Evaluating UAM–Wildlife Collision Prevention Efficacy with Fast-Time Simulations

Urban Air Mobility (UAM) promises to reduce ground traffic and journey times by using electric vertical take-off and landing (eVTOL) aircraft for short, low-altitude flights, especially in urban environments. However, low-flying aircraft are at particularly high risk of collisions with wildlife, such as birds. This study builds on previous research into UAM collision avoidance systems (UAM-CAS) by implementing one such system in the BlueSky open-source air traffic simulator and evaluating its efficacy in reducing bird strikes. Several modifications were made to the original UAM-CAS framework to improve performance. Realistic UAM flight plans were developed and combined with real-world bird movement datasets representing typical birds in sustained flight from all seasons, recorded by an avian radar at Leeuwarden Air Base. Fast-time simulations were conducted in the BlueSky Open Air Traffic Simulator using the UAM flight plan, the bird datasets, and the UAM-CAS algorithm. Results demonstrated that, under modelling assumptions, the UAM-CAS reduced bird strikes by 62%, with an average delay per flight of 15 s, whereas 27% of the remaining strikes occurred with birds outside the system’s design scope. A small number of flights faced substantially longer delays, indicating some operational impacts. Based on the findings, specific avenues for future research to improve UAM-CAS performance are suggested.

Institutions

Publication Details

Journal
Aerospace
Published
2025-12-25
DOI
https://doi.org/10.3390/aerospace13010018
Primary Topic
UAV Applications and Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluating UAM–Wildlife Collision Prevention Efficacy with Fast-Time Simulations

Aerospace
UAV Applications and Optimization
article

Evaluating UAM–Wildlife Collision Prevention Efficacy with Fast-Time Simulations

article en

Abstract

Urban Air Mobility (UAM) promises to reduce ground traffic and journey times by using electric vertical take-off and landing (eVTOL) aircraft for short, low-altitude flights, especially in urban environments. However, low-flying aircraft are at particularly high risk of collisions with wildlife, such as birds. This study builds on previous research into UAM collision avoidance systems (UAM-CAS) by implementing one such system in the BlueSky open-source air traffic simulator and evaluating its efficacy in reducing bird strikes. Several modifications were made to the original UAM-CAS framework to improve performance. Realistic UAM flight plans were developed and combined with real-world bird movement datasets representing typical birds in sustained flight from all seasons, recorded by an avian radar at Leeuwarden Air Base. Fast-time simulations were conducted in the BlueSky Open Air Traffic Simulator using the UAM flight plan, the bird datasets, and the UAM-CAS algorithm. Results demonstrated that, under modelling assumptions, the UAM-CAS reduced bird strikes by 62%, with an average delay per flight of 15 s, whereas 27% of the remaining strikes occurred with birds outside the system’s design scope. A small number of flights faced substantially longer delays, indicating some operational impacts. Based on the findings, specific avenues for future research to improve UAM-CAS performance are suggested.

AerospaceVol. 13(1)
Vaughn College of Aeronautics and Technology (US), Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE), Imperial College London (GB)
Sustainable cities and communities
Openalex Percentile: Top 22%
UAV Applications and Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.