Short-term passenger flow prediction of urban rail transit based on wavelet neural network
CHEN Tongjian
SHEN Dekui
Abstract：In order to further explore the issue of short-term passenger flow prediction for urban rail transit,this paper analyzes the factors influencing the short-term passenger flow of urban rail transit from three aspects and extracts the main influencing factors.Wavelet neural network is used for prediction under two conditions,and the prediction results are evaluated by mean absolute percentage error and root mean square error.The results show that the model has good stability and excellent prediction performance.Regardless of whether the model input variables are in a specific time sequence,the overall prediction error of the model is within 10%.This model is suitable for short-term passenger flow prediction of urban rail transit and can provide a reference for the management and operation of urban rail transit.
Keywords：urban rail transitpassenger flowshort-term predictionWNN
Publication Date：2025-03-25
Pages：3( 41-43 )