Optimizing Flight Delay Predictions with Scorecard Systems
Flight delays represent a significant challenge for airlines, airports, and passengers, impacting operational costs and customer satisfaction. Traditional prediction methods often rely on complex statistical analysis and mathematical models that may not be easily implementable. This study proposes s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/11/5918 |
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| author | Ilona Jacyna-Gołda Krzysztof Cur Justyna Tomaszewska Karol Przanowski Sarka Hoskova-Mayerova Szymon Świergolik |
| author_facet | Ilona Jacyna-Gołda Krzysztof Cur Justyna Tomaszewska Karol Przanowski Sarka Hoskova-Mayerova Szymon Świergolik |
| author_sort | Ilona Jacyna-Gołda |
| collection | DOAJ |
| description | Flight delays represent a significant challenge for airlines, airports, and passengers, impacting operational costs and customer satisfaction. Traditional prediction methods often rely on complex statistical analysis and mathematical models that may not be easily implementable. This study proposes scorecards as an innovative and simplified approach to forecast flight delays. Historical flight data from the United States were used, incorporating variables such as departure and arrival times, flight routes, aircraft types, and other factors related to delay. Exploratory data analysis identified key variables influencing delays, and scorecards were constructed by assigning weights, normalizing, and scaling variables to improve interpretability. The model was validated using test datasets, and predictive performance was evaluated by comparing forecast delays with actual results. The results indicate that scorecards provide accurate and interpretable predictions of flight delays. This method facilitates the identification of critical factors that contribute to delays and allows for an estimation of their likelihood and duration. Scorecards offer a practical tool for airlines and airport operators, potentially enhancing decision-making processes, reducing delay-related costs, and improving service quality. Future research should explore the integration of scorecards into operational systems and the inclusion of additional variables to increase model robustness and generalizability. |
| format | Article |
| id | doaj-art-0b2db42085944dfbb3a387e9adb931d1 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-0b2db42085944dfbb3a387e9adb931d12025-08-20T02:32:34ZengMDPI AGApplied Sciences2076-34172025-05-011511591810.3390/app15115918Optimizing Flight Delay Predictions with Scorecard SystemsIlona Jacyna-Gołda0Krzysztof Cur1Justyna Tomaszewska2Karol Przanowski3Sarka Hoskova-Mayerova4Szymon Świergolik5Faculty of Mechanical and Industrial Engineering, Warsaw University of Technology, 00-661 Warsaw, PolandInstitute of Logistics and Transport, Polish Air Force University, Dywizjonu 303 Street No. 35, 08-521 Dęblin, PolandFaculty of Aviation, Polish Air Force University, Dywizjonu 303 Street No. 35, 08-521 Dęblin, PolandStatistical Methods & Business Analytics Unit, SGH Warsaw School of Economics, al. Niepodległości 162, 02-554 Warsaw, PolandDepartment of Mathematics and Physics, Faculty of Military Technology, University of Defence, Kounicova 65, 66210 Brno, Czech RepublicAir Force Institute of Technology, Księcia Bolesława 6, 01-494 Warsaw, PolandFlight delays represent a significant challenge for airlines, airports, and passengers, impacting operational costs and customer satisfaction. Traditional prediction methods often rely on complex statistical analysis and mathematical models that may not be easily implementable. This study proposes scorecards as an innovative and simplified approach to forecast flight delays. Historical flight data from the United States were used, incorporating variables such as departure and arrival times, flight routes, aircraft types, and other factors related to delay. Exploratory data analysis identified key variables influencing delays, and scorecards were constructed by assigning weights, normalizing, and scaling variables to improve interpretability. The model was validated using test datasets, and predictive performance was evaluated by comparing forecast delays with actual results. The results indicate that scorecards provide accurate and interpretable predictions of flight delays. This method facilitates the identification of critical factors that contribute to delays and allows for an estimation of their likelihood and duration. Scorecards offer a practical tool for airlines and airport operators, potentially enhancing decision-making processes, reducing delay-related costs, and improving service quality. Future research should explore the integration of scorecards into operational systems and the inclusion of additional variables to increase model robustness and generalizability.https://www.mdpi.com/2076-3417/15/11/5918operational forecastingair traffic managementflight delay |
| spellingShingle | Ilona Jacyna-Gołda Krzysztof Cur Justyna Tomaszewska Karol Przanowski Sarka Hoskova-Mayerova Szymon Świergolik Optimizing Flight Delay Predictions with Scorecard Systems Applied Sciences operational forecasting air traffic management flight delay |
| title | Optimizing Flight Delay Predictions with Scorecard Systems |
| title_full | Optimizing Flight Delay Predictions with Scorecard Systems |
| title_fullStr | Optimizing Flight Delay Predictions with Scorecard Systems |
| title_full_unstemmed | Optimizing Flight Delay Predictions with Scorecard Systems |
| title_short | Optimizing Flight Delay Predictions with Scorecard Systems |
| title_sort | optimizing flight delay predictions with scorecard systems |
| topic | operational forecasting air traffic management flight delay |
| url | https://www.mdpi.com/2076-3417/15/11/5918 |
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