A computational intelligence based prediction model for flight departure delays

Abstract : Flight departure delays are a major problem at OR Tambo International airport (ORTIA). There is a high delay for flights to depart, especially at the beginning of the month and at the end of the month. The increasing demand for flights departing at ORTIA often leads to a negative effect on business deals, individuals’ health, job opportunities and tourists. When flights are delayed departing, travellers are notified at the airport every 30 minutes about the status of the flight and the reason the flight is delayed if it is known. This study aims to construct a flight delays prediction model using machine learning algorithms. The flight departures data were obtained from ORTIAs website timetable for departing flight schedules. The flight departure data for ORTIA to any destination (i.e. Johannesburg (JNB) Airport to Cape Town (CPT)) for South African Airways (SAA) airline was used for this study. Machine learning algorithms namely Decision Trees (J48), Support Vector Machine (SVM), K-Means Clustering (K-Means) and Multi-Layered Perceptron (MLP) were used to construct the flight departure delays prediction models. A cross-validation (CV) method was used for evaluating the models. The best prediction model was selected by using a confusion matrix. The results showed that the models constructed using Decision Trees (J48) achieved the best prediction for flight departure delays at 67.144%, while Multi-layered Perceptron (MLP) obtained 67.010%, Support Vector Machine (SVM) obtained 66.249% and K-Means Clustering (K-Means) obtained 61.549%. Travellers wishing to travel from ORTIA can predict flight departure delays using this tool. This tool will allow travellers to enter variables such as month, week of month, day of week and time of day. The entered variables will predict the flight departure status by examining target concepts such as On Time, Delayed and Cancelled. The travellers will only be able to predict flight departures status, although they will not have full knowledge of the flight departures volume. In that case, they will depend on the flight information display system (FIDS) board. This study can predict and empower travellers by providing them with a tool that can determine the punctuality of the flights departing from ORTIA.M.Com. (Information Technology Management