The Unmanned Aircraft System (UAS) Traffic Management (UTM) effort at NASA aims to enable access to low-altitude airspace for small UAS. This goal is being pursued partly through partnerships that NASA has developed with the UAS stakeholder community, the FAA, other government agencies, and the designated FAA UAS Test Sites. By partnering with the FAA UAS Test Sites, NASA's UTM project has performed a geographically diverse, simultaneous set of UAS operations at locations in six states. The demonstrations used an architecture that was developed by NASA in partnership with the FAA to safely coordinate such operations. These demonstrations-the second or "Technical Capability Level (TCL 2)" National Campaign of UTM testing-was performed from May 15 through June 9, 2017. Multiple UAS operations occurred during the testing at sites located in Alaska, Nevada, Texas, North Dakota, Virginia, and New York with multiple organizations serving as UAS Service Suppliers and/or UAS Operators per the specifications provided by NASA. By engaging various members of the UAS community in development and operational roles, this campaign provided initial validation of different aspects of the UTM concept including: UAS Service Supplier technologies and procedures; geofencing technologies/conformance monitoring; groundbased surveillance/sense and avoid; airborne sense and avoid; communication, navigation, surveillance; and human factors related to UTM data creation and display. Additionally, measures of performance were defined and calculated from the flight data to establish quantitative bases for comparing flight test activities and to provide potential metrics that might be routinely monitored in future operational UTM systems.

In recent years, NASA has initiated the development of a focused near-to mid-term concept called "Integrated Demand Management" (IDM) under the Airspace Operations and Safety Program (AOSP). The focus of the research has been to develop more powerful, integrated operations and tools for managing trajectory constraints, leveraging existing systems and adding new automation tools and methods where needed. The IDM concept is predicated on the idea that in situations where the capacity of critical resources (such as airspace or airports) is insufficient to meet demand, a better match between available capacity and the predicted demand would significantly benefit the operations, with potential improvements in throughput, delays, and efficient flight trajectories. In current operations the reasons for the capacity/demand mismatches can vary, and range from problems due to structural limitations (e.g., surface capacity at high-volume airports, high-complexity en route or arrival airspace); to wind-related capacity changes; to the more severe, unstable and dynamic mismatches that occur with convective weather. The IDM solution proposes to address the demand / capacity mismatches by using the strategic flow management capabilities within the traffic flow management system (TFMS) toolset to "pre-condition" demand into the more tactical time-based flow management (TBFM) system, which should enable TBFM to better manage delivery to the capacity-constrained resource(s). The intent of IDM is to leverage the strengths of each of these systems to produce an integrated solution that is more powerful and robust than either could provide alone, or than the two would provide today as uncoordinated systems.Newark Liberty International Airport (EWR) was chosen to be the focus of our initial design problem for several reasons. EWR routinely sees scheduled demand at or near airport capacity through much of the day with a varying mix of short-haul and long-haul flights. Although this is usually managed effectively using miles-in-trail and TBFM metering, close-in departures can experience excessive and unpredictable ground delay if the overhead flow is saturated. In the initial development of IDM concept, an alternative solution to this volume problem was proposed that integrates 3 key capabilities: 1) Collaborative Trajectory Options Program (CTOP) capability within TFMS to issue traffic management initiatives that can "strategically" manage demand into the TBFM system; 2) TBFM capability closer to the destination airport to "tactically" manage delivery to the capacity-constrained destination; and 3) required-time-of-arrival (RTA) capability on the flight deck to provide a more controlled traffic demand using the CTOP derived schedule into the TBFM domain.The IDM concept development is ongoing and iterative, based on inputs from the FAA and airline stakeholders, as well as on insights gained from a series of human-in-the-loop

This paper introduces NASA's Integrated Demand Management (IDM) concept and presents the results from an early proof-of-concept evaluation and an exploratory experiment. The initial development of the IDM concept was focused on integrating two systems-i.e. the FAA's newly deployed Traffic Flow Management System (TFMS) tool called the Collaborative Trajectory Options Program (CTOP) and the Time-Based Flow Management (TBFM) system with Extended Metering (XM) capabilities-to manage projected heavy traffic demand into a capacity-constrained airport. A human-in-the-loop (HITL) simulation experiment was conducted to demonstrate the feasibility of the initial IDM concept by adapting it to an arrival traffic problem at Newark Liberty International Airport (EWR) during clear weather conditions. In this study, the CTOP was utilized to strategically plan the arrival traffic demand by controlling takeoff times of both short-and long-haul flights (long-hauls specify aircraft outside TBFM regions and short-hauls specify aircraft within TBFM regions) in a way that results in equitable delays among the groups. Such strategic planning decreases airborne and ground delay within TBFM by delivering manageable long-haul traffic demand while reserving sufficient slots in the overhead streams for the short-haul departures. A manageable traffic demand ensures the TBFM scheduler does not assign more airborne delay than a particular airspace is capable of absorbing. TBFM uses its time-based metering capabilities to deliver the desirable throughput by tactically coordinating and scheduling the long-haul flights and short-haul departures. Additional research was performed to explore the use of Required Time of Arrival (RTA) capabilities as a potential control mechanism to improve the arrival time accuracy of scheduled long-haul traffic. Results indicated that both short-and long-haul flights received similar ground delays. In addition, there was a noticeable reduction in the total amount of excessive, unanticipated ground delays, i.e. delays that are frequently imposed on the shorthaul flight in current day operations due to saturation in the overhead stream, commonly referred to as 'double penalty.' Furthermore, the concept achieved the target throughput while minimizing the expected cost associated with overall delays in arrival traffic. Assessment of the RTA capabilities showed that there was indeed improvement of the scheduled entry times into TBFM regions by using RTA capabilities. However, with respect to reduction in delays incurred within TBFM, there was no observable benefit of improving the precision of entry times for long-haul flights.

the objective of this study is to explore the use of Required Time of Arrival (RTA) capability on the flight deck as a control mechanism on arrival traffic management to improve traffic delivery accuracy by mitigating the effect of traffic demand uncertainty. The uncertainties are caused by various factors, such as departure error due to the difference between scheduled departure and the actual take-off time. A simulation study was conducted using the Multi Aircraft Control System (MACS) software, a comprehensive research platform developed in the Airspace Operations Laboratory (AOL) at NASA Ames Research Center. The Crossing Time (CT) performance (i.e. the difference between target crossing time and actual crossing time) of the RTA for uncertainty mitigation during cruise phase was evaluated under the influence of varying two main factors: wind severity (heavy wind vs. mild wind), and wind error (1 hour, 2 hours, and 5 hours wind forecast errors). To examine the CT performance improvement made by the RTA, the comparison to the CT of the aircraft that were not assigned with RTA (Non-RTA) under the influence of the selected factors was also made. The Newark Liberty International Airport (EWR) was chosen for this study. A total 66 inbound traffic to the EWR (34 of them were airborne when the simulation was initiated, 32 were predepartures at that time) was simulated, where the pre-scripted departure error was assigned to each pre-departure (61 % conform to their Expected Departure Clearance Time, which is +/-300 seconds of their scheduled departure time). The results of the study show that the delivery accuracy improvement can be achieved by assigning RTA, regardless of the influence of the selected two factors (the wind severity and the wind information inaccuracy). Across all wind variances, 66.9% (265 out of 396) of the CT performance of the RTA assigned aircraft was within +/-60 seconds (i.e. target tolerance range) and 88.9% (352 out of 396) aircraft met +/-300 seconds marginal tolerance range, while only 33.6% (133 out of 396) of the Non-RTA assigned aircraft's CT performance achieved the target tolerance range and 75.5% (299 out of 396) stayed within the marginal. Examination of the impact of different error sources -i.e. departure error, wind severity, and wind error -suggest that although large departure errors can significantly impact the CT performance, the impacts of wind severity and errors were modest relative the targeted +/-60 second conformance range.

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Research is described for realizing a Remote Airport Traffic Control Center (DLR project RAiCe) for remote surveillance and control of several small airports from a central location. Work and task analyses performed in a previous project resulted in the concept of a high resolution video panorama system with zoom and augmented vision functions as controllers main HMI in the Remote Tower Center (RTC). Video-see-through augmentation of the reconstructed outside view by means of superimposed flight information and data from electronic non-visual sources is supposed to improve the controllers situational awareness. The augmented vision function allows for a compact RTO-work environment due to its potential for reduction of displays.A corresponding 180°-video panorama system was set up as experimental testbed at Braunschweig research airport which served for initial field testing. It consists of four digital high resolution CCD cameras located near Braunschweig tower, and a remotely controlled pan-tilt zoom (PTZ) camera (including automatic tracking option) with PC clusters for compression, image processing / movement detection, decompression and panorama reconstruction, and a 450 m fiberoptic Gbit Ethernet link between sensor and display clusters. Field testing of the reconstructed far view with participation of local controllers shows an effective visual resolution of < 2 arcmin in agreement with the theoretical predictions. The PTZ camera provides a "foveal" vision with a high resolution exceeding the human eye (1 arcmin) within an observation angle < 15°.In addition to the experimental testbed simulation systems for two-airport control are under development for support of the RTC work environment design, based on a 200°-tower-simulator with RTO-console extension and a simplified twoairport microworld computer simulation for laboratory type part task simulations.

NASA's UAS Traffic Management (UTM) project concluded its second flight demonstration activity in late October 2016. This activity demonstrated the capabilities and functionality incorporated into its Technical Capability Level 2 (TCL 2) concept, which envisions future operations that are low density, capable of being performed over sparsely populated areas, and allow for a concurrent mix of longer duration, beyond visual-line-of-sight flights and shorter flights within visual-lineof-sight (VLOS). To incorporate these features into a flight demonstration, a scenario-based approach was taken to address different aspects of the TCL 2 environment and to meet defined objectives. This paper will describe elements of how the flight activity was conducted and present analyses regarding UTM operations, system messages, and alerting as they pertained to meeting the demonstration objectives and shedding light on research questions and lessons learned.

Today, capturing the behavior of a human eye is considered a standard method for measuring the information-gathering process and thereby gaining insights into cognitive processes. Due to the dynamic character of most task environments there is still a lack of a structured and automated approach for analyzing eye movement in combination with moving objects. In this article, we present a guideline for advanced gaze analysis, called IGDAI (Integration Guideline for Dynamic Areas of Interest). The application of IGDAI allows gathering dynamic areas of interest and simplifies its combination with eye movement. The first step of IGDAI defines the basic requirements for the experimental setup including the embedding of an eye tracker. The second step covers the issue of storing the information of task environments for the dynamic AOI analysis. Implementation examples in XML are presented fulfilling the requirements for most dynamic task environments. The last step includes algorithms to combine the captured eye movement and the dynamic areas of interest. A verification study was conducted, presenting an air traffic controller environment to participants. The participants had to distinguish between different types of dynamic objects. The results show that in comparison to static areas of interest, IGDAI allows a faster and more detailed view on the distribution of eye movement.