Abstract: Siting low-altitude takeoff and landing platforms (vertiports) is a fundamental challenge for developing urban air mobility (UAM). This study formulates this issue as a variant of the capacitated facility location problem, incorporating flight range and service capacity constraints, and proposes SPID, a deep reinforcement learning (DRL)-based solution framework that models the problem as a Markov decision process. To handle dynamic coverage, the designed DRL framework-based SPID uses a multi-head attention mechanism to capture spatiotemporal patterns, followed by integrating dynamic and static information into a unified input state vector. Afterward, a gated recurrent unit (GRU) is used to generate the query vector, thereby enhancing sequential decision-making. The action network within the DRL network is regulated by a loss function that integrates service distance costs with unmet demand penalties, enabling end-to-end optimization. Subsequent experimental results demonstrate that SPID significantly enhances solution efficiency and robustness compared with traditional methods under flight and capacity constraints. Especially, across the social performance metrics emphasized in this study, SPID outperforms the suboptimal solutions produced by traditional clustering and graph neural network (GNN)-based methods by up to approximately 29%. This improvement comes with an increase in distance-based cost that is kept within 10%. Overall, we demonstrate an efficient, scalable approach for vertiport siting, supporting rapid decision-making in large-scale UAM scenarios.

Key words: Low-altitude planning; Vertiport siting; Deep reinforcement learning; Algorithm exploration

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