Abstract:

To improve the safety of air traffic operations, a delay level prediction method based on the combination of spatiotemporal association rule mining and deep learning was proposed. Firstly, the average flight delay time and delay rate were selected as airport delay metrics, and their spatial-temporal correlation characteristics were analyzed. Secondly, the airport delay levels were identified based on Fuzzy-C Means (FCM)clustering algorithm, and the spatiotemporal association rules of airport delay were mined based on (FP(Frequent Pattern)Growth) algorithm. Thirdly, sample data was constructed based on association rules and delay time series, which was put into LSTM model to predict the future airport delay levels. At the same time, attention mechanism was introduced into the prediction model to learn the influence of different rules on prediction. Finally, the actual US flight data were collected for example analysis. The results show that the average prediction accuracy of overall delay levels reaches 0.91 and the prediction accuracy of different periods is all larger than 80%. The connection weight of the attention layer network reflects the influence of each rule on the prediction, which can be used to explain the prediction results.

Key words: spatiotemporal association rules, long short term memory (LSTM), airport arrival, delay level, delay prediction, air traffic management