Research on airport passenger flow forecast based on PSO-BP neural network

Airport passenger flow forecast is the basis of revenue management for airlines. Accurate forecast model can help airlines develop more accurate sales strategies to reduce costs and maximize revenue. Since BP neural network model has the problems of slow convergence speed and easy to fall into local optimality when predicting passenger flow, improved particle swarm optimization algorithm is used to optimize BP neural network model and establish PSO-BP neural network prediction model. The passenger flow data of Beijing Capital Airport is used to carry out the experiment. The results show that compared with BP neural network, PSO-BP neural network can effectively improve the accuracy and stability of the forecast, and provide a new way of thinking for airport passenger flow