Airport Passenger Flow Forecast with Optimized EDM Based on Attention Mechanism

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    The standard encoder-decoder model has a weak ability to extract time series data. For this reason, this study proposes a novel encoder-decoder model integrating the Bi-directional Long Short-Term Memory (Bi-LSTM) and Attention Mechanism (AM). First, the input data is extracted by Bi-LSTM from both positive and negative directions. Then, different weights are assigned to the obtained features by AM according to different moments, and corresponding background variables are generated according to different moments in the decoding stage. As a result, the airport passenger flow can be predicted. Further, the Shanghai Hongqiao International Airport is taken as an example for the experimental simulation with this algorithm. Experimental results show that compared with Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), the proposed method reduces the average standard error by more than 57.9%. This study provides a new way for airport passenger flow forecast.

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  • Received:January 04,2021
  • Revised:January 29,2021
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  • Online: October 08,2021
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