Data‐Driven Vertiport Site Selection: A Two‐Stage Framework With Multi‐Source Urban Data and Scalable Optimization

Urban Air Mobility (UAM) is increasingly recognized as a promising solution to alleviate urban congestion and improve transportation efficiency, playing a key role in developing sustainable urban infrastructure. A central challenge for UAM deployment is the siting of vertiports, requiring the balancing of various urban factors such as demand, traffic, housing prices, and noise. Existing methods rely on heuristic or simplified optimization and are tailored to individual cities, resulting in poor transferability and limited scalability. This paper introduces a novel two‐stage framework for UAM vertiport location selection. In the first stage, we develop DRAG‐Net, a Dual‐stream Recency‐biased Attention Graph Network, to learn transferable candidate‐site representations from multi‐source urban data and identify potential vertiport locations. In the second stage, we apply the non‐dominated sorting genetic Algorithm II (NSGA‐II) to generate Pareto‐optimal layouts from these candidate sites, maximizing demand coverage while minimizing noise. To evaluate effectiveness and generalization, we construct comprehensive datasets for Beijing and Shanghai that integrate geographical, socio‐economic, and temporal features. The results show that the model trained in Beijing and effectively transferred to Shanghai preserves the learned spatial structure and achieves an effective multi‐objective trade‐off between demand and noise, thus supporting its cross‐city generalizability. Furthermore, we enhance interpretability by employing SHAP analysis, which provides model‐agnostic explanations and transparent insights into the decision process. Overall, this study establishes a scalable and generalizable framework for UAM infrastructure planning by combining predictive modelling with multi‐objective optimization and interpretable analysis.