Research on Railway Passenger Volume Forecast Based on the Spline Interpolation and IPSO-Gradient Difference Acceleration Rule

Abstract

In order to solve the problem that the railway passenger volume data are abnormal due to holidays and major events interfering with the prediction accuracy, the spline interpolation method is introduced to replace the abnormal passenger volume data. In addition, an improved particle swarm optimization (IPSO) is proposed to optimize the gradient difference acceleration law to combine and improve the predicted value and further improve the prediction accuracy of the railway passenger traffic. Finally, taking Beijing as the research object, the Holt exponential smoothing method and the BP neural network are selected to verify the effect of spline interpolation and IPSO-gradient difference acceleration law on prediction accuracy. The research results show that the spline interpolation method has a better prediction effect after processing abnormal passenger traffic data, and the improved particle swarm algorithm also shows better optimization ability and convergence speed when solving the double difference postulate. In comparison with the BP neural network, Holt exponential smoothing, simple averaging, and conventional redifference approaches, the IPSO-redifference acceleration method achieves a superior prediction performance, and the absolute values of the forecast error are reduced by 3.320%, 1.518%, 2.419%, and 0.602%.