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...
Flights incur a large percentage of their delays on the ground during the departure process between their scheduled departure from the gate and takeoff. Because of the large uncertainties associated with them, these delays are difficult to predict and account for, hindering the ability to effectively manage the Air Traffic Control (ATC) system. This paper presents an effort to improve the accuracy of estimating the taxi-out time, which is the duration between pushback and takeoff. The method was to identify the main factors that affect the taxi-out time and build an estimation model that takes the most important ones into account. An analysis conducted at Boston Logan International Airport identified the runway configuration, the airline/terminal, the downstream restrictions and the takeoff queue size as the main causal factors that affect the taxiout time. Of these factors the takeoff queue size was the most important one, where the queue size that an aircraft experienced was measured as the number of takeoffs that took place between its pushback time and its takeoff time. Consequently, a queuing model was built to estimate the taxi-out time at Logan Airport based on queue size estimation. For each aircraft, the queuing model assumes knowledge of the number of departure aircraft present on the airport surface at its pushback time and estimates its takeoff queue size by predicting the amount of passing that it may experience on the airport surface during its taxi out. The prediction performance of the queuing model was compared at Logan Airport to a running average model, which represents the baseline used currently in the Enhanced Traffic Management System (ETMS). The running average model uses a fourteen-day average as the estimate of the taxi-out time. The queuing model improved the mean absolute error in the taxi-out time estimation by approximately twenty percent and the accuracy rate by approximately ten percent, over the fourteen-day running average model. List of symbols and acronyms t offTakeoff time t out Pushback time T Taxi-out time (t off -t out ) N Number of departure aircraft present on the airport surface at the pushback time of a particular aircraft Q Takeoff queue experienced by an aircraft N P
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