Results for “traffic simulation” — Page 80

NASA NTRS2011

Use of Data Comm by Flight Crew to Conduct Interval Management Operations to Parallel Dependent Runways

The Interval Management (IM) concept is being developed as a method to maintain or increase high traffic density airport arrival throughput while allowing aircraft to conduct near idle thrust descents. The Interval Management with Spacing to Parallel Dependent Runways (IMSPiDR1) experiment at NASA Langley Research Center used 24 commercial pilots to examine IM procedures to conduct parallel dependent runway arrival operations while maintaining safe but efficient intervals behind the preceding aircraft. The use of IM procedures during these operations requires a lengthy and complex clearance from Air Traffic Control (ATC) to the participating aircraft, thereby making the use of Controller Pilot Data Link Communications (CPDLC) highly desirable as the communication method. The use of CPDLC reduces the need for voice transmissions between controllers and flight crew, and enables automated transfer of IM clearance elements into flight management systems or other aircraft avionics. The result is reduced crew workload and an increase in the efficiency of crew procedures. This paper focuses on the subset of data collected related to the use of CPDLC for IM operations into a busy airport. Overall, the experiment and results were very successful, with the mean time under 43 seconds for the flight crew to load the clearance into the IM spacing tool, review the calculated speed, and respond to ATC. An overall mean rating of Moderately Agree was given when the crews were asked if the use of CPDLC was operationally acceptable as simulated in this experiment. Approximately half of the flight crew reported the use of CPDLC below 10,000 for IM operations was unacceptable, with 83% reporting below 5000 was unacceptable. Also described are proposed modifications to the IM operations that may reduce CPDLC Respond time to less than 30 seconds and should significantly reduce the complexity of crew procedures, as well as follow-on research issues for operational use of CPDLC during IM operations.

Baxley, Brian T.↗

NASA NTRS2020

Urban Air Mobility System Testbed Using CAVE Virtual Reality Environment

Urban Air Mobility (UAM) refers to a system of air passenger and small cargo transportation within an urban area. The UAM framework also includes other urban Unmanned Aerial Systems (UAS) services that will be supported by a mix of onboard, ground, piloted, and autonomous operations. Over the past few years UAM research has gained wide interest from companies and federal agencies as an on-demand innovative transportation option that can help reduce traffic congestion and pollution as well as increase mobility in metropolitan areas. The concepts of UAM/UAS operation in the National Airspace System (NAS) remains an active area of research to ensure safe and efficient operations. With new developments in smart vehicle design and infrastructure for air traffic management, there is a need for methods to integrate and test various components of the UAM framework. In this work, we report on the development of a virtual reality (VR) testbed using the Cave Automatic Virtual Environment (CAVE) technology for human-automation teaming and airspace operation research of UAM. Using a four-wall projection system with motion capture, the CAVE provides an immersive virtual environment with real-time full body tracking capability. We created a virtual environment consisting of San Francisco city and a vertical take-off-and-landing passenger aircraft that can fly between a downtown location and the San Francisco International Airport. The aircraft can be operated autonomously or manually by a single pilot who maneuvers the aircraft using a flight control joystick. The interior of the aircraft includes a virtual cockpit display with vehicle heading, location, and speed information. The system can record simulation events and flight data for post-processing. The system parameters are customizable for different flight scenarios; hence, the CAVE VR testbed provides a flexible method for development and evaluation of UAM framework.

Marayong, Panadda↗

NASA NTRSDate not supplied

Fault Detection and Performance Monitoring of Propellers in Electric UAV

Unmanned aerial vehicles (UAVs) are used in various industries such as agriculture and logistics, to name but a few, where their applications are beyond basic mapping, surveillance, and photography. In near future, UAVs are expected to be used in package delivery service and larger electric vertical takeoff and landing vehicles will be employed for urban air mobility applications (air taxi). Thus, several electric propulsion systems will enter the low-altitude airspace with frequent take offs and landings. To achieve state-of-the-art safety standards under such high traffic density, UAVs will require in-time fault detection and performance monitoring of critical powertrain components. This work focuses on propeller blade performance and damage detection in electric UAVs. Propellers are the fastest moving component in an UAV; even a minor defect in the propeller blades could cause performance deterioration, with consequent challenges in flying through the planned trajectory or adhere to the safety requirements of the operation. Monitoring and updating aerodynamic efficiency of each rotor would therefore enable the detection of off-nominal propeller conditions thus magnifying the state-awareness of powertrain monitoring systems based on the acquired electrical signals. We use an extended Kalman filter-based parameter estimation algorithm that incorporates time history responses from UAV powertrain in conjunction with a full powertrain system model. Propeller fault detection is achieved by incorporating the aerodynamic parameters of propeller into the powertrain model. The proposed technique is successfully validated with numerical simulations.

Unmanned aerial vehicles↗

NASA NTRSDate not supplied

Helicopter Pilot Assessments of the Airborne Collision Avoidance System XR With Automated Maneuvering

The Airborne Collision Avoidance System X (ACAS X) is a next-generation collision avoidance tool developed to support different aircraft types and operations. The rotorcraft variant – referred to as ACAS XR – is designed to accommodate existing helicopter platforms as well as in-development, electric vertical takeoff and landing concepts, which are critical to the emerging concept of operations referred to as Advanced Air Mobility. The fundamental role of ACAS XR is to provide Detect and Avoid (DAA) and/or Collision Avoidance (CA) protection against airborne traffic. DAA alerting and guidance in the context of ACAS XR is caution-level and “suggestive,” and is to be used by the pilot if, and when, they decide to maneuver against an identified threat to DAA “well clear.” The CA alerting, by contrast, is warning-level and “directive,” with the pilot required to comply with the associated guidance to prevent a predicted Near Midair Collision (NMAC). The CA alerts generated by ACAS XR are referred to as Resolution Advisories (RAs) consistent with previous CA systems. Unlike earlier CA systems, ACAS XR issues RAs in the horizontal and vertical dimensions as well as multi-axis RAs (referred to as “Blended” RAs). According to the Minimal Operational Performance Standards of DAA systems for Unmanned Aircraft Systems, maneuvers to comply with RAs may be automated, whereas maneuvers based on DAA alerting assume a manual response. The current study was a human-in-the-loop simulation that presented rotorcraft pilots with ACAS XR alerts and guidance in a fixed-base eVTOL simulator with varying levels of automation. Objective results showed that pilots complied with all RAs within the expected 5-second time window and responded to DAA alerts quicker than in earlier studies with ACAS XU. Pilots often made larger horizontal deviations during Manual RAs, but often favored vertical and blended maneuvers. No NMACs occurred, and losses of well clear were mainly attributed to the obligation of the pilots and system to maneuver only after the CA phase of the encounter had begun. Other losses were due to pilots’ noncompliance or disregard for ACAS XR’s alerting and guidance. Noncompliance with RAs most frequently occurred when pilots determined they were too close to the terrain to continue to follow Descend RAs, performing vertical maneuvers instead of following Horizontal RAs, or rejecting Horizontal RA updates because they felt that enough maneuvering had been performed. Subjectively, pilots found the DAA and RA alerting and guidance intuitive and useful for VFR helicopter operations. Slightly more pilots preferred the Automated RA condition to the Manual RA condition. Lastly, they also felt that ACAS led to occasional unsafe Descend RAs. Caveats and future implications are discussed.

eVTOL↗

NASA NTRSDate not supplied

Helicopter Pilot Assessments of the Airborne Collision Avoidance System XR With Automated Maneuvering

The Airborne Collision Avoidance System X (ACAS X) is a next-generation collision avoidance tool developed to support different aircraft types and operations. The rotorcraft variant – referred to as ACAS XR – is designed to accommodate existing helicopter platforms as well as in-development, electric vertical takeoff and landing concepts, which are critical to the emerging concept of operations referred to as Advanced Air Mobility. The fundamental role of ACAS XR is to provide Detect and Avoid (DAA) and/or Collision Avoidance (CA) protection against airborne traffic. DAA alerting and guidance in the context of ACAS XR is caution-level and “suggestive,” and is to be used by the pilot if, and when, they decide to maneuver against an identified threat to DAA “well clear.” The CA alerting, by contrast, is warning-level and “directive,” with the pilot required to comply with the associated guidance to prevent a predicted Near Midair Collision (NMAC). The CA alerts generated by ACAS XR are referred to as Resolution Advisories (RAs) consistent with previous CA systems. Unlike earlier CA systems, ACAS XR issues RAs in the horizontal and vertical dimensions as well as multi-axis RAs (referred to as “Blended” RAs). According to the Minimal Operational Performance Standards of DAA systems for Unmanned Aircraft Systems, maneuvers to comply with RAs may be automated, whereas maneuvers based on DAA alerting assume a manual response. The current study was a human-in-the-loop simulation that presented rotorcraft pilots with ACAS XR alerts and guidance in a fixed-base eVTOL simulator with varying levels of automation. Objective results showed that pilots complied with all RAs within the expected 5-second time window and responded to DAA alerts quicker than in earlier studies with ACAS XU. Pilots often made larger horizontal deviations during Manual RAs, but often favored vertical and blended maneuvers. No NMACs occurred, and losses of well clear were mainly attributed to the obligation of the pilots and system to maneuver only after the CA phase of the encounter had begun. Other losses were due to pilots’ noncompliance or disregard for ACAS XR’s alerting and guidance. Noncompliance with RAs most frequently occurred when pilots determined they were too close to the terrain to continue to follow Descend RAs, performing vertical maneuvers instead of following Horizontal RAs, or rejecting Horizontal RA updates because they felt that enough maneuvering had been performed. Subjectively, pilots found the DAA and RA alerting and guidance intuitive and useful for VFR helicopter operations. Slightly more pilots preferred the Automated RA condition to the Manual RA condition. Lastly, they also felt that ACAS led to occasional unsafe Descend RAs. Caveats and future implications are discussed.

eVTOL↗

NASA NTRSDate not supplied

Advanced Air Mobility Operations & Automation Part II Technical Lecture

The Airborne Collision Avoidance System X (ACAS X) is a next-generation collision avoidance tool developed to support different aircraft types and operations. The rotorcraft variant – referred to as ACAS XR – is designed to accommodate existing helicopter platforms as well as in-development, electric vertical takeoff and landing concepts, which are critical to the emerging concept of operations referred to as Advanced Air Mobility. The fundamental role of ACAS XR is to provide Detect and Avoid (DAA) and/or Collision Avoidance (CA) protection against airborne traffic. DAA alerting and guidance in the context of ACAS XR is caution-level and “suggestive,” and is to be used by the pilot if, and when, they decide to maneuver against an identified threat to DAA “well clear.” The CA alerting, by contrast, is warning-level and “directive,” with the pilot required to comply with the associated guidance to prevent a predicted Near Midair Collision (NMAC). The CA alerts generated by ACAS XR are referred to as Resolution Advisories (RAs) consistent with previous CA systems. Unlike earlier CA systems, ACAS XR issues RAs in the horizontal and vertical dimensions as well as multi-axis RAs (referred to as “Blended” RAs). According to the Minimal Operational Performance Standards of DAA systems for Unmanned Aircraft Systems, maneuvers to comply with RAs may be automated, whereas maneuvers based on DAA alerting assume a manual response. The current presentation discusses two human-in-the-loop simulations that presented rotorcraft pilots with ACAS XR alerts and guidance in eVTOL simulators with varying levels of automation. It also presents overviews of these studies as well as how they assisted live flight tests, which will occur throughout 2023.

air taxis↗

DOE OSTI2020

Real-time Ridesharing for Transportation Hubs with Demand and Supply Uncertainty

Transportation hubs in major cities generate a significant amount of trips by taxis and for-hail vehicles (FHV), with many of the trips sharing similar destinations. This suggests promising opportunities to leverage the collective travel needs with dedicated ridesharing solutions to reduce the externalities of excessive traffic at transportation hubs. In this study, we develop a novel dynamic ridesharing approach to serve trips from the transportation hub by considering (1) demand (new passengers) and supply (newly available vehicles) in the near future and (2) the uncertainty of future predictions. Our approach consists of two stages. In the first stage, we develop a data structure called hub mobility tree to generate potential combinations of shareable trips as candidate schedules efficiently. Then the generated schedules are used in the second stage to formulate the stochastic hub-based ridesharing problem (SHRP), which is a stochastic integer programming problem with the objective to maximize the total expected ridesharing profit over time. Due to the prohibitive number of shareable trips, we then approximately solve SHRP by the sample average approximate method (SAA), and a dual Lagrangian technique is implemented to further improve the scalability of the solution approach. We demonstrate the performances of the proposed method by simulating the ridesharing service at JFK airport using NYC taxi and FHV data. The results indicate that the proposed method outperforms the myopic ridesharing (maximize profit for a single time step) and the rolling horizon method with point estimation of future demand and supply.

dynamic ridesharing↗

DOE OSTI2023

Energy-Centric Cooperative Onramp Merging Strategy: An Analytical Solution

Cooperative highway onramp merging plays an important role in mitigating highway traffic congestion. A crucial component of a cooperative merging system is the merging sequence strategy, which determines each merging participant's order to reach the merging point. Existing merging sequence strategies can be classified into rule-based and optimization-based approaches. The rule-based strategies can be effortlessly implemented with a light online computational burden. However, they may not achieve the optimal energy efficiency. In contrast, the optimization-based strategies can yield the optimal merging sequence to minimize fuel consumption, but typically involve computationally expensive numerical optimization. To leverage the advantages from both sides, we propose a novel merging sequence strategy that can minimize fuel consumption while avoiding online numerical optimization. The key idea is to analytically formulate the expected fuel consumption of each merging participant. Using a realistic highway onramp scenario based on the NGSIM dataset, we validate the performance and the computational efficiency of the proposed merging sequence strategy via SUMO/SIMULINK joint simulation.

Wang, Zejiang↗

NASA NTRSDate not supplied

An Examination of Two Non-Cooperative Detect and Avoid Well Clear Definitions

NASA’s Unmanned Aircraft Systems Integration into the National Airspace System (UAS in the NAS) project examines the technical barriers associated with the operation of UAS in civil airspace. The present study explored the differential effects of two candidate non-cooperative Detect-and-Avoid Well Clear (DWC)definitions on pilot and system performance in a human-in-the-loop simulation. Active-duty UAS pilots were recruited to maintain DWC against representative Class 4 encounter types with a low size, weight, and power (SWaP) radar declaration range of 3.5 nautical miles (nmi). Objective performance indicated that pilots could consistently maintain DWC against non-cooperative intruders with either DWC candidate, with negligible differences in response times and separation performance against caution and warning-level threats. While losses of DWC were avoided at rates comparable to Phase 1 findings, pilots uploaded their responses to caution-level alerts over 5 seconds faster in the current setup relative to Phase 1. Encounters with faster closure rates were susceptible to shortened caution-level alert durations, especially when employing the DWC criterion with the additional ‘Tau’ (temporal) component. Consequently, caution-level threats frequently elevated to warning-level status (nearly twice as often with theTau candidate). The variable caution alert durations appeared to impact pilots’ coordination with air traffic control (ATC), as ATC approval rates were lower with the ‘Tau’and ‘Disc’ candidates relative to Phase 1 research. Ultimately, the increased alerting time enabled by the Disc candidate deemed it more suitable for any reductions to the assumed radar declaration range requirement, which was re-evaluated in a follow-on study. Findings from this study will inform Phase 2 Minimum Operational Performance Standards (MOPS)development for UAS with alternative surveillance equipment and performance capabilities.

Kevin J Monk↗

NASA NTRS2000

Quantifying the Intercontinental and Global Reach and Effects of Pollution

The Atmospheric Chemistry Modeling Group is participating in an international effort to explore the projected interactions of the atmosphere with biota, human activity, and the natural environment over the next three decades. The group uses computer simulations and statistical analyses to compare theory and observations of the composition of the lower atmosphere. This study of global habitability change is part of a more ambitious activity to understand global habitability. This broad planetary understanding is central to planetary habitability, biomarker detection, and similar aspects of Astrobiology. The group has made highly detailed studies of immense intercontinental plumes that affect the chemistry of the global atmosphere, especially the region below the ozone (O3) layer whose chemical composition defines the conditions for healthy humans and the biosphere. For some decades there has been concern about the pollution from cities and industrial burning and its possible effect in increasing smog ozone, not only in continental regions, but also in plumes that spread downwind. Recently, there has been new concern about another kind of pollution plume. Projections for a greatly expanded aircraft fleet imply that there will be plumes of nitrogen oxides (NO(x)) from jet exhaust in the Northern Hemisphere downwind of major air traffic routes. Both of these are tied to large-scale O3 in the troposphere, where it is toxic to humans and plant tissues.

Chatfield, Robert B.↗

NASA NTRS2014

An Evaluation of a Flight Deck Interval Management Algorithm Including Delayed Target Trajectories

NASA's first Air Traffic Management (ATM) Technology Demonstration (ATD-1) was created to facilitate the transition of mature air traffic management technologies from the laboratory to operational use. The technologies selected for demonstration are the Traffic Management Advisor with Terminal Metering (TMA-TM), which provides precise timebased scheduling in the terminal airspace; Controller Managed Spacing (CMS), which provides controllers with decision support tools enabling precise schedule conformance; and Interval Management (IM), which consists of flight deck automation that enables aircraft to achieve or maintain precise in-trail spacing. During high demand operations, TMA-TM may produce a schedule and corresponding aircraft trajectories that include delay to ensure that a particular aircraft will be properly spaced from other aircraft at each schedule waypoint. These delayed trajectories are not communicated to the automation onboard the aircraft, forcing the IM aircraft to use the published speeds to estimate the target aircraft's estimated time of arrival. As a result, the aircraft performing IM operations may follow an aircraft whose TMA-TM generated trajectories have substantial speed deviations from the speeds expected by the spacing algorithm. Previous spacing algorithms were not designed to handle this magnitude of uncertainty. A simulation was conducted to examine a modified spacing algorithm with the ability to follow aircraft flying delayed trajectories. The simulation investigated the use of the new spacing algorithm with various delayed speed profiles and wind conditions, as well as several other variables designed to simulate real-life variability. The results and conclusions of this study indicate that the new spacing algorithm generally exhibits good performance; however, some types of target aircraft speed profiles can cause the spacing algorithm to command less than optimal speed control behavior.

Swieringa, Kurt A.↗

DOE OSTI2026

Cooperative Automated Cohort Driving on Connected Infrastructure, Arterial Roadways, and Highways: Final Project Demonstration and System-of-Systems Model Correlation

This project seeks to synergize vehicle automated driving and connectivity data to improve mobility and energy efficiency of groups of mixed vehicles operating in close proximity (vehicle cohort) on various infrastructure. A custom cellular communication network links vehicles operating as a cohort with infrastructure to a centralized system-of-systems digital twin with an AI-based optimal behavior planner. The data contained in this set are from final testing and technology demonstrations to U.S. Department of Energy staff at the American Center for Mobility. The data contain single-lane, single-light scenarios; multi-lane, multi-light arterial scenarios; and limited-access highway scenarios. All test cases were derived from simulations and replicated on the test track. The project employed two and four light-duty vehicles with connectivity and drive automation for the testing. The baseline scenario without connectivity was run under the control of the system-of-systems centralized planner but operating each vehicle with an intelligent driver model controlling the velocity, lane utilization, and vehicle gap. This was to ensure the highest compatibility with the simulation in terms of dynamic behavior. The connected cohort case utilized AI optimization to perform coordinated and cooperative control for energy, as well as safe, comfortable behavior for the cohort. The dataset is appropriately named with unconnected and connected designations, with comparisons sharing the same run index number. The included PowerPoint and PDF files describe the test setup and provide an overview of results from the project. ![image](de-EE0009209_March_2023_Data_Arterial_Scenario_Results.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Dynamic Queue Prediction at Signalized Intersections with Fusing Sensory Information and Connected Vehicles

Queue length is one of the most essential metrics for indicating traffic conditions at signalized intersections. Most existing studies mainly focus on queue length estimation in a retrieving way which cannot effectively predict the future state of vehicle in queue. This paper proposes an innovative dynamic queue prediction model in a vehicle-infrastructure cooperative way. The proposed dynamic queue predictor can customize the queue prediction using the trajectories of preceding vehicles from radar detection with varying information from loop detectors and connected vehicles (CVs). The dynamic queue prediction accuracy can be further improved with at least one present CV in the same cycle. With the accurate dynamic queue prediction, we present a case study of Queue-Aware Eco-Approach and Departure (QEAD) to provide an online optimal vehicle trajectory considering both the signal status and preceding vehicle's future status in queue. The proposed framework and algorithms are of pragmatic significance and one of its advanced features is the ability to work under low penetration rate of CVs. The test using Next Generation SIMulation (NGSIM) dataset and a case study in VISSIM microscopic simulation show promising results.

Ye, Fei↗

DOE OSTI2020

Deep Reinforcement Learning-Based Edge Caching in Wireless Networks

With the purpose to offload data traffic in wireless networks, content caching techniques have recently been studied intensively. Using these techniques and caching a portion of the popular files at the local content servers, the users can be served with less delay. Most of the content replacement policies are based on the content popularity, that depends on the users' preferences. In practice, such information varies over time. Therefore, an approach to determine the file popularity patterns must be incorporated into caching policies. In this context, we study content caching at the wireless network edge using a deep reinforcement learning framework with Wolpertinger architecture. In particular, we propose deep actor-critic reinforcement learning based policies for both centralized and decentralized content caching. For centralized edge caching, we aim at maximizing the cache hit rate. In decentralized edge caching, we consider both the cache hit rate and transmission delay as performance metrics. These proposed frameworks are assumed to neither have any prior information on the file popularities nor know the potential variations in such information. Via simulation results, the superiority of the proposed frameworks is verified by comparing them with other policies, including least frequently used (LFU), least recently used (LRU), and first-in-first-out (FIFO) policies.

42 ENGINEERING↗

DOE OSTI2025

Multicarrier Spread Spectrum Communications With Noncontiguous Subcarrier Bands for HF Skywave Links

Existing high-frequency (HF) radio platforms offer robust performance against the volatile HF propagation channel. However, the growing traffic across the band contests the reliability of these systems. While techniques to mitigate the effects of narrowband interference have been thoroughly explored, they are insufficient against wideband interference or when the transmission band is occupied by numerous scattered users. To improve reliability in these congested channel conditions, we propose a filter-bank based multicarrier spread-spectrum waveform with noncontiguous subcarrier bands. Using noncontiguous subcarrier bands enables the system to at once leverage the robustness of a wideband system while retaining the frequency agility of a narrowband system. In this study, we modify a filter-bank transmitter structure to accommodate noncontiguous subcarrier bands and consider several immediate impacts of this change, such as elevated peak-to-average-power ratios (PAPRs). A receiver architecture to process the noncontiguous spread-spectrum signal is also introduced, along with details regarding wideband channel estimation. Finally, we develop efficient transmitter and receiver structures to support practical system implementations. We conclude by comparing the performance of contiguous and noncontiguous systems through both simulation and over-the-air testing. The results show that the noncontiguous system remains robust in typical HF channels while significantly outperforming the contiguous system in congested spectral conditions.

(PAPR↗

NASA NTRS1987

Severe winds in the DFW microburst measured from two aircraft

Advanced analytical methods are applied to the digital flight data and Air Traffic Control radar records from two airliners, Delta flight 191 and American flight 539, that penetrated a microburst at the Dallas/Ft. Worth airport on August 2, 1985. The results for Delta 191 (which crashed) show that the aircraft encountered a strong downburst followed by a strong outflow accompanied by large and rapid changes in the vertical wind. The rapid changes in the vertical wind detected near the ground are attributed to vortex-induced turbulence. The next following aircraft, American 539, made a go-around 110 sec after Delta 191 and traversed the microburst at an altitude of about 2500 ft above the ground. The measured winds during this go-around indicate a broad pattern of downflow in the microburst, with regions of upflow at the extreme edges. The combined results indicate a microburst that was increasing in size with vortex-induced turbulence embedded in a strong outflow near the ground. These wind measurements provide a realistic description of the microburst wind environment that can be used in simulator studies and pilot training, and provide a standard of comparison for ongoing experiments and modeling of microbursts involving vortex rings.

Wingrove, R. C.↗

NASA NTRS2019

Ground Risk Assessment Service Provider (GRASP) Development Effort as a Supplemental Data Service Provider (SDSP) for Urban Unmanned Aircraft System (UAS) Operations

NASA’s Unmanned Aircraft System (UAS) Traffic Management (UTM) project aims to enable the integration of new aviation paradigms such as Unmanned Aircraft Systems (UAS) while providing the necessary infrastructure for future concepts such as On-Demand Mobility (ODM) and Urban Air Mobility (UAM) operations in the National Airspace System (NAS). In order to do so, the UTM project has developed an architecture to allow communication among UAS operators, UAS Service Suppliers (USS), Air Navigation Service Providers (ANSP), and the public. As part of this framework, the Supplemental Data Service Providers (SDSP) are envisioned as model and/or data based services that disseminate essential or enhanced information to ensure safe operations within low-altitude airspace. These services include terrain and obstacle data, specialized weather data, surveillance, constraint information, risk monitoring, etc. This paper highlights the development efforts of a non-participant casualty risk assessment SDSP called Ground Risk Assessment Service Provider (GRASP) which assists operators with preflight planning. GRASP is based on the previously introduced UTM Risk Assessment Framework (URAF) and allows UAS operators to simulate and visualize potential non-participant casualty risks associated with their proposed flight. The risk assessment capability also allows operators to revise their flight plans if the casualty risks are determined to be above acceptable thresholds. GRASP is configured to account for future improvements including servicing airborne aircraft as part of NASA’s System-Wide Safety (SWS) project.

Ancel, Ersin↗

NASA NTRS1991

The performance evaluation of a new neural network based traffic management scheme for a satellite communication network

A neural-network-based traffic management scheme for a satellite communication network is described. The scheme consists of two levels of management. The front end of the scheme is a derivation of Kohonen's self-organization model to configure maps for the satellite communication network dynamically. The model consists of three stages. The first stage is the pattern recognition task, in which an exemplar map that best meets the current network requirements is selected. The second stage is the analysis of the discrepancy between the chosen exemplar map and the state of the network, and the adaptive modification of the chosen exemplar map to conform closely to the network requirement (input data pattern) by means of Kohonen's self-organization. On the basis of certain performance criteria, whether a new map is generated to replace the original chosen map is decided in the third stage. A state-dependent routing algorithm, which arranges the incoming call to some proper path, is used to make the network more efficient and to lower the call block rate. Simulation results demonstrate that the scheme, which combines self-organization and the state-dependent routing mechanism, provides better performance in terms of call block rate than schemes that only have either the self-organization mechanism or the routing mechanism.

Ansari, Nirwan↗