The Maximum Coverage Model and Recommendation System for UAV Vertiports Location Planning

2025-08-18

AI Summary

Urban Air Mobility (UAM) vertiport network planning faces challenges in jointly modeling spatiotemporal demand dynamics, heterogeneous user behavior, and infrastructure capacity constraints. Method: This paper proposes the Capacitated Dynamic Maximum Covering Location Problem (CDMCLP)—the first optimization model integrating dynamic spatiotemporal coverage, identification of heterogeneous travel behaviors, and capacity-aware facility location—complemented by an integrated recommendation system embedding socioeconomic factors, enabling dynamic clustering initialization and adaptive parameter tuning. Contribution/Results: Empirical evaluation in a major Chinese metropolis demonstrates that the proposed approach improves coverage efficiency by 38%–52% over conventional methods, significantly enhancing practicality, scalability, and decision-support capability for large-scale UAM infrastructure planning.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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 solutions. Validation in a Chinese center city demonstrates the effectiveness of the new optimization framework and recommendation system. Under the evaluation and optimization of CDMCLP, the quantitative performance of traditional location methods are exposed and can be improved by 38%--52%, while the recommendation system shows user-friendliness and the effective integration of complex elements. By integrating mathematical rigor with practical implementation considerations, this hybrid approach bridges the gap between theoretical location modeling and real-world UAM infrastructure planning, offering municipalities a pragmatic tool for vertiport network design.

Problem

• Optimizing vertiport locations for urban aerial mobility networks
• Integrating spatial-temporal demand and capacity constraints in planning
• Developing a recommendation system for practical vertiport network design

Innovation

• Capacitated Dynamic Maximum Covering Location Problem framework
• Integrated Planning Recommendation System with socio-economic factors
• Adaptive parameter tuning based on empirical user behavior