Research on Passenger Flow Forecast of Urban Rail Transit Based on SARIMA-RBF Combination Model

摘要

Passenger flow forecast is the basis of passenger transportation organization, and the forecast results can provide decision basis for operation management and emergency response. The actual passenger flow variation has both linear and nonlinear patterns. According to the characteristics of Seasonal Autoregressive Integrated Moving Average model and Radial Basis Function neural network model, the combined model established in this paper by combining these two models can grasp the linear law of passenger flow sequence, effectively solve the linear change law of passenger flow sequence, and also take into account the nonlinear law sequence of passenger flow.