An enhanced deep autoencoder for flight delay prediction

Accurate and timely flight delay prediction cannot be overemphasized because of the ever-increasing demand for air travel and its importance in deploying intelligent transportation systems. Nonetheless, there has not been a universal solution to the problem, as more intelligent flight decision systems are required for the aviation industry’s future growth. Existing flight delay classification and prediction approaches are mainly shallow traffic models and do not satisfy many applications in the real world. Our motivation to rethink the deep architecture model for predicting flight delays emanates from the problem. In this research, we proposed a technique that modified stacked autoencoder architecture parameters for training the network and understanding the link between space, time and information gained from the flight on-time data. We developed three different types of autoencoders based on the architecture of the modified stacked autoencoder. The models learn the generic flight delay features, and it’s trained greedily in a layer-wise fashion. To the best of our knowledge, this is the first time these performances of vanilla autoencoder, logistic regression autoencoder and Multilayer perceptron for classification were evaluated based on the developed modified stacked autoencoder architecture. Moreover, our experiment demonstrates that the models achieved varying levels of accuracy in the flight delay classifications task. The deep vanilla autoencoder shows superior accuracy, recall and precision performance compared to logistic regression autoencoder and Multilayer perceptron autoencoders at different parameter settings.