Urban Air Mobility-defined as safe and efficient air traffic operations in a metropolitan area for manned aircraft and unmanned aircraft systems-is being researched and developed by industry, academia, and government. Significant resources have been invested toward cultivating an ecosystem for Urban Air Mobility that includes manufacturers of electric vertical takeoff and landing aircraft, builders of takeoff and landing areas, and researchers of the airspace integration concepts, technologies, and procedures needed to conduct Urban Air Mobility operations safely and efficiently alongside other airspace users. This paper provides high-level descriptions of both emergent and early expanded operational concepts for Urban Air Mobility that NASA is developing. The scope of this work is defined in terms of missions, aircraft, airspace, and hazards. Past and current Urban Air Mobility operations are also reviewed, and the considerations for the data exchange architecture and communication, navigation, and surveillance requirements are also discussed. This paper will serve as a starting point to develop a framework for NASA's Urban Air Mobility airspace integration research and development efforts with partners and stakeholders that could include fast-time simulations, human-in-the-loop simulations, and flight demonstrations.A https://ntrs.nasa.gov/search.jsp?R=20180005218 2020-07-07T23:11:55+00:00Z of ODM that is focused on air traffic operations in metropolitan areas with aircraft capable of seating a small number of passengers or equivalent volume of goods flying trips of about 100 nautical miles (nmi) or less.The technologies and procedures required for ODM were investigated in a NASA study [14] that covered the range of the airspace integration problem, including mission planning, separation from hazards (e.g., terrain, obstacles, other aircraft), contingency management, demand-capacity balancing, traffic flow management, as well as sequencing, scheduling, and spacing. A similar spectrum of topics will be covered in this complementary paper on UAM airspace integration. This paper also describes at a high level NASA's initial airspace integration concepts for both emergent and early expanded UAM operations. It also serves as a framework for NASA's UAM airspace integration research and development efforts with partners and stakeholders.The remainder of this paper is organized as follows. Section II reviews past and current UAM operations. Section III presents an overview of UAM, including the goals, principles, barriers, and benefits. This section also discusses the competing considerations that need to be taken into account and balanced for UAM operations, as well as the requirements for communication, navigation, and surveillance. Section IV defines the scope of the concepts with regard to missions, aircraft, airspace, and hazards. Section V describes at a high level NASA's initial airspace integration concepts for both emergent and early expanded UAM operations. Section VI discusses NASA's plan to develop and refi...

Aircraft climb trajectories are difficult to predict, and large errors in these predictions reduce the potential operational benefits of some advanced concepts in the Next Generation Air Transportation System. An algorithm that dynamically adjusts modeled aircraft weights based on observed track data to improve the accuracy of trajectory predictions for climbing flights has been developed. In real-time evaluation with actual Fort Worth Center traffic, the algorithm decreased the altitude root-mean-square error by about 20%. It also reduced the root-mean-square error of predicted time at top of climb by the same amount.

Aircraft climb trajectories are difficult to predict, and large errors in these predictions reduce the potential operational benefits of some advanced features for NextGen. The algorithm described in this paper improves climb trajectory prediction accuracy by adjusting trajectory predictions based on observed track data. It utilizes rate-of-climb and airspeed measurements derived from position data to dynamically adjust the aircraft weight modeled for trajectory predictions. In simulations with weight uncertainty, the algorithm is able to adapt to within 3 percent of the actual gross weight within two minutes of the initial adaptation. The root-mean-square of altitude errors for five-minute predictions was reduced by 73 percent. Conflict detection performance also improved, with a 15 percent reduction in missed alerts and a 10 percent reduction in false alerts. In a simulation with climb speed capture intent and weight uncertainty, the algorithm improved climb trajectory prediction accuracy by up to 30 percent and conflict detection performance, reducing missed and false alerts by up to 10 percent.

Safe separation of aircraft is a primary objective of all air traffic control systems. A probabilistic risk analysis of a next-generation air traffic control operational concept was conducted using Monte Carlo simulation. Results indicate that this safety-critical system would have a mean time between mid-air collisions of about 281 years in enroute airspace, which is around the desired target for an automated separation assurance function. Among the simulation trials that ended with mid-air collision, most involved failure to locate and track aircraft through radar or satellite positioning systems with the remainder due to conflict detection failure.

Climb prediction uncertainty is a major source of error in trajectory-based automation for air traffic management. In this study, the performance of a trajectory-based automated separation assurance system is analyzed under different levels of uncertainty in laboratory simulations to investigate its robustness to climb uncertainty. Results indicate that this fully automated system can successfully detect and resolve 99% of conflicts in the high-altitude sectors of Fort Worth Center during 3-20 minutes prior to first loss of separation under near-zero uncertainty. Trajectory uncertainty was then incorporated into the simulation in the form of weight uncertainty. System performance remained unchanged for these scenarios when weight uncertainties ranged ± 10%. However, performance declined to 87% when this range was expanded to ± 20%.