An Initial Assessment of the Potential Weather Barriers of Urban Air Mobility

• Published in: IEEE transactions on intelligent transportation systems Vol. 22; no. 9; pp. 6018 - 6027
• Main Authors: Reiche, Colleen, Cohen, Adam P., Fernando, Chris
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Advanced air mobility (AAM); Aeronautics; Air transportation; Aircraft; Artificial intelligence; barriers; Climate; Climatology; Emergency management; eVTOL; Light aircraft; Machine learning; Meteorology; NASA; on-demand air mobility; perception; rural air mobility; societal; Sociology; Statistics; Unmanned aircraft; Urban air mobility; urban air mobility (UAM); Urban areas; vertical take-off and land (VTOL); Vertical takeoff aircraft; Weather; Wind
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2020.3048364

Urban Air Mobility (UAM), a subset of advanced air mobility, is a concept that envisions safe, sustainable, affordable, and accessible air transportation for passenger mobility, cargo delivery, and emergency management within or traversing a metropolitan area. In recent years, several companies have designed and tested enabling elements of this concept, including; prototypes of vertical take-off and landing (VTOL) aircraft, operational concepts, and market studies to understand potential business models. While UAM may be enabled by the convergence of several factors, a number of barriers such as weather could present challenges to scaling operations. This research discusses the potential weather and public acceptance challenges for operations in adverse conditions. This article presents a comprehensive seasonal and diurnal climatology analysis using historical observations across anticipated operational altitudes (surface- 5000 ft AGL) at ten metropolitan areas across the United States for the NASA Aeronautics Research Mission Directorate (ARMD). Public perceptions of weather-related societal barriers were evaluated through a five-city general population survey (n = 1,702) where respondents were asked about their views regarding flying in a small aircraft in a variety of adverse weather conditions using a six statement 5-point Likert scale. The results of the climatology analysis found weather most favorable in Los Angeles and San Francisco, with much less favorable conditions in Denver, New York City, and Washington D.C. In the future, equipping automated vehicles, unmanned aircraft systems, and VTOLs with meteorological sensors coupled with machine learning and artificial intelligence could enhance predictive capabilities that reduce flight cancellations and delays for travelers.