Conference proceeding
SkyGrid: Energy-Flow Optimization at Harmonized Aerial Intersections
IEEE Vehicular Technology Conference, pp.1-7
10/07/2024
DOI: 10.1109/VTC2024-Fall63153.2024.10757812
Abstract
The rapid evolution of urban air mobility (UAM) is reshaping the future of transportation by integrating aerial vehicles into urban transit systems. The design of aerial intersections plays a critical role in the phased development of UAM systems to ensure safe and efficient operations in air corridors. This work adapts the concept of rhythmic control of connected and automated vehicles (CAVs) at unsignalized intersections to address complex traffic control problems. This control framework assigns UAM vehicles to different movement groups and significantly reduces the computation of routing strategies to avoid conflicts. In contrast to ground traffic, the objective is to balance three measures: minimizing energy utilization, maximizing intersection flow (throughput), and maintaining safety distances. This optimization method dynamically directs traffic with various demands, considering path assignment distributions and segment-level trajectory coefficients for straight and curved paths as control variables. To the best of our knowledge, this is the first multi-objective optimization approach for unsignalized aerial intersection control using rhythmic control. A sensitivity analysis with respect to inter-platoon safety and straight/left demand balance demonstrates the effectiveness of our method in handling traffic under various scenarios.
Details
- Title: Subtitle
- SkyGrid: Energy-Flow Optimization at Harmonized Aerial Intersections
- Creators
- Sahand KhoshdelFatemeh AfghahQi Luo
- Resource Type
- Conference proceeding
- Publication Details
- IEEE Vehicular Technology Conference, pp.1-7
- DOI
- 10.1109/VTC2024-Fall63153.2024.10757812
- eISSN
- 2577-2465
- Publisher
- IEEE
- Grant note
- National Science Foundation (10.13039/100000001)
- Language
- English
- Date published
- 10/07/2024
- Academic Unit
- Business Analytics
- Record Identifier
- 9984757066202771
Metrics
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