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Urban and regional Air Mobility (URAM) and relocation decisions in the United States: Insights from a machine learning-supported path analysis

Author

Listed:
  • Xu, Ningzhe
  • Pena-Bastidas, Javier
  • Yang, Chenxuan
  • Liu, Jun
  • Hockstad, Trayce
  • Jones, Steven

Abstract

Urban and Regional Air Mobility (URAM) uses electric vertical takeoff and landing (eVTOL) aircraft to offer efficient, sustainable transportation within and between urban and regional areas. While existing studies have primarily focused on public interest and willingness to adopt URAM, its potential implications for residential and workplace relocation decisions remain underexplored. By substantially reducing travel times, URAM may disrupt conventional location constraints for daily commuters. This study surveys over 1000 individuals across the United States to assess perceptions of URAM and its influence on relocation decisions. A combination of path analysis and machine learning techniques—including Naïve Bayes, K-Nearest Neighbors, Random Forest, Support Vector Machine, and Neural Networks—is employed to explore the associations among sociodemographic factors, travel behavior, URAM perceptions, and relocation decisions. Results indicate that higher income and employment in technical occupations are positively associated with URAM interest, while older age, larger household sizes, and carpooling habits are negatively associated. Educational attainment, income, and commuting preferences also shape the extent to which URAM is considered as an alternative to relocation. Path analysis reveals intricate indirect effects, some of which amplify or reverse direct influences on relocation behavior. The insights from this study suggest that, for example, URAM planning should account for access disparities for rural residents and older populations, support mobility for high-tech workers, and anticipate land use changes around future vertiport hubs.

Suggested Citation

  • Xu, Ningzhe & Pena-Bastidas, Javier & Yang, Chenxuan & Liu, Jun & Hockstad, Trayce & Jones, Steven, 2025. "Urban and regional Air Mobility (URAM) and relocation decisions in the United States: Insights from a machine learning-supported path analysis," Transport Policy, Elsevier, vol. 170(C), pages 92-109.
    • Handle: RePEc:eee:trapol:v:170:y:2025:i:c:p:92-109
    DOI: 10.1016/j.tranpol.2025.05.021
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    References listed on IDEAS

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    1. Xu, Ningzhe & Nie, Qifan & Liu, Jun & Jones, Steven, 2024. "Linking short- and long-term impacts of the COVID-19 pandemic on travel behavior and travel preferences in Alabama: A machine learning-supported path analysis," Transport Policy, Elsevier, vol. 151(C), pages 46-62.
    2. Xu, Yiming & Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2021. "Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 170-188.
    3. Janotta, Frederica & Hogreve, Jens, 2024. "Ready for take-off? The dual role of affective and cognitive evaluations in the adoption of Urban Air Mobility services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 185(C).
    4. Zhao, Ying & Feng, Tao, 2025. "Commuter choice of UAM-friendly neighborhoods," Transportation Research Part A: Policy and Practice, Elsevier, vol. 192(C).
    5. Karami, Hossein & Abbasi, Mohammadhossein & Samadzad, Mahdi & Karami, Ali, 2024. "Unraveling behavioral factors influencing the adoption of urban air mobility from the end user's perspective in Tehran – A developing country outlook," Transport Policy, Elsevier, vol. 145(C), pages 74-84.
    6. Anna Straubinger & Erik T. Verhoef & Henri L.F. de Groot, 2021. "Will urban air mobility fly? The efficiency and distributional impacts of UAM in different urban spatial structures," Tinbergen Institute Discussion Papers 21-021/VIII, Tinbergen Institute.
    7. Kshitija Desai & Christelle Al Haddad & Constantinos Antoniou, 2021. "Roadmap to Early Implementation of Passenger Air Mobility: Findings from a Delphi Study," Sustainability, MDPI, vol. 13(19), pages 1-17, September.
    8. Jiang, Yu & Li, Zhichao & Wang, Yasha & Xue, Qingwen, 2025. "Vertiport location for eVTOL considering multidimensional demand of urban air mobility: An application in Beijing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 192(C).
    9. Lin, Tao & Wang, Donggen & Zhou, Meng, 2018. "Residential relocation and changes in travel behavior: what is the role of social context change?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 360-374.
    10. Yang, Linchuan & Ao, Yibin & Ke, Jintao & Lu, Yi & Liang, Yuan, 2021. "To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults," Journal of Transport Geography, Elsevier, vol. 94(C).
    11. Raoul Rothfeld & Mengying Fu & Miloš Balać & Constantinos Antoniou, 2021. "Potential Urban Air Mobility Travel Time Savings: An Exploratory Analysis of Munich, Paris, and San Francisco," Sustainability, MDPI, vol. 13(4), pages 1-20, February.
    12. Lhéritier, Alix & Bocamazo, Michael & Delahaye, Thierry & Acuna-Agost, Rodrigo, 2019. "Airline itinerary choice modeling using machine learning," Journal of choice modelling, Elsevier, vol. 31(C), pages 198-209.
    13. Boddupalli, Sreekar-Shashank & Garrow, Laurie A. & German, Brian J. & Newman, Jeffrey P., 2024. "Mode choice modeling for an electric vertical takeoff and landing (eVTOL) air taxi commuting service," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    14. Morteza Taiebat & Elham Amini & Ming Xu, 2022. "Sharing Behavior in Ride-hailing Trips: A Machine Learning Inference Approach," Papers 2201.12696, arXiv.org.
    15. LeRoy, Stephen F. & Sonstelie, Jon, 1983. "Paradise lost and regained: Transportation innovation, income, and residential location," Journal of Urban Economics, Elsevier, vol. 13(1), pages 67-89, January.
    16. Hu, Songhua & Xiong, Chenfeng & Chen, Peng & Schonfeld, Paul, 2023. "Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    17. Coppola, Pierluigi & De Fabiis, Francesco & Silvestri, Fulvio, 2024. "Urban Air Mobility (UAM): Airport shuttles or city-taxis?," Transport Policy, Elsevier, vol. 150(C), pages 24-34.
    18. Zhang, Xiaojian & Zhou, Zhengze & Xu, Yiming & Zhao, Xilei, 2024. "Analyzing spatial heterogeneity of ridesourcing usage determinants using explainable machine learning," Journal of Transport Geography, Elsevier, vol. 114(C).
    19. Al Haddad, Christelle & Chaniotakis, Emmanouil & Straubinger, Anna & Plötner, Kay & Antoniou, Constantinos, 2020. "Factors affecting the adoption and use of urban air mobility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 696-712.
    20. David Levinson, 2008. "Density and dispersion: the co-development of land use and rail in London," Journal of Economic Geography, Oxford University Press, vol. 8(1), pages 55-77, January.
    21. Cohen, Adam P & Shaheen, Susan A PhD & Farrar, Emily M, 2021. "Urban Air Mobility: History, Ecosystem, Market Potential, and Challenges," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt8nh0s83q, Institute of Transportation Studies, UC Berkeley.
    22. Weijia (Vivian) Li & Kara M. Kockelman, 2022. "How does machine learning compare to conventional econometrics for transport data sets? A test of ML versus MLE," Growth and Change, Wiley Blackwell, vol. 53(1), pages 342-376, March.
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