1596. A Data-Driven Bi-Objective Optimization Framework for Vertiport Placement in Urban Air Mobility
Invited abstract in session MC-57: Mathematical Models and UAV Applications, stream Transportation.
Monday, 12:30-14:00Room: Liberty 1.12
Authors (first author is the speaker)
| 1. | Zhirong Zhang
|
| 2. | Yong Fang
|
| 3. | Qixiu Cheng
|
| University of Bristol Business School, University of Bristol |
Abstract
Strategic vertiport placement is critical to the successful implementation of Urban Air Mobility (UAM), influencing network efficiency and accessibility. However, existing approaches predominantly rely on demand models with static assumptions, limiting applicability to dynamic urban scenarios. To address this, we propose a data-driven optimization framework that leverages ground transportation data to optimize UAM vertiport placement. The model integrates key constraints, including eVTOL cruise speeds, charging limitations, vertiport capacity, and inter-vertiport distance, ensuring alignment with urban mobility patterns and infrastructure feasibility. Candidate locations are first generated using a weighted k-means clustering approach to capture spatial demand patterns. A bi-objective optimization model then derives Pareto-optimal solutions balancing total cost and service coverage. To enhance adaptability, a dynamic resource allocation mechanism integrates real-time eVTOL fleet states into the optimization loop, enabling adaptive vertiport capacity allocation during peak demand periods. Additionally, a threshold-driven iterative strategy refines candidate configurations based on historical performance, improving solution quality and computational efficiency. A case study in San Francisco validates the model’s effectiveness in optimizing vertiport placement, achieving a balance between cost efficiency and service coverage.
Keywords
- Transportation
Status: accepted
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