Bimodal Regression Method for Passenger Flow Prediction in Large Airport Terminals

With the rapid development of the civil aviation industry, the number of civil aviation passengers continues to increase. However, it also brings new challenges to the crowd control capability of airports. Predicting the trend of the passen-ger flow is an important way to realize intelligent airport crowd control, and also has profound significance for building a modern national airport system, which is conducive to further improving the service quality and management level. In this paper, we propose an effective bimodal regression method for predicting the passenger flow by utilizing flight schedule information and crowd density information. Experimental results show that the proposed method offers promising performance in terms of prediction accuracy and robustness.