A novel intelligent approach for flight delay prediction
Abstract Flight delay prediction is one of the most significant components of intelligent aviation systems that may spread throughout the whole aviation network and cause multi-billion-dollar losses faced by airlines and airports, it is quickly becoming an important research issue to improve airport...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-12-01
|
| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-023-00854-w |
| _version_ | 1797376910512095232 |
|---|---|
| author | Maged Mamdouh Mostafa Ezzat Hesham A.Hefny |
| author_facet | Maged Mamdouh Mostafa Ezzat Hesham A.Hefny |
| author_sort | Maged Mamdouh |
| collection | DOAJ |
| description | Abstract Flight delay prediction is one of the most significant components of intelligent aviation systems that may spread throughout the whole aviation network and cause multi-billion-dollar losses faced by airlines and airports, it is quickly becoming an important research issue to improve airport and airline performance. Thus this paper proposed an effective algorithm called Flight Delay Path Previous-based Machine Learning (FDPP-ML) capable of improved prediction of individual flight delay minutes using regression models to an up level of accuracy. As aviation system connectivity presents complex spatial–temporal correlations, machine learning approaches have addressed flight delay prediction by using complex flight or weather features, or private information for specific airports and airlines that are hard to obtain, In contrast, the proposed FDPP-ML improved prediction based only on basic flight schedule features even with wide flight networks. The FDPP-ML consists of a novel algorithm with a supervised learning model, which works on reshaping datasets and creates two new features the main feature is previous flight delay (PFD) for flight paths, there is a strong relationship between departure and arrival delay, and vice versa for the same flight path, which increases the strength of the training model based on historical data. For target future flights, the algorithm works on inheriting the predicted flight delay to the next flight on the same flight path and repeats this process to end the prediction forecast horizon. The proving of approach effectiveness by using a wide network of US flight arrival and departure flights containing 366 airports and 10 airlines with various metrics accuracies of regression, and explanatory the impacts on various forecast horizons 2, 6, and 12 h for future flights. The FDPP-ML outperforms traditional training models by using machine and deep learning models and improving model accuracy in 10 models with an average of up to 39% in MAE, and 42% in MSE in a forecast horizon of 2 h. Finally, providing airport and airline analysis further reveals that can improve prediction than traditional training models for the individual busiest airports "Core 30" with an average of 35% in MAE and 42% in MSE respectively, and for the busiest 10 airlines with an average of 36% in MAE and 47% in MSE respectively. The findings of this study may offer informative recommendations to airport regulators and aviation authorities for developing successful air traffic control systems for enhanced flight delay prediction to flight operational effectiveness, not only over the US flight network but with wide worldwide flight networks if a dataset of flights existed. |
| first_indexed | 2024-03-08T19:46:20Z |
| format | Article |
| id | doaj.art-6ec24f082e9f4705bad2b324838ac32d |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-03-08T19:46:20Z |
| publishDate | 2023-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-6ec24f082e9f4705bad2b324838ac32d2023-12-24T12:21:32ZengSpringerOpenJournal of Big Data2196-11152023-12-0110114010.1186/s40537-023-00854-wA novel intelligent approach for flight delay predictionMaged Mamdouh0Mostafa Ezzat1Hesham A.Hefny2Department of Computer Science, Faculty of Graduate Studies for Statistical Researches (FGSSR), Cairo UniversityDepartment of Computer Science, Faculty of Graduate Studies for Statistical Researches (FGSSR), Cairo UniversityDepartment of Computer Science, Faculty of Graduate Studies for Statistical Researches (FGSSR), Cairo UniversityAbstract Flight delay prediction is one of the most significant components of intelligent aviation systems that may spread throughout the whole aviation network and cause multi-billion-dollar losses faced by airlines and airports, it is quickly becoming an important research issue to improve airport and airline performance. Thus this paper proposed an effective algorithm called Flight Delay Path Previous-based Machine Learning (FDPP-ML) capable of improved prediction of individual flight delay minutes using regression models to an up level of accuracy. As aviation system connectivity presents complex spatial–temporal correlations, machine learning approaches have addressed flight delay prediction by using complex flight or weather features, or private information for specific airports and airlines that are hard to obtain, In contrast, the proposed FDPP-ML improved prediction based only on basic flight schedule features even with wide flight networks. The FDPP-ML consists of a novel algorithm with a supervised learning model, which works on reshaping datasets and creates two new features the main feature is previous flight delay (PFD) for flight paths, there is a strong relationship between departure and arrival delay, and vice versa for the same flight path, which increases the strength of the training model based on historical data. For target future flights, the algorithm works on inheriting the predicted flight delay to the next flight on the same flight path and repeats this process to end the prediction forecast horizon. The proving of approach effectiveness by using a wide network of US flight arrival and departure flights containing 366 airports and 10 airlines with various metrics accuracies of regression, and explanatory the impacts on various forecast horizons 2, 6, and 12 h for future flights. The FDPP-ML outperforms traditional training models by using machine and deep learning models and improving model accuracy in 10 models with an average of up to 39% in MAE, and 42% in MSE in a forecast horizon of 2 h. Finally, providing airport and airline analysis further reveals that can improve prediction than traditional training models for the individual busiest airports "Core 30" with an average of 35% in MAE and 42% in MSE respectively, and for the busiest 10 airlines with an average of 36% in MAE and 47% in MSE respectively. The findings of this study may offer informative recommendations to airport regulators and aviation authorities for developing successful air traffic control systems for enhanced flight delay prediction to flight operational effectiveness, not only over the US flight network but with wide worldwide flight networks if a dataset of flights existed.https://doi.org/10.1186/s40537-023-00854-wFlight delay predictionRegressionMachine learningDeep learningAir traffic |
| spellingShingle | Maged Mamdouh Mostafa Ezzat Hesham A.Hefny A novel intelligent approach for flight delay prediction Journal of Big Data Flight delay prediction Regression Machine learning Deep learning Air traffic |
| title | A novel intelligent approach for flight delay prediction |
| title_full | A novel intelligent approach for flight delay prediction |
| title_fullStr | A novel intelligent approach for flight delay prediction |
| title_full_unstemmed | A novel intelligent approach for flight delay prediction |
| title_short | A novel intelligent approach for flight delay prediction |
| title_sort | novel intelligent approach for flight delay prediction |
| topic | Flight delay prediction Regression Machine learning Deep learning Air traffic |
| url | https://doi.org/10.1186/s40537-023-00854-w |
| work_keys_str_mv | AT magedmamdouh anovelintelligentapproachforflightdelayprediction AT mostafaezzat anovelintelligentapproachforflightdelayprediction AT heshamahefny anovelintelligentapproachforflightdelayprediction AT magedmamdouh novelintelligentapproachforflightdelayprediction AT mostafaezzat novelintelligentapproachforflightdelayprediction AT heshamahefny novelintelligentapproachforflightdelayprediction |