Comparison Airport Traffic Prediction Performance Using BiGRU and CNN-BiGRU Models

Abstrak

COVID-19 pandemic has significantly disrupted the aviation industry, highlighting the critical need for accurate airport traffic predictions. This study compares the performance of BiGRU and CNN-BiGRU models to enhance airport traffic forecasting accuracy models from March to December 2020. Data preprocessing was performed using Python's Pandas library. This involved filtering, scaling using min-max normalization, and splitting the data into 80:20 training-testing split using Python's Pandas library. Various optimization techniques—RMSProp, Adam, Nadam, Adamax, AdamW, and Lion—were applied, along with ReduceLROnPlateau, to optimize model performance. The models were evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). The best predictive performance was observed in the United States using the CNN-BiGRU model with the Adam optimizer, achieving the lowest MAE of 0.0580, MSE of 0.0097, and MAPE of 0.0979. The use of a balanced dataset, representing each airport's traffic as a percentage of a baseline period, significantly improved prediction accuracy. This research provides valuable insights for stakeholders seeking effective airport traffic prediction methods during unprecedented times.