Abstract: In order to forecast the subway passenger flow more accurately, a short-term prediction model of subway passenger flow based on empirical mode decomposition (EMD) optimization of nonlinear auto regressive (NAR) dynamic neural network is proposed. First, by analyzing subway passenger flow data, we find that the daily passenger flow performs in a certain change rule. Second, we select a time-based NAR dynamic neural network, which has excellent nonlinear dynamic fitting ability and feedback memory function. Furthermore, in order to reduce prediction errors and improve prediction accuracy, we use EMD empirical mode decomposition algorithm to optimize the NAR dynamic neural network prediction model. Simulation results show that the EMD-NAR neural network combined prediction model is well applicable for short-term prediction of subway passenger flow with high prediction result accuracy about 93%.

Key words: subway passenger flow, short-term prediction, nonlinear auto regressive (NAR) dynamic neural network, empirical mode decomposition (EMD), combined model