Flight Delay Forecast
Goal
Predict aircraft delays in airport terminal area using machine learning techniques on historical data.
Focus
- On departure and arrival delays of flights for 227 European airports.
- Airlines: Charter, Low-cost, Traditional Scheduled, All-Cargo, Business Aviation.
Data sources
Traffic, time, and waiting times from 2015 to mid-2018
Weather from 2015 to mid-2018
Departure model & results
Trained on 227 ICAOS
Plotted 4 ICAOs
See F1-score»
Arrival model & results
Trained on 227 ICAOS
Plotted 4 ICAOs
See F1-score»
Additional experiments
In order to make a comparison, it was decided to run the same trainings using the same models and datasets on only 10 ICAOs, the most recurring ones within the entire dataset.
Training on 10 ICAOS: LSZH, LTBA, LEBL, EKCH, ENGM, LOWW, LTFJ, EGKK, LFPO, ESSA
Departure
Trained on 227 ICAOS
Trained on 10 ICAOS
Arrival
Trained on 227 ICAOS
Trained on 10 ICAOS
Conclusions
The model should be refined for being used in a real environment. The performance related to some airports should be improved.
Soul Software model’s results are comparable with the those provided by Eurocontrol in the 2021.
Future developments
The performance should improve through an enrichment of the dataset with weather information, airports features, etc...
