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Papers and presentations from the conference are presented. The topics covered include the following: satellite network architecture, network control and protocols, fault tolerance and autonomy, multichanned demultiplexing and demodulation, information switching and routing, modulation and coding, and planned satellite communications systems.
This paper extends the process for validating the Airspace Concept Evaluation System using real-world historical flight operational data. System inputs such as flight plans and airport en-route capacities, are generated and processed to create a realistic reproduction of a single day's operations within the National Airspace System. System outputs such as airport throughput, delays, and en-route sector loads are then compared to real world operational metrics and delay statistics for the reproduced day. The process is repeated for 4 historical days with high and low traffic volume and delay attributed to weather. These 4 days are simulated using default en-route capacities and variable en-route capacities used to emulate weather. The validation results show that default enroute capacity simulations are closer to real-world data for low weather days than high weather days. The use of reduced variable enroute capacities adds a large delay bias to ACES but delay trends between weather days are better represented.
In January 2013, a human-in-the-loop evaluation of the Operational Airspace Sectorization Integrated System (OASIS) was conducted in the Airspace Operations Laboratory of the Human Systems Integration Division (Code TH) in conjunction with the Aviation Systems Division (Code AF). The development of OASIS is a major activity of the Dynamic Airspace Configuration (DAC) research focus area within the Aeronautics Research Mission Directorate (ARMD) Airspace Systems Program. OASIS is an advisory tool to assist Federal Aviation Administration (FAA) En Route Area Supervisors in their planning of sector combinedecombine operations as well as opening closing of Data-side (D-side) control positions. These advisory solutions are tailored to the predicted traffic demand over the next few hours. During the experiment, eight retired FAA personnel served as participants for a part-task evaluation of OASIS functionality, covering the user interface as well as the underlying algorithm. Participants gave positive feedback on both the user interface and the algorithm solutions for airspace configuration, including an excellent average rating of 94 on the tool usability scales. They also suggested various enhancements to the OASIS tool, which will be incorporated into the next tool development cycle for the full-scale human-in-the-loop evaluation to be conducted later this year.
As an integral part of the overall upgraded National Airspace System (NAS), the objective of the Real-Time Weather Processor (RWP) project is to improve the quality of weather information and the timeliness of its dissemination to system users. To accomplish this, an RWP will be installed in each of the Center Weather Service Units (CWSUs), located in 21 of the 23 Air Route Traffic Control Centers (ARTCCs). The RWP System is a prototype system. It is planned that the software will be GFE and that production hardware will be acquired via industry competitive procurement. The ARTCC is a facility established to provide air traffic control service to aircraft operating on Instrument Flight Rules (IFR) flight plans within controlled airspace, principally during the en route phase of the flight. Covered here are requirement metrics, Software Problem Failure Reports (SPFRs), and Ada portability metrics and observations.
The visibility of pavement markings is one of the most critical factors for traffic safety, and a periodical assessment plan is crucial for maintaining this function. Traditional assessment methods, such as visual windshield surveys or manual testing using handheld devices, are unsafe, time-consuming, and labor-intensive. In recent years, transportation agencies have begun to adopt the use of mobile retroreflectivity units (MRUs) for condition assessment of pavement markings. MRUs, different from other manual methods, can be utilized to collect large-scale retroreflectivity data in an efficient manner. However, no relevant research has yet proposed a mathematical optimization model for arranging the evaluation schedule and paths of MRUs. This study aims to propose a MRU routing model, and an efficient solution methodology. A branch-and-price algorithm, including column generation and branch-and-bound, was implemented. Computational experiments have been conducted based on actual tasks from the Florida MRU program for validation. In conclusion, results show that the proposed solution methodology with a set partitioning model in this study not only finds the optimal solution for problems with tasks less than 60, but also effectively narrows the solution gap to be within 1.0% for problems with tasks less than 131.
The Dynamic Weather Routes (DWR) toolcontinuously and automatically analyzes airborne flights in en route airspace to identify flights where a route correction could save significant flight time and still avoid weather. A partnership between NASA, American Airlines (AA), and the FAA has enabled testing of DWR in real-world air traffic operations. En route Data Communications (Data Comm) could significantly reduce the controller and pilot workload needed to communicate DWR route changes under today’s voice-based operations and thereby enable more timely and frequent high-value corrections to weather avoidance routes. Sample data from the DWR trial at AA illustrate how Data Comm could improve DWR operations. Two operating concepts that integrate DWR with Data Comm are described:(1) route corrections are initiated by air traffic control and implemented using Airborne Reroutes and Data Comm, and (2) route corrections are initiated by the dispatcher and pilot and implemented via Data Comm. Both concepts align with Federal Aviation Administration (FAA) plans for implementation ofData Comm in en route airspace.
NASA and the FAA have designed and developed and an automation tool known as the Traffic Management Advisor (TMA). The system was operationally evaluated at the Ft. Worth Air Route Traffic Control Center (ARTCC). The TMA is a time-based strategic planning tool that provides Traffic Management Coordinators and En Route Air Traffic Controllers the ability to efficiently optimize the capacity of a demand impacted airport. The TMA consists of trajectory prediction, constraint-based runway scheduling, traffic flow visualization and controllers advisories. The TMA was used and operationally evaluated for forty-one rush traffic periods during a one month period in the Summer of 1996. The evaluations included all shifts of air traffic operations as well as periods of inclement weather. Performance data was collected for engineering and human factor analysis and compared with similar operations without the TMA. The engineering data indicates that the operations with the TMA show a one to two minute per aircraft delay reduction during rush periods. The human factor data indicate a perceived reduction in en route controller workload as well as an increase in job satisfaction. Upon completion of the evaluation, the TMA has become part of the normal operations at the Ft. Worth ARTCC.
This is the API endpoint for RouteE energy prediction, which can be used to get both single vehicle link or route energy estimates and transportation network-wide energy consumption estimates for a variety of vehicles. This enables external researchers and transportation engineers to access and utilize NREL's growing library of pre-trained vehicle models for prediction of transportation energy consumption. This API provides three endpoints: • /route: Energy estimation of a vehicle over a planning link or sequence of links (route). • /network: Network-wide estimation of energy consumption for all vehicle traffic in the desired area. • /compass: Energy-optimal “eco-routing” between input origin and destination coordinates (Currently in beta for Denver metro area only).
This is the API endpoint for RouteE energy prediction, which can be used to get both single vehicle link or route energy estimates and transportation network-wide energy consumption estimates for a variety of vehicles. This enables external researchers and transportation engineers to access and utilize NREL's growing library of pre-trained vehicle models for prediction of transportation energy consumption. This API provides three endpoints: - /route: Energy estimation of a vehicle over a planning link or sequence of links (route). - /network: Network-wide estimation of energy consumption for all vehicle traffic in the desired area. - /compass: Energy-optimal “eco-routing” between input origin and destination coordinates (Currently in beta for Denver metro area only).
This is the API endpoint for RouteE energy prediction, which can be used to get both single vehicle link or route energy estimates and transportation network-wide energy consumption estimates for a variety of vehicles. This enables external researchers and transportation engineers to access and utilize NLR's growing library of pre-trained vehicle models for prediction of transportation energy consumption. This API provides three endpoints: - /route: Energy estimation of a vehicle over a planning link or sequence of links (route). - /network: Network-wide estimation of energy consumption for all vehicle traffic in the desired area. - /compass: Energy-optimal “eco-routing” between input origin and destination coordinates (Currently in beta for Denver metro area only).
In terminal airspace, integrating arrivals, departures, and surface operations with competing resources provides the potential of improving operational efficiency by removing barriers between different operations. This work develops a centralized stochastic scheduler for operations in a terminal area including airborne and surface operations using Non-dominated sorting genetic algorithm and Monte Carlo simulations. The scheduler handles completing resources between different flows, such as runway allocations, runway crossing, departure fixes, and other interaction way points between arrivals and departures. Meanwhile, the scheduler also takes time-varied uncertainties into account when optimizing schedules. The scheduler is run sequentially to identify the best and robust schedule for the next planning window. Resulting schedules decide the routes, speed or delays, and runway assignments with separation constraints at mergingdiverging waypoints in the air and crossing and separations on runways. The Los Angels terminal area was used as an example. The implementation of this stochastic scheduler for integrated arrival, departure and surface operations is completed. And several preliminary runs are finished for over 1,200 flights in LAX in a typical day. Sensitivity studies on various planning window sizes are presented, which shows that trade-off exits between planning window size and achievable minimum delay. Preliminary results on runway usage are also presented in this abstract. Because arrivals on the outer runways have to be followed by crossings on the inner runways, algorithmic runway allocation prefers inner runways for arrivals and outer runways for departures. More results will be presented in the final paper. And current terminal arrival and departure procedures based on first-come-first-serve procedure will also be set up and used as a baseline for comparison.
Recently, the problem of compiling general quantum algorithms for implementation on near-term quantum processors has been introduced to the AI community. Previous work demonstrated that temporal planning is an attractive approach for part of this compilation task, specifically, the routing of circuits that implement the Quantum Alternating Operator Ansatz (QAOA) applied to theMaxCut problem on a quantum processor architecture. In this paper, we extend the earlier work to route circuits that implement QAOAfor Graph Coloring problems. QAOA for coloring requires execution of more, and more complex, operations on the chip, which makes routing a more challenging problem. We evaluate the approach on state-of-the-art hardware architectures from leading quantum computing companies. Additionally, we investigate applying the planning approach to qubit initialization as well as routing. Our empirical evaluation shows that temporal planning compares well to reasonable analytic upper bounds [20], and that solving qubit initialization with a classical planner generally helps temporal planners in finding shorter-makespan compilations for QAOA for Graph Coloring.These advances suggest that temporal planning can be an effective approach for more complex quantum computing algorithms and architectures.
Safety and security are priorities of U.S. Department of Energy (DOE) radioactive materials shipping campaigns. In the more than 70-year history of domestic transport of spent nuclear fuel (SNF), there has never been a transportation-related radiological injury. To support transportation planning, among the tools that DOE uses is the Stakeholder Tool for Assessing Radioactive Transportation (START). START contains geospatial data and transportation route analyses capabilities designed to support a range of DOE transportation planning initiatives. One of those functions is the capability to support emergency response planning and training for State and Tribal jurisdictions located on routes used for DOE shipments of radioactive materials. As part of the Department's commitment to public safety, DOE provides federally-funded radiological response training to emergency responders along DOE radioactive materials transportation corridors through its Transportation Emergency Preparedness Program (TEPP). START contains spatial data representing the locations and emergency-response capabilities of fire departments, police, hospitals, State emergency response centers, and where TEPP-trained personnel are based. The START tool supports State and Tribal users' ability to evaluate emergency-response coverage on active and potential DOE radioactive materials transport routes through their jurisdictions, provide expected response times to reach the scene of an incident, identify equipment available to support a response, and identify the number of response personnel and their respective training levels. In addition, START can be used to identify gaps in coverage along a transportation corridor where additional radiological emergency response training may be needed. This paper describes the data, features, and functionality DOE uses to provide a resource for emergency response training needs assessments for States and Tribes along active and potential routes for transporting radioactive materials, and illustrates its use. It also discusses future plans to integrate TEPP and Federal Emergency Management Agency (FEMA) radiological training data to provide a more comprehensive source of geospatial information on personnel who have received equivalent radiological response training. (authors)
A study was conducted to examine the map and route cues pilots use while navigating under controlled, but realistic, nap-of-the-earth (NOE) flight conditions. US Army helicopter flight crews were presented a map and route overlay and asked to perform normal mission planning. They then viewed a video-recording of the out-the-window scene during low-level flights, without the route overlay, and were asked periodically to locate their current position on the map. The pilots and navigators were asked to communicate normally during the planning and flight phases. During each flight the navigator's response time, accuracy, and subjective workload were assessed. Post-flight NASA-TLX workload ratings were collected. No main effect of map orientation (north-up vs. track-up) was found for errors or response times on any of the tasks evaluated. Navigators in the north-up group rated their workload lower than those in the track-up group.
Many airspace threats affect the selection of a flight route, such as terrain, physical obstacles, adverse weather, and special-use airspace, among others. Threat avoidance during Urban Air Mobility (UAM) and Low Altitude Mobility (LAM) flights is especially challenging due to their lower cruising altitudes. These operations may be exposed to buildings, towers, trees, terrain undulations, etc., along much of their flight route. Moreover, these low-altitude flights in urban environments are expected to encounter very busy airspace, increasing the workload associated with threat avoidance. The Proximity to Threat (PtT) function aims to support onboard or remote pilots by computing the risk from geospatial threats in the airspace along an aircraft’s flight path. The threats are both static entities and dynamic airspace restrictions that have been modeled and stored in geo-referenced mapping databases. The flight path may be specified by waypoints, airways, or similar discrete route elements or by a time series of closely spaced position and altitude points. Flight path uncertainty can also be taken into account by assuming both the position and altitude to be sampled from two normal distributions, each being specified by a mean and variance. PtT uses this additional information to verify that the flight path remains clear of threats along a wider path or if a position is reached earlier or later than anticipated, assuring the pilot or operator that there will be an available safety margin even if the aircraft deviates due to one or more occurrences of unexpected wind gusts, mechanical failures, airspeed changes, collision avoidance maneuvers, etc. The client can specify an uncertainty confidence level to bound the set of trajectories evaluated for threats so that the confidence level corresponds to the client’s risk tolerance under the expected conditions. PtT can be used as a pre-flight planning tool to determine a safe route through known threats and in-flight to avoid emerging or changing threats. In this report, we present the PtT function, discuss how the trajectory uncertainty is incorporated into the PtT function, and describe the use of this PtT function in a representative scenario.
Urban Air Mobility (UAM) is a new transportation concept that enables highly automated, cooperative, passenger or cargo-carrying air transportation services in and around urban areas. To achieve the high level of operational density and complexity desired by the UAM community, an airspace system that allows UAM operators to readily access and operate safely and efficiently in the airspace is needed. This airspace system will require air traffic management designed to reduce the risk of conflicts and loss of separation between UAM flights. In general, strategic conflict management is considered as the first layer of conflict management for safe flight operations to condition the traffic to reduce the need for airborne separation provision, the second layer of conflict management. Demand Capacity Balancing (DCB) is one of the concept components to achieve strategic conflict management. DCB strategically evaluates traffic demand and resource capacities to allow UAM operators to determine when, where and how they operate, while mitigating conflicting needs for airspace and vertiport capacity. DCB can be applied whenever UAM demand exceeds the capacity in airspace or at vertiports. As the UAM ecosystem evolves with advanced technologies and matured operational procedures, more complicated conflict management will likely be needed. In the current UAM ‘Concept of Operation (ConOps) 1.0’ operational stage defined by FAA, however, it will be meaningful to explore the demand capacity balancing at vertiports only, as an initial strategic conflict management approach for UAM operations because vertiport capacity seems to be a bottleneck of UAM traffic. For this research, we developed a demand-capacity imbalance detection and resolution service for UAM. This DCB service identifies the demand from operators and compares the demand to a given capacity at the shared resources (i.e., vertiports) over the upcoming time horizon which is divided into time bins having a constant interval. When a new flight plan is submitted, the algorithm embedded in the DCB service checks the available time bins based on the desired departure time and estimated arrival time at origin and destination vertiports, respectively. If the time bins for the originally desired times are already occupied by other flights (i.e., demand is at or above capacity), the algorithm finds the next available time bins for takeoff and landing and shifts the conflicting departure time to the earliest time that satisfies the capacity constraints at both origin and destination vertiports. The details of the algorithm will be described in the final manuscript. Figure 1 shows that the proposed DCB algorithm works well for a sample traffic scenario. In this example, a total of 144 flights, split between two operators, are planned over 2 hours, traveling 10 routes between five vertiports. In the heatmaps, the horizontal axis shows 12 time bins where each bin represents a 12-minute interval, and the vertical axis shows five vertiports. The number in each cell shows the number of operations, counting both departures and arrivals, at a specific vertiport in each time bin. For the given capacity of 2 operations/vertiport/bin, Figure 1 shows that the original demand sometimes exceeds the capacity, but the modified demand is reduced to the given capacity after resolving demand-capacity imbalances. When UAM flights are operated, it is expected that many practical issues would arise in the federated system architecture with multiple operators. UAM operators may experience a time synchronization issue due to communication delay between operator and vehicle. UAM vehicles would fly at different flight speeds, depending on vehicle models. Actual departure and arrival times can have large variations, compared to the schedule. The lead time from flight plan submission to desired departure time can vary by service type (e.g., regular shuttle service vs. on-demand service). Using the proposed DCB algorithm, we also investigated how the actual flight schedule and DCB performance are affected by these uncertainties such as unsynchronized times between operators, flight speed differences, lead time differences, and departure time errors. The final manuscript will include the background of this research work, the description of the DCB algorithm and its use cases with traffic scenarios. It will also provide the analytical results about the impact of various uncertainties that can occur in actual UAM operations on the DCB at vertiports, in terms of demand distribution changes, number of simultaneous operations, and delay propagation.
The following document summarizes project results from “CORE-CM in the Greater Green River and Wind River Basins: Transforming and Advancing a National Coal Asset”. This project is part of the U.S. Department of Energy’s (“DOE”) National Energy Technology Laboratory’s (“NETL”) Carbon Ore, Rare Earth Elements, and Critical Minerals (CORE-CM) Initiative. This report concludes that the Greater Green River and Wind River Basins (GGRB-WRB) Area-of-Interest (AOI 9) is the ideal region for continued research and development in progressing the broader CORE-CM goals outlined by the DOE. Based upon the extensive analyses of technical, social, and community criteria, this report illustrates that the GGRB-WRB hosts numerous potential CORE-CM feedstocks (both coal- and non-coal based), diverse opportunities for utilizing existing industrial waste streams, ample infrastructure and industry to support new CORE-CM-focused technologies, and a highly motivated, well educated, and adaptable workforce to further develop the regional and national CORE-CM supply chain. Additionally, some potential solutions for technological gaps suggest that the GGRB-WRB's diverse resources can play a significant role in achieving the national goal of critical materials independence. With full community participation, meaningful involvement of regional Tribal Nations, and building upon the stakeholder engagement demonstrated here, the GGRB-WRB region presents a unique opportunity for advancing the CORE-CM Initiative. This project was designed to bring together coal-based communities and stakeholders from across the GGRB-WRB to advance new industries for CORE-CM resources. The University of Wyoming (UWyo) School of Energy Resources (SER) led a project team of experts from the Colorado Geological Survey (CGS), Colorado School of Mines (CSM), Los Alamos National Lab (LANL), and local community colleges. Input from basinal, regional, and national experts bolstered the coalition in order to advance the mission of DOE’s CORE-CM initiative and develop the domestic CORE-CM supply chain. Phase I of this project was designed to address the goal of developing and catalyzing economic growth, job creation, and technology innovation in the GGRB-WRB of Wyoming and Colorado, by increasing the supply of CORE-CM to manufacturers of non-fuel Carbon Based Products (CBP) and products reliant upon CM. The GGRB-WRB CORE-CM project worked toward providing benefit through several avenues of performance and research. • Develop a coalition team to achieve project objectives • Complete detailed assessments, including State-of-the-Art (SOTA) Data acquisition of potential CORE-CM materials across the AOI, and meaningfully contributes to DOE’s CORE-CM goals nationally. • Strategic planning for regional economic growth, job creation, and associated technology innovation around coal materials, including plans to maximize the development of potential CORE-CM resources and technology by creating regional public-private partnerships. • Define regional economic growth potential around existing strengths, energy infrastructure, business and industry, including planning for the leveraging of highly trained workforces, existing and novel coal technologies, and energy infrastructure in development of CORE-CM supply chains. • Develop a preliminary strategic plan for increasing the supply of CORE-CM materials to manufacturers of non-fuel Carbon Based Products (CBP) and products reliant upon CM, focusing on regional strengths that result in an emerging diversified CORE-CM economy. • Assemble a committed network of stakeholders and communities that learn about, accept, and grow new energy technologies within coal regions. Additionally, the project team significantly contributed to the CORE-CM Initiative’s national goals, through cross-regional scoping, collaborating with CORE-CM projects in other AOIs, and including parallel regional project experts. In addition to active inclusion and meaningful engagement and contribution to DOE-led working groups, the project team focused on engaging with regional communities including Tribal Nations, economic development groups, and regional government organizations. The project’s CORE-CM development and commercialization plan identified diverse CORECM feedstocks, potential routes towards integration with existing industries, methods for supply-chain development that leverage existing infrastructure and businesses considering the regional economy, identified entry barriers for incorporating traditional and new technologies in those supply chains, recognized opportunities for public-private partnerships to develop technology innovation centers, identified diverse workforces, and conducted stakeholder outreach and education to build a community of understanding on CORE-CM potential in the GGRB-WRB region. Detailed task descriptions can be found in each chapter.
The aviation community is faced with reducing the fatal aircraft accident rate by 80 percent within 10 years. This must be achieved even with ever increasing, traffic and a changing National Airspace System. This is not just an altruistic goal, but a real necessity, if our growing level of commerce is to continue. Honeywell Technology Center's topical study, "Weather Avoidance Using Route Optimization as a Decision Aid", addresses these pressing needs. The goal of this program is to use route optimization and user interface technologies to develop a prototype decision aid for dispatchers and pilots. This decision aid will suggest possible diversions through single or multiple weather hazards and present weather information with a human-centered design. At the conclusion of the program, we will have a laptop prototype decision aid that will be used to demonstrate concepts to industry for integration into commercialized products for dispatchers and/or pilots. With weather a factor in 30% of aircraft accidents, our program will prevent accidents by strategically avoiding weather hazards in flight. By supplying more relevant weather information in a human-centered format along with the tools to generate flight plans around weather, aircraft exposure to weather hazards can be reduced. Our program directly addresses the NASA's five year investment areas of Strategic Weather Information and Weather Operations (simulation/hazard characterization and crew/dispatch/ATChazard monitoring, display, and decision support) (NASA Aeronautics Safety Investment Strategy: Weather Investment Recommendations, April 15, 1997). This program is comprised of two phases, Phase I concluded December 31, 1998. This first phase defined weather data requirements, lateral routing algorithms, an conceptual displays for a user-centered design. Phase II runs from January 1999 through September 1999. The second phase integrates vertical routing into the lateral optimizer and combines the user interface into a prototype software testbed. Phase II concludes with a dispatcher and pilot evaluation of the route optimizer decision aid. This document describes work completed in Phase I in contract with NASA Langley August 1998 - December 1998. This report includes: (1) Discuss how weather hazards were identified in partnership with experts, and how weather hazards were prioritized; (2) Static representations of display layouts for integrated planning function (3) Cost function for the 2D route optimizer; (4) Discussion of the method for obtaining, access to raw data of, and the results of the flight deck user information requirements definition; (5) Itemized display format requirements identified for representing weather hazards in a route planning aid.