JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

Accepted manuscript available online (unedited version)

SPID: Solving the low-altitude takeoff and landing facility location problem via deep reinforcement learning

Author(s): Xiaocheng LIU, Meilong LE, Yupu LIU?, Minghua HU
Affiliation(s): College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing China, 211106
Corresponding email(s): lxc2307084@nuaa.edu.cn, lemeilong@126.com, liuyupu@nuaa.edu.cn, minghuahu@nuaa.edu.cn
Key Words: Low-altitude planning; Facility site selection; Deep reinforcement learning; Algorithm exploration

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Xiaocheng LIU, Meilong LE, Yupu LIU?, Minghua HU. SPID: Solving the low-altitude takeoff and landing facility location problem via deep reinforcement learning[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2500534

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author="Xiaocheng LIU, Meilong LE, Yupu LIU?, Minghua HU",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2500534"
}

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%T SPID: Solving the low-altitude takeoff and landing facility location problem via deep reinforcement learning
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%A Meilong LE
%A Yupu LIU?
%A Minghua HU
%J Frontiers of Information Technology & Electronic Engineering
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%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/FITEE.2500534"

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T1 - SPID: Solving the low-altitude takeoff and landing facility location problem via deep reinforcement learning
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A1 - Meilong LE
A1 - Yupu LIU?
A1 - Minghua HU
J0 - Frontiers of Information Technology & Electronic Engineering
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Y1 - in press
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doi="https://doi.org/10.1631/FITEE.2500534"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: The siting of low-altitude takeoff and landing platforms is a fundamental challenge for the future development of Urban Air Mobility (UAM). We formulate the problem as a variant of capacitated facility location problem (CFLP) with flight-range and service-capacity constraints, and propose SPID, a Deep Reinforcement Learning (DRL)-based solution framework. The problem is modeled as a Markov Decision Process (MDP). To address dynamic coverage, we design a DRL framework with a multi-head attention mechanism to capture spatiotemporal patterns and integrate dynamic and static information into a unified input state vector. A Gated Recurrent Unit (GRU) is used to generate the query vector, enhancing sequential decision-making. The action network is guided by a loss function combining unmet demand penalties with service-distance costs, enabling end-to-end optimization. Experimental results demonstrate that SPID significantly improves solution efficiency and robustness over traditional methods under flight and capacity constraints. It also outperforms heuristic algorithms in accuracy, with a performance gap of around 6% compared to exact solutions. This work provides an efficient, scalable approach to low-altitude site selection, supporting rapid decision-making in large-scale UAM scenarios.

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