Abstract Urban air mobility (UAM), as an emerging transportation mode, represents an effective strategy to alleviate ground traffic congestion and is expected to be promoted globally in the coming years. This study focuses on the data-driven optimization of vertiport location. By integrating travel choice modeling with a three-phase multi-objective integer programming (TP-MOIP) model based on K -means clustering analysis, the study provides decision-making support for scientific vertiport network planning. The study combines revealed preference (RP) and stated preference (SP) surveys, employing binomial logistic regression to analyze the probability of individuals choosing UAM, while introducing time value coefficients to identify potential user groups. Utilizing ride-hailing order data, potential vertiport locations were identified through K -means clustering. The developed TP-MOIP model simultaneously optimizes the objectives of demand coverage maximization and cost minimization across three implementation phases. Using Shenzhen as a case study, the feasibility of the proposed approach was validated. The results show that the average selection probability for UAM is 16.7%, with time-sensitive users being a critical demographic. In addition, the TP-MOIP model achieved a demand coverage rate of 88.21%. Sensitivity analysis indicates that the land cost budget has the most pronounced impact on demand coverage. This research establishes both theoretical foundations and practical methodologies for vertiport location optimization, offering substantial implications for advancing UAM development.
Zhou et al. (Mon,) studied this question.