No more flying blind: Leveraging weather forecasting for clear-cut risk-based decisions
Unmanned aircraft systems (UASs) have experienced a notable surge in applications, particularly with the increasing deployment of vertical take-off and landing (VTOL) vehicles in urban environments, which are more flexible in comparison to traditional aircraft. Nevertheless, the advantages of using...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Transportation Research Interdisciplinary Perspectives |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590198225000284 |
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| author | Manuel Lombardi David Sladek Francesco Simone Riccardo Patriarca |
| author_facet | Manuel Lombardi David Sladek Francesco Simone Riccardo Patriarca |
| author_sort | Manuel Lombardi |
| collection | DOAJ |
| description | Unmanned aircraft systems (UASs) have experienced a notable surge in applications, particularly with the increasing deployment of vertical take-off and landing (VTOL) vehicles in urban environments, which are more flexible in comparison to traditional aircraft. Nevertheless, the advantages of using VTOLs come with an increase in operational risks, too. Although there are approaches to support the fulfillment of safety objectives for VTOL operations, none of them specifically consider the type of weather information needed to guide decision-making successfully. Having detailed weather forecasts within operational areas can help avoid unwanted outcomes while assuring safe operations and mission success. On this basis, this paper proposes an innovative methodology to support decision-making in VTOLs missions, emphasizing the importance of weather forecasting practices. The decision support methodology presented in this study involves four phases, which consider different timespans (i.e., from more than two weeks before up to two hours before the mission), eventually assessing dedicated feasibility indexes. A case study is proposed to show how the methodology could be implemented into a decision support system with the objective of guiding VTOL decision makers in identifying the most suitable vehicle to ensure successful operations in various contexts from innovative air mobility solutions towards industrial inspection practices. |
| format | Article |
| id | doaj-art-2719eb24c3a142ccb7fccb537bc54385 |
| institution | Kabale University |
| issn | 2590-1982 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Transportation Research Interdisciplinary Perspectives |
| spelling | doaj-art-2719eb24c3a142ccb7fccb537bc543852025-02-07T04:48:16ZengElsevierTransportation Research Interdisciplinary Perspectives2590-19822025-03-0130101349No more flying blind: Leveraging weather forecasting for clear-cut risk-based decisionsManuel Lombardi0David Sladek1Francesco Simone2Riccardo Patriarca3Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana, 18 00184 Rome, Italy; Corresponding author.Department of Military Geography and Meteorology, Faculty of Military Technology, University of Defence, Brno 662 10 Czech RepublicDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana, 18 00184 Rome, ItalyDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana, 18 00184 Rome, ItalyUnmanned aircraft systems (UASs) have experienced a notable surge in applications, particularly with the increasing deployment of vertical take-off and landing (VTOL) vehicles in urban environments, which are more flexible in comparison to traditional aircraft. Nevertheless, the advantages of using VTOLs come with an increase in operational risks, too. Although there are approaches to support the fulfillment of safety objectives for VTOL operations, none of them specifically consider the type of weather information needed to guide decision-making successfully. Having detailed weather forecasts within operational areas can help avoid unwanted outcomes while assuring safe operations and mission success. On this basis, this paper proposes an innovative methodology to support decision-making in VTOLs missions, emphasizing the importance of weather forecasting practices. The decision support methodology presented in this study involves four phases, which consider different timespans (i.e., from more than two weeks before up to two hours before the mission), eventually assessing dedicated feasibility indexes. A case study is proposed to show how the methodology could be implemented into a decision support system with the objective of guiding VTOL decision makers in identifying the most suitable vehicle to ensure successful operations in various contexts from innovative air mobility solutions towards industrial inspection practices.http://www.sciencedirect.com/science/article/pii/S2590198225000284Unmanned operationsPerformance indicatorsRisk managementOperations managementMachine learning |
| spellingShingle | Manuel Lombardi David Sladek Francesco Simone Riccardo Patriarca No more flying blind: Leveraging weather forecasting for clear-cut risk-based decisions Transportation Research Interdisciplinary Perspectives Unmanned operations Performance indicators Risk management Operations management Machine learning |
| title | No more flying blind: Leveraging weather forecasting for clear-cut risk-based decisions |
| title_full | No more flying blind: Leveraging weather forecasting for clear-cut risk-based decisions |
| title_fullStr | No more flying blind: Leveraging weather forecasting for clear-cut risk-based decisions |
| title_full_unstemmed | No more flying blind: Leveraging weather forecasting for clear-cut risk-based decisions |
| title_short | No more flying blind: Leveraging weather forecasting for clear-cut risk-based decisions |
| title_sort | no more flying blind leveraging weather forecasting for clear cut risk based decisions |
| topic | Unmanned operations Performance indicators Risk management Operations management Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2590198225000284 |
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