The Maximum Coverage Model and Recommendation System for UAV Vertiports Location Planning
Chunliang Hua, Xiao Hu, Jiayang Sun, Zeyuan Yang

TL;DR
This paper introduces a novel optimization framework and recommendation system for planning UAV vertiport locations, effectively addressing urban demand, capacity, and socio-economic factors, validated through a case study in China.
Contribution
It proposes the Capacitated Dynamic Maximum Covering Location Problem (CDMCLP) and an integrated planning system that combines data-driven modeling with socio-economic considerations for UAM infrastructure.
Findings
Traditional location methods improve by 38-52% with CDMCLP.
The recommendation system demonstrates user-friendliness and practical applicability.
Validation in a Chinese city confirms the effectiveness of the proposed approach.
Abstract
As urban aerial mobility (UAM) infrastructure development accelerates globally, cities like Shenzhen are planning large-scale vertiport networks (e.g., 1,200+ facilities by 2026). Existing planning frameworks remain inadequate for this complexity due to historical limitations in data granularity and real-world applicability. This paper addresses these gaps by first proposing the Capacitated Dynamic Maximum Covering Location Problem (CDMCLP), a novel optimization framework that simultaneously models urban-scale spatial-temporal demand, heterogeneous user behaviors, and infrastructure capacity constraints. Building on this foundation, we introduce an Integrated Planning Recommendation System that combines CDMCLP with socio-economic factors and dynamic clustering initialization. This system leverages adaptive parameter tuning based on empirical user behavior to generate practical planning…
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Taxonomy
TopicsAdvanced Manufacturing and Logistics Optimization · Robotic Path Planning Algorithms
