Flight Passenger Load Factors Prediction Based on RNN Using Multi-Granularity Time Attention
摘要
Accurate prediction of Flight Passenger Load Factors(FPLFs) helps in addressing overbooking and overserving of the flight seats.However,traditional time series-based prediction methods only focus on variation feature of recent daily FPLFs and ignore impacts of other factors,leading to limited prediction performance.To address the problem,this paper proposes a recurrent neural network model using multi-granularity temporal attention mechanism named MTA-RNN.The model constructs a hierarchical attention mechanism to acquire the temporal correlation of FPLFs under different temporal granularities.Also,other factors including the properties of a flight,festivals and holidays are introduced into the model to compute the target FPLFs over a certain period in the future.Experimental results on datasets of real historical FPLFs show that the MTA-RNN model has a higher prediction accuracy than ARIMA,LSTM and Seq2seq models.