Social-psychological determinants and nonlinear thresholds: behavioral insights into urban air mobility adoption as an airport shuttle

Kaihan Zhang, Xiang Liu, Qinyu Cui, Xing Gao, Mengqiu Cao and Inhi Kim

Transportation Research Part A: Policy and Practice, 2026, vol. 205, issue C

Abstract: Urban Air Mobility (UAM) is an emerging mobility service increasingly proposed by cities worldwide. Among its various applications, UAM as an airport shuttle offers particularly strong early-stage commercial potential. However, understanding of the key factors influencing the adoption of UAM as an airport shuttle service remains limited, particularly regarding the role of social-psychological factors and their tolerance thresholds from a nonlinear perspective, where critical points in factors such as time or cost may shift the decision from declination to acceptance. Using a stated-preference survey of 1250 respondents from South Korea, this study identifies the primary determinants of UAM adoption and examines their decision thresholds using a newly proposed hybrid approach that combines automated machine learning (AutoML) and statistical models in a complementary manner. The results show thatt: (1) Previously overlooked social psychological factors, such as individuals seeking time savings, environmental benefits, and openness to new technologies, play adominant role, accounting for 55.4 % of explanatory power in predicting adoption decisions. (2) Threshold effects emerge in airport trip chains, with first-mile and in-vehicle durations under 15 min or over one hour marking critical adoption points; and (3) UAM holds strong substitute potential for car use for long-distance airport access. These findings provide actionable insights for policymakers and service providers aiming to promote UAM adoption, emphasizing the need to align service design and marketing strategies with users’ psychological motivations and to improve access environments for UAM connectivity within urban areas.

Keywords: Urban air mobility; Airport shuttle; Stated preference survey; Social psychology; Machine learning; Nonlinear relationship (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1016/j.tra.2025.104856

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