In this paper, we discuss the Federal Aviation Administration's (FAA's) procedural and regulatory practices that present challenges to the introduction of on-demand aviation (ODA) and pay particular attention to the impacts of increasingly autonomous piloting practices being pursued under ODA. The concept of ODA discussed in this research is that of a small electric or hybrid-electric air vehicle, with propeller-based propulsion, resulting in a low-greenhouse-gas-emissions footprint and substantially reduced pilot requirements through the use of automation. We outline the structure of the Federal Aviation Regulations, subchapters and parts, examining each subchapter in turn for areas of applicability for ODA certification, both pilot and vehicle. Our examination of reduced pilot roles makes use of the simplified vehicle operations levels that proceed from human pilot roles to full autonomy, as described in NASA research. We discuss risk-assessment options and suggest research to support aircraft and operator certification.
This paper presents a search algorithm based framework to calibrate origin-destination (O-D) market specific airline ticket demands and prices for the Air Transportation System (ATS). This framework is used for calibrating an agent based model of the air ticket buy-sell process -Airline Evolutionary Simulation (Airline EVOS) -that has fidelity of detail that accounts for airline and consumer behaviors and the interdependencies they share between themselves and the NAS. More specificially, this algorithm simultaneous calibrates demand and airfares for each O-D market, to within specified threshold of a pre-specified target value. The proposed algorithm is illustrated with market data targets provided by the Transportation System Analysis Model (TSAM) and Airline Origin and Destination Survey (DB1B). Although we specify these models and datasources for this calibration exercise, the methods described in this paper are applicable to calibrating any low-level model of the ATS to some other demand forecast model-based data. We argue that using a calibration algorithm such as the one we present here to synchronize ATS models with specialized forecast demand models, is a powerful tool for establishing credible baseline conditions in experiments analyzing the effects of proposed policy changes to the ATS.
Nomenclature α= price elasticity of demand λ = multiplier for airfare of non-stop itineraries A = advertised airfare B = basefare d = number of days to travel date l = load-factor (ratio of sold to total number of available seats)
As part of ongoing research, the National Aeronautics and Space Administration (NASA) and LMI developed a research framework to assist policymakers in identifying impacts on the U.S.
air transportation system (ATS) of potential policies and technology related to the implementation of the Next Generation Air Transportation System (NextGen). This framework, called the Air Transportation System Evolutionary Simulation (ATS-EVOS), integrates multiple models into a single process flow to best simulate responses by U.S. commercial airlines and other ATS stakeholders to NextGen-related policies, and in turn, how those responses impact the ATS. Development of this framework required NASA and LMI to create an agent-based model of airline and passenger behavior. This Airline Evolutionary Simulation (AIRLINE-EVOS) models airline decisions about tactical airfare and schedule adjustments, and strategic decisions related to fleet assignments, market prices, and equipage. AIRLINE-EVOS models its own heterogeneous population of passenger agents that interact with airlines; this interaction allows the model to simulate the cycle of action-reaction as airlines compete with each other and engage passengers. We validated a baseline configuration of AIRLINE-EVOS against Airline Origin andDestination Survey (DB1B) data and subject matter expert opinion, and we verified the ATS-EVOS framework and agent behavior logic through scenario-based experiments. These experiments demonstrated AIRLINE-EVOS's capabilities in responding to an input price shock in fuel prices, and to equipage challenges in a series of analyses based on potential incentive policies for best equipped best served, optimal-wind routing, and traffic management initiative exemption concepts.
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