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Advanced Air Mobility (AAM) using electrical vertical take-off and landing (eVTOL) aircraft is an emerging way of air transportation within metropolitan areas. A key challenge for the success of AAM is how to manage large-scale flight operations with safety guarantees in high-density, dynamic, and uncertain airspace environments in real time. To address these challenges, we introduce the concept of a data-driven probabilistic geofence, which can guarantee that the probability of potential conflicts between eVTOL aircraft is bounded under data-driven uncertainties. To evaluate the probabilistic geofences online, Kernel Density Estimation (KDE) based on Fast Fourier Transform (FFT) is customized to model data-driven uncertainties. Based on the FFT-KDE values from data-driven uncertainties, we introduce an optimization framework of Integer Linear Programming (ILP) to find a parallelogram box to approximate the data-driven probabilistic geofence. To overcome the computational burden of ILP, an efficient heuristic algorithm is further developed. Numerical results demonstrate the feasibility and efficiency of the proposed algorithms.
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This content will become publicly available on December 1, 2026
Advanced Air Mobility for commuting? An exploration of economic, energy, and environmental feasibility
Advanced Air Mobility (AAM) presents an emerging alternative to traditional car driving for commuting in metropolitan areas. However, its feasibility has not been thoroughly studied nor well understood at the operational level. Given that AAM has not been in place, this study explores the economic, energy, and environmental feasibility of AAM for commuting at an early stage of AAM deployment. We propose a time expanded network model to characterize the dynamics of eVTOL operations between a vertiport pair in different states: in-service flying, relocation flying, charging, and parking, while respecting various operational and commuter time window constraints. By jointly considering eVTOL flying with vertiport access and egress and using real-world data, we demonstrate an application of the model in the Chicago metropolitan area in the US. Different vertiport pairs and eVTOL aircraft models are investigated. We find substantial travel time saving if commuting by AAM. While vehicle operating cost will be higher using eVTOLs than using auto, the generalized travel cost will be less for commuters. On the other hand, with current eVTOL power requirement, the energy consumption and CO2 emissions of AAM will be greater than those of auto driving, with an important contributor being the significance presence of empty flights relocation. These findings, along with sensitivity analysis, shed light on future eVTOL development to enhance the competitiveness of AAM as a viable option for commuting.
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- Award ID(s):
- 2112650
- PAR ID:
- 10591471
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Transport Economics and Management
- Volume:
- 3
- Issue:
- C
- ISSN:
- 2949-8996
- Page Range / eLocation ID:
- 135 to 152
- Subject(s) / Keyword(s):
- Advanced Air Mobility (AAM) Electric vertical takeoff and landing aircraft (eVTOL) Commuting Time-expanded network model Economic feasibility Energy and environmental feasibility
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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