model-scale HWB recently placed in NASA's 14x22 wind tunnel are presented. The results are in the form of community noise metrics and auralizations.

Rizzi, Stephen A.↗

NASA NTRS2012

NASA's ATM Technology Demonstration-1: Integrated Concept of Arrival Operations

This paper describes operations and procedures envisioned for NASA s Air Traffic Management (ATM) Technology Demonstration #1 (ATD-1). The ATD-1 Concept of Operations (ConOps) demonstration will integrate three NASA technologies to achieve high throughput, fuel-efficient arrival operations into busy terminal airspace. They are Traffic Management Advisor with Terminal Metering (TMA-TM) for precise time-based schedules to the runway and points within the terminal area, Controller-Managed Spacing (CMS) decision support tools for terminal controllers to better manage aircraft delay using speed control, and Flight deck Interval Management (FIM) avionics and flight crew procedures to conduct airborne spacing operations. The ATD-1 concept provides de-conflicted and efficient operations of multiple arrival streams of aircraft, passing through multiple merge points, from top-of-descent (TOD) to touchdown. It also enables aircraft to conduct Optimized Profile Descents (OPDs) from en route altitude to the runway, using primarily speed control to maintain separation and schedule. The ATD-1 project is currently addressing the challenges of integrating the three technologies, and implantation into an operational environment. Goals of the ATD-1 demonstration include increasing the throughput of high-density airports, reducing controller workload, increasing efficiency of arrival operations and the frequency of trajectory-based operations, and promoting aircraft ADS-B equipage.

Baxley, Brian T.↗

NASA NTRS2019

Prediction of Pushback Times and Ramp Taxi Times for Departures at Charlotte Airport

When optimizing the takeoff sequence and schedule for departures at busy airports, it is important to accurately predict the taxi times from gate to runway because those are used to calculate the earliest possible takeoff times. Several airports like Charlotte Douglas International Airport show relatively long taxi times inside the ramp area with large variations, with respect to the travel times in the airport movement area. Also, the pushback process times have not been accurately modeled so far mainly due to the lack of accurate data. The recent deployment of the integrated arrival, departure, and surface traffic management system at Charlotte airport by NASA enables more accurate flight data in the airport surface operations to be obtained. Taking advantage of this system, actual pushback times and ramp taxi times from historical flight data at this airport are analyzed. Based on the analysis, a simple, data-driven prediction model is introduced for estimating pushback times and ramp transit times of individual departure flights. To evaluate the performance of this prediction model, several machine learning techniques are also applied to the same dataset. The prediction results show that the data-driven prediction model is as good as the machine learning algorithms when comparing various prediction performance metrics.

Lee, Hanbong↗

NASA NTRS2019

Prediction of Pushback Times and Ramp Taxi Times for Departures at Charlotte Airport

When optimizing the takeoff sequence and schedule for departures at busy airports, it is important to accurately predict the taxi times from gate to runway because those are used to calculate the earliest possible takeoff times. Several airports like Charlotte Douglas International Airport show relatively long taxi times inside the ramp area with large variations, with respect to the travel times in the airport movement area. Also, the pushback process times have not been accurately modeled so far mainly due to the lack of accurate data. The recent deployment of the integrated arrival, departure, and surface traffic management system at Charlotte airport by NASA enables more accurate flight data in the airport surface operations to be obtained. Taking advantage of this system, actual pushback times and ramp taxi times from historical flight data at this airport are analyzed. Based on the analysis, a simple, data-driven prediction model is introduced for estimating pushback times and ramp transit times of individual departure flights. To evaluate the performance of this prediction model, several machine learning techniques are also applied to the same dataset. The prediction results show that the data-driven prediction model is as good as the machine learning algorithms when comparing various prediction performance metrics.

airport surface operations↗

NASA NTRS2004

Wireless Channel Characterization in the Airport Surface Environment

Given the anticipated increase in air traffic in the coming years, modernization of the National Airspace System (NAS) is a necessity. Part of this modernization effort will include updating current communication, navigation, and surveillance (CNS) systems to deal with the increased traffic as well as developing advanced CNS technologies for the systems. An example of such technology is the integrated CNS (ICNS) network being developed by the Advanced CNS Architecture and Systems Technology (ACAST) group for use in the airport surface environment. The ICNS network would be used to convey voice/data between users in a secure and reliable manner. The current surface system only supports voice and does so through an obsolete physical infrastructure. The old system is vulnerable to outages and costly to maintain. The proposed ICNS network will include a wireless radio link. To ensure optimal performance, a thorough and accurate characterization of the channel across which the link would operate is necessary. The channel is the path the signal takes from the transmitter to the receiver and is prone to various forms of interference. Channel characterization involves a combination of analysis, simulation, and measurement. My work this summer was divided into four tasks. The first task required compiling and reviewing reference material that dealt with the characterization and modeling of aeronautical channels. The second task involved developing a systematic approach that could be used to group airports into classes, e.g. small airfields, medium airports, large open airports, large cluttered airports, etc. The third task consisted of implementing computer simulations of existing channel models. The fourth task entailed measuring possible interference sources in the airport surface environment via a spectrum analyzer.

Neville, Joshua T.↗

NASA NTRS2013

Air Traffic Management Technology Demonstration-1 Concept of Operations (ATD-1 ConOps), Version 2.0

This document is an update to the operations and procedures envisioned for NASA s Air Traffic Management (ATM) Technology Demonstration #1 (ATD-1). The ATD-1 Concept of Operations (ConOps) integrates three NASA technologies to achieve high throughput, fuel-efficient arrival operations into busy terminal airspace. They are Traffic Management Advisor with Terminal Metering (TMA-TM) for precise time-based schedules to the runway and points within the terminal area, Controller-Managed Spacing (CMS) decision support tools for terminal controllers to better manage aircraft delay using speed control, and Flight deck Interval Management (FIM) avionics and flight crew procedures to conduct airborne spacing operations. The ATD-1 concept provides de-conflicted and efficient operations of multiple arrival streams of aircraft, passing through multiple merge points, from top-of-descent (TOD) to the Final Approach Fix. These arrival streams are Optimized Profile Descents (OPDs) from en route altitude to the runway, using primarily speed control to maintain separation and schedule. The ATD-1 project is currently addressing the challenges of integrating the three technologies, and their implantation into an operational environment. The ATD-1 goals include increasing the throughput of high-density airports, reducing controller workload, increasing efficiency of arrival operations and the frequency of trajectory-based operations, and promoting aircraft ADS-B equipage.

Baxley, Brian T.↗

NASA NTRSDate not supplied

Joint Communication Resource Allocation and Velocity Selection in Urban Air Mobility via Multi-agent Reinforcement Learning

With traffic congestion problems becoming more severe in urban areas, the National Aeronautics and Space Administration promotes the Urban Air Mobility (UAM) concept, which envisages a safe and efficient air transportation system. However, the increased communication demands in UAM can exacerbate the spectrum scarcity. Therefore, a new communication resource allocation solution is necessary. In this paper, we focus on uplink UAM communications, where multiple aerial vehicles (AV) perform cargo/passenger delivery tasks. With predefined flight paths, AVs make decisions on communication resource allocation and velocity selection to complete their missions under safety constraints. Accordingly, we formulate a joint optimization problem to minimize the weighted sum of the total travel time and communication outage time. We first model the optimization problem as a Markov game and propose a multi-agent reinforcement learning based solution. Simulation results corroborate the effectiveness of the proposed solution.

Ruixuan Han↗

DOE OSTI2025

Understanding and Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, and Implications

Ridesharing allows people to share a vehicle with others traveling in the same direction, which can reduce costs and traffic congestion. Pooled rideshare (PR) services, such as UberX Share and Lyft Shared, offer an economical and environmentally friendly alternative by matching passengers traveling similar routes. However, despite these benefits, PR adoption remains low due to concerns about safety, privacy, and convenience. This research explores the factors influencing PR adoption and provides recommendations to improve user acceptance. A nationwide survey of 5,385 participants across the U.S. was conducted to understand why people choose or avoid PR. The study identified five key factors influencing PR consideration: safety, service experience, privacy, traffic/environment, and time/cost. Additional research examined ways to optimize PR experiences by identifying four critical factors: comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. To measure the impact of these factors, a statistical model called the Pooled Rideshare Acceptance Model (PRAM) was developed, providing insights into how each element influences PR adoption. Further analysis using the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMA) revealed how demographic characteristics such as age, gender, income, and past rideshare experience shape PR perceptions. Some key findings from the multigroup analyses showed that younger users valued technological features and environmental benefits, while older users prioritized reliability and service transparency. Additionally, privacy concerns were more significant for female users, while convenience was critical for higher-income groups. These results emphasize that a 'onesize-fits-all' approach to PR service design is not effective, highlighting the need for tailored strategies to address different user segments. Further, workshops were conducted with researchers and students to translate the findings into real-world solutions. These workshops and 3 all the statistical analyses led to the development of 95 actionable recommendations. The recommendations focus on key areas such as safety, service reliability, user education, and accessibility, offering tangible improvements to PR services. The insights from this study provide valuable guidance for policymakers, transportation network companies (TNCs), and researchers aiming to make PR services safer, more accessible, and widely accepted. By addressing user concerns, PR can become a more viable transportation option, supporting sustainable urban mobility and reducing reliance on private vehicles. Additionally, these findings emphasize the importance of user-centric service design in encouraging broader PR adoption. Future research should explore evolving trends in PR preferences, technological advancements, and policy changes to ensure continued improvements. By implementing these recommendations, PR services can better align with user expectations, enhance trust in shared mobility, and contribute to a more efficient transportation ecosystem.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2017

Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project: Advanced Collision Avoidance System for UAS (ACAS Xu) Interoperability White Paper Presentation

The Phase 1 DAA Minimum Operational Performance Standards (MOPS) provided requirements for two classes of DAA equipment: equipment Class 1 contains the basic DAA equipment required to assist a pilot in remaining well clear, while equipment Class 2 integrates the Traffic Alert and Collision Avoidance (TCAS) II system. Thus, the Class 1 system provides RWC functionality only, while the Class 2 system is intended to provide both RWC and Collision Avoidance (CA) functionality, in compliance with the Minimum Aviation System Performance (MASPS) for the Interoperability of Airborne Collision Avoidance Systems. The FAAs TCAS Program Office is currently developing Airborne Collision Avoidance System X (ACAS X) to support the objectives of the Federal Aviation Administrations (FAA) Next Generation Air Transportation System Program (NextGen). ACAS X has a suite of variants with a common underlying design that are intended to be optimized for their intended airframes and operations. ACAS Xu being is designed for UAS and allows for new surveillance technologies and tailored logic for platforms with different performance characteristics. In addition to Collision Avoidance (CA) alerting and guidance, ACAS Xu is being tuned to provide RWC alerting and guidance in compliance with the SC 228 DAA MOPS. With a single logic performing both RWC and CA functions, ACAS Xu will provide industry with an integrated DAA solution that addresses many of the interoperability shortcomings of Phase I systems. While the MOPS for ACAS Xu will specify an integrated DAA system, it will need to show compliance with the RWC alerting thresholds and alerting requirements defined in the DAA Phase 2 MOPS. Further, some functional components of the ACAS Xu system such as the remote pilots displayed guidance might be mostly references to the corresponding requirements in the DAA MOPS. To provide a seamless, integrated, RWC-CA system to assist the pilot in remaining well clear and avoiding collisions, several issues need to be addressed within the Phase 2 SC-228 DAA efforts. Interoperability of the RWC and CA alerting and guidance, and ensuring pilot comprehension, compliance and performance, will be a primary research area.

detect and avoid↗

NASA NTRS2023

NASA’s Digital Information Platform to Accelerate the Transformation of the National Airspace System

In order to accelerate the digital transformation of airspace operations, a foundational framework and infrastructure for providing sustainable, data-driven, and cohesive decision-making digital services for both traditional and emergent air vehicles is being developed. The reference implementation of Digital Information Platform builds an ecosystem for the aviation community by providing access to a secure and trusted source of aviation data and services. Several key features and services have been implemented to enable secure data sharing, communication, and service registration on the Platform. The technical approach used to implement these features is presented here. NASA-developed integrated aviation data and machine learning based prediction services to optimize airspace operations are available on the Platform. These services are being evaluated in an operational environment by flight operators and the real-world benefits are being captured. The Platform fosters collaboration among industry and researchers to develop complex aviation services and the aim is to make it publicly accessible for consumption by the aviation community.

Digital Transformation↗

NASA NTRSDate not supplied

NASA’s Digital Information Platform to Accelerate the Transformation of the National Airspace System

In order to accelerate the digital transformation of airspace operations, a foundational framework and infrastructure for providing sustainable, data-driven, and cohesive decision-making digital services for both traditional and emergent air vehicles is being developed. The reference implementation of Digital Information Platform builds an ecosystem for the aviation community by providing access to a secure and trusted source of aviation data and services. Several key features and services have been implemented to enable secure data sharing, communication, and service registration on the Platform. The technical approach used to implement these features is presented here. NASA-developed integrated aviation data and machine learning based prediction services to optimize airspace operations are available on the Platform. These services are being evaluated in an operational environment by flight operators and the real-world benefits are being captured. The Platform fosters collaboration among industry and researchers to develop complex aviation services and the aim is to make it publicly accessible for consumption by the aviation community.

Digital Transformation↗

NASA NTRS1993

Interworking and integration of the Inmarsat Standard-M with Pan-European GSM system

The market demand on mobile telephone communications has been increasing since the introduction of the cellular mobile telephone systems about twelve years ago. In Europe, projections indicate a demand of about 17 million subscribers for a fully deployed Pan European system. The Pan-European GSM system is to harmonize the growth of the terrestrial mobile system. Studies conducted by the European Space Agency (ESA) indicate that even with 60-70 percent of the area being covered by the GSM, a significant traffic demand (voice and data) will still exist for areas not covered by the terrestrial systems. This demand could be satisfied by a land mobile satellite system. The satellite system is therefore seen in a complementary role rather than in competition with the terrestrial system in an integrated telecommunications network. One possible scenario may be that initially the satellite system provides services to the rural areas together with areas still not covered by the GSM system. This service area is then gradually diminished as the terrestrial system expands until an optimum point is reached where the systems co-exist optimally. The objective of this paper is to investigate the possibility of integration of a space based system, in this case Inmarsat Standard-M, with the GSM (Global System for Mobile communications). One very important advantage of incorporating GSM services in Standard-M is that it will be feasible to extend the GSM services economically worldwide, not only to land mobiles but also to aeronautical and maritime mobiles.

Tafazolli, R.↗

NASA NTRS2010

Configuring Airspace Sectors with Approximate Dynamic Programming

In response to changing traffic and staffing conditions, supervisors dynamically configure airspace sectors by assigning them to control positions. A finite horizon airspace sector configuration problem models this supervisor decision. The problem is to select an airspace configuration at each time step while considering a workload cost, a reconfiguration cost, and a constraint on the number of control positions at each time step. Three algorithms for this problem are proposed and evaluated: a myopic heuristic, an exact dynamic programming algorithm, and a rollouts approximate dynamic programming algorithm. On problem instances from current operations with only dozens of possible configurations, an exact dynamic programming solution gives the optimal cost value. The rollouts algorithm achieves costs within 2% of optimal for these instances, on average. For larger problem instances that are representative of future operations and have thousands of possible configurations, excessive computation time prohibits the use of exact dynamic programming. On such problem instances, the rollouts algorithm reduces the cost achieved by the heuristic by more than 15% on average with an acceptable computation time.

Bloem, Michael↗

NASA NTRS2014

Annualized TASAR Benefits for Virgin America Operations

The Traffic Aware Strategic Aircrew Request (TASAR) concept offers onboard automation for the purpose of advising the pilot of traffic compatible trajectory changes that would be beneficial to the flight. A fast-time simulation study was conducted to assess the benefits of TASAR to Virgin America. The simulation compares historical trajectories without TASAR to trajectories developed with TASAR and evaluated by controllers against their objectives. It was estimated that about 25,000 gallons of fuel and about 2,500 minutes could be saved annually per aircraft. These savings were applied fleet-wide to produce an estimated annual cost savings to Virgin America in excess of $5 million due to fuel, maintenance, and depreciation cost savings. Switching to a more wind-optimal trajectory was found to be the use case that generated the highest benefits out of the three TASAR use cases analyzed. Virgin America TASAR requests peaked at two to four requests per hour per sector in high-altitude Oakland and Salt Lake City center sectors east of San Francisco.

Source record↗

NASA NTRS2015

Annualized TASAR Benefit Estimate for Virgin America Operations

The Traffic Aware Strategic Aircrew Request (TASAR) concept offers onboard automation for the purpose of advising the pilot of traffic compatible trajectory changes that would be beneficial to the flight. A fast-time simulation study was conducted to assess the benefits of TASAR to Virgin America. The simulation compares historical trajectories without TASAR to trajectories developed with TASAR and evaluated by controllers against their objectives. It was estimated that about 25,000 gallons of fuel and about 2,500 minutes could be saved annually per aircraft. These savings were applied fleet-wide to produce an estimated annual cost savings to Virgin America in excess of $5 million due to fuel, maintenance, and depreciation cost savings. Switching to a more wind-optimal trajectory was found to be the use case that generated the highest benefits out of the three TASAR use cases analyzed. Virgin America TASAR requests peaked at two to four requests per hour per sector in high-altitude Oakland and Salt Lake City center sectors east of San Francisco.

Henderson, Jeffrey↗

NASA NTRS1998

Architectural Optimization of Digital Libraries

This work investigates performance and scaling issues relevant to large scale distributed digital libraries. Presently, performance and scaling studies focus on specific implementations of production or prototype digital libraries. Although useful information is gained to aid these designers and other researchers with insights to performance and scaling issues, the broader issues relevant to very large scale distributed libraries are not addressed. Specifically, no current studies look at the extreme or worst case possibilities in digital library implementations. A survey of digital library research issues is presented. Scaling and performance issues are mentioned frequently in the digital library literature but are generally not the focus of much of the current research. In this thesis a model for a Generic Distributed Digital Library (GDDL) and nine cases of typical user activities are defined. This model is used to facilitate some basic analysis of scaling issues. Specifically, the calculation of Internet traffic generated for different configurations of the study parameters and an estimate of the future bandwidth needed for a large scale distributed digital library implementation. This analysis demonstrates the potential impact a future distributed digital library implementation would have on the Internet traffic load and raises questions concerning the architecture decisions being made for future distributed digital library designs.

Biser, Aileen O.↗

NASA NTRS2014

Traffic Aware Strategic Aircrew Requests (TASAR)

The Traffic Aware Strategic Aircrew Request (TASAR) concept offers onboard automation for the purpose of advising the pilot of traffic compatible trajectory changes that would be beneficial to the flight. A fast-time simulation study was conducted to assess the benefits of TASAR to Alaska Airlines. The simulation compares historical trajectories without TASAR to trajectories developed with TASAR and evaluated by controllers against their objectives. It was estimated that between 8,000 and 12,000 gallons of fuel and 900 to 1,300 minutes could be saved annually per aircraft. These savings were applied fleet-wide to produce an estimated annual cost savings to Alaska Airlines in excess of $5 million due to fuel, maintenance, and depreciation cost savings. Switching to a more wind-optimal trajectory was found to be the use case that generated the highest benefits out of the three TASAR use cases analyzed. Alaska TASAR requests peaked at four to eight requests per hour in high-altitude Seattle center sectors south of Seattle-Tacoma airport..

Wing, David J.↗

NASA NTRS2015

Annualized TASAR Benefit Estimate for Alaska Airlines Operations

The Traffic Aware Strategic Aircrew Request (TASAR) concept offers onboard automation for the purpose of advising the pilot of traffic compatible trajectory changes that would be beneficial to the flight. A fast-time simulation study was conducted to assess the benefits of TASAR to Alaska Airlines. The simulation compares historical trajectories without TASAR to trajectories developed with TASAR and evaluated by controllers against their objectives. It was estimated that between 8,000 and 12,000 gallons of fuel and 900 to 1,300 minutes could be saved annually per aircraft. These savings were applied fleet-wide to produce an estimated annual cost savings to Alaska Airlines in excess of $5 million due to fuel, maintenance, and depreciation cost savings. Switching to a more wind-optimal trajectory was found to be the use case that generated the highest benefits out of the three TASAR use cases analyzed. Alaska TASAR requests peaked at four to eight requests per hour in high-altitude Seattle center sectors south of Seattle-Tacoma airport.

Henderson, Jeffrey↗