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<p>Urban air mobility (UAM) promises to transform metropolitan transportation by enabling rapid, flexible, and terrain-independent travel. However, realizing its benefits requires a robust framework for siting vertiports. Existing studies often overlook UAM’s unique network connection and under-integrating critical negative externalities such as noise exposure. Thus, this study proposes a comprehensive, multi-objective optimization framework for vertiport location planning, explicitly integrating four objectives: maximizing service coverage, maximizing network connectivity, minimizing noise exposure, and minimizing siting cost. Leveraging high-resolution spatial and mobility datasets for Shenzhen, China, we apply evolutionary framework to generate and analyze the full Pareto front of optimal deployment strategies. The results demonstrate that optimized site selection outperforms random baselines by over 35%–66% in service coverage and connectivity, while reducing both noise exposure and cost. Cluster analysis of Pareto-optimal solutions reveals distinct typologies of deployment strategies, enabling policymakers to align UAM planning with diverse urban priorities. Moreover, the identification of “core” sites provides actionable guidance for phased, robust implementation.</p>

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Designing the future urban sky: a multi-objective framework for optimizing urban vertiport locations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月15日

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  • Si Qiao,
  • Jinfeng Xie,
  • Anthony Gar-On Yeh

摘要

Urban air mobility (UAM) promises to transform metropolitan transportation by enabling rapid, flexible, and terrain-independent travel. However, realizing its benefits requires a robust framework for siting vertiports. Existing studies often overlook UAM’s unique network connection and under-integrating critical negative externalities such as noise exposure. Thus, this study proposes a comprehensive, multi-objective optimization framework for vertiport location planning, explicitly integrating four objectives: maximizing service coverage, maximizing network connectivity, minimizing noise exposure, and minimizing siting cost. Leveraging high-resolution spatial and mobility datasets for Shenzhen, China, we apply evolutionary framework to generate and analyze the full Pareto front of optimal deployment strategies. The results demonstrate that optimized site selection outperforms random baselines by over 35%–66% in service coverage and connectivity, while reducing both noise exposure and cost. Cluster analysis of Pareto-optimal solutions reveals distinct typologies of deployment strategies, enabling policymakers to align UAM planning with diverse urban priorities. Moreover, the identification of “core” sites provides actionable guidance for phased, robust implementation.