Population density estimation for dynamic ground risk assessment of drone operations
Unmanned Aircraft Systems (UAS), also known as drones, have promising potential for integration into intelligent transportation systems to facilitate enhanced cargo and passenger flow. The escalating prevalence of UAS operations raises concerns regarding third-party ground risks, particularly within...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
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2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/170158 |
| _version_ | 1826121547838914560 |
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| author | Pang, Bizhao Hu, Xinting Poh, Yi Yang Low, Kin Huat |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Pang, Bizhao Hu, Xinting Poh, Yi Yang Low, Kin Huat |
| author_sort | Pang, Bizhao |
| collection | NTU |
| description | Unmanned Aircraft Systems (UAS), also known as drones, have promising potential for integration into intelligent transportation systems to facilitate enhanced cargo and passenger flow. The escalating prevalence of UAS operations raises concerns regarding third-party ground risks, particularly within densely populated urban landscapes. Current mitigation strategies propose avoiding areas of high population density; however, these methodologies predominantly operate under the assumption that population density within specific locations remains static, which overlooks critical spatial-temporal population dynamics. This study introduces a prediction method with random forest regression to enhance the accuracy of population density estimations integral to risk assessment models. Obtained results were integrated into a gravity model to diffuse the predicted population volume at various time intervals, thereby generating dynamic risk maps. Finally, a graphic user interface is developed with backend computations to facilitate the decision makings for: 1) regulatory bodies to determine if a flight plan can be approved based on its risk cost and the target level of safety, and 2) UAS operators (e.g., drone delivery companies) to evaluate the overall risk level of their fleet by compute the risk cost of each flight plan. This work also contributes to the safety risk management of urban air mobility (UAM) in time-dependent ground risk mitigation. |
| first_indexed | 2024-10-01T05:34:01Z |
| format | Conference Paper |
| id | ntu-10356/170158 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:34:01Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1701582023-11-14T15:30:54Z Population density estimation for dynamic ground risk assessment of drone operations Pang, Bizhao Hu, Xinting Poh, Yi Yang Low, Kin Huat School of Mechanical and Aerospace Engineering 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC) Air Traffic Management Research Institute Engineering::Aeronautical engineering::Aviation Air Traffic Management Urban Air Mobility Third Party Risk Unmanned Aircraft Systems (UAS), also known as drones, have promising potential for integration into intelligent transportation systems to facilitate enhanced cargo and passenger flow. The escalating prevalence of UAS operations raises concerns regarding third-party ground risks, particularly within densely populated urban landscapes. Current mitigation strategies propose avoiding areas of high population density; however, these methodologies predominantly operate under the assumption that population density within specific locations remains static, which overlooks critical spatial-temporal population dynamics. This study introduces a prediction method with random forest regression to enhance the accuracy of population density estimations integral to risk assessment models. Obtained results were integrated into a gravity model to diffuse the predicted population volume at various time intervals, thereby generating dynamic risk maps. Finally, a graphic user interface is developed with backend computations to facilitate the decision makings for: 1) regulatory bodies to determine if a flight plan can be approved based on its risk cost and the target level of safety, and 2) UAS operators (e.g., drone delivery companies) to evaluate the overall risk level of their fleet by compute the risk cost of each flight plan. This work also contributes to the safety risk management of urban air mobility (UAM) in time-dependent ground risk mitigation. Civil Aviation Authority of Singapore (CAAS) Submitted/Accepted version This research is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme. 2023-11-14T07:08:24Z 2023-11-14T07:08:24Z 2023 Conference Paper Pang, B., Hu, X., Poh, Y. Y. & Low, K. H. (2023). Population density estimation for dynamic ground risk assessment of drone operations. 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC). https://dx.doi.org/10.1109/DASC58513.2023.10311224 979-8-3503-3357-2 2155-7209 https://hdl.handle.net/10356/170158 10.1109/DASC58513.2023.10311224 en © 2023 IEEE. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1109/DASC58513.2023.10311224. application/pdf |
| spellingShingle | Engineering::Aeronautical engineering::Aviation Air Traffic Management Urban Air Mobility Third Party Risk Pang, Bizhao Hu, Xinting Poh, Yi Yang Low, Kin Huat Population density estimation for dynamic ground risk assessment of drone operations |
| title | Population density estimation for dynamic ground risk assessment of drone operations |
| title_full | Population density estimation for dynamic ground risk assessment of drone operations |
| title_fullStr | Population density estimation for dynamic ground risk assessment of drone operations |
| title_full_unstemmed | Population density estimation for dynamic ground risk assessment of drone operations |
| title_short | Population density estimation for dynamic ground risk assessment of drone operations |
| title_sort | population density estimation for dynamic ground risk assessment of drone operations |
| topic | Engineering::Aeronautical engineering::Aviation Air Traffic Management Urban Air Mobility Third Party Risk |
| url | https://hdl.handle.net/10356/170158 |
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