Prediction of rail passenger flow based on time-frequency decomposition and deep learning
To improve the accuracy of short-term passenger flow forecasting at urban rail stations,the passenger flow of each type of station on the basis of the classification of rail stations was predicted.Dynamic time warping was used as a measure,and the K-means algorithm was used to classify the stations.The time-series characteristics of passenger flow at various stations were ana-lyzed.To weaken the influence of noise in the original passenger flow data,the empirical mode decomposition(EMD)method was used to perform time-frequency decomposition on the original passenger flow of various stations.A deep learning model that com-bined graph convolution network(GCN)and gated recurrent unit(GRU)was proposed,and the components decomposed by EMD were used as model input.Taking Xi'an Metro as an example,the results showed that the stations could be divided into five types:office employment type,dense residential type,leisure and entertainment type,remote residential type,and occupation-residential balance type according to the time series characteristics of passenger flow in a continuous week.The average absolute percentage er-ror and the root mean square error were used as evaluation indicators.The results showed that the method proposed in this study out-performed the baseline model in terms of accuracy for predicting passenger flow at various stations.
NETL
NSTL
万方数据
