Flight Delay Propagation Prediction Based on Deep Learning

Abstract

The current flight delay not only affects the normal operation of the current flight, but also spreads to the downstream flights through the flights schedule, resulting in a wide range of flight delays. The analysis and prediction of flight delay propagation in advance can help civil aviation departments control the flight delay rate and reduce the economic loss caused by flight delays. Due to the small number of data samples that can constitute flight chains, it is difficult to construct flight chain data. In recent years, the analysis of the flight delay propagation problem is generally based on traditional machine learning methods with a small sample size. After obtaining a large amount of raw data from the China Air Traffic Management Bureau, we have constructed 36,287 pieces of three-level flight chain data. Based on these data, we tried to use a deep learning method to analyze and forecast flight delays. In the field of deep learning, there are CNN models and RNN models that deal with classification problems well. Based on these two classes of models, we modify and innovate the study of the problem of flight delay propagation and prediction. Firstly, the CNN-based CondenseNet algorithm is used to predict the delay level of the three-level flight chain data. Based on this, the CondenseNet network is improved by inserting CBAM modules and named CBAM-CondenseNet. The experimental results show that the improved algorithm can effectively improve the network performance, and the prediction accuracy can reach 89.8%. Compared with the traditional machine learning method, the average prediction accuracy increased by 8.7 percentage points. On the basis of the CNN model, we also considered the superiority of the LSTM (Long Short-Term Memory network) considering the processing time sequence information, and then constructed the CNN-MLSTM network and injected the SimAM module to enhance the attention of flight chain data. In the experiment of flight delay propagation prediction, the accuracy rate is 91.36%, which is a significant improvement compared to using the CNN or LSTM alone.