基于小波包与长短时记忆融合的铁路旅客流量预测模型

Hybrid Model Based on Wavelet Packet and Long Short-Term Memory for Railway Passenger Traffic Volume Prediction

短期铁路客运需求量的实时精准预测可以为实时调整客运服务结构提供依据.铁路旅客流量数据具有时变性、非线性和随机波动性等特点,传统的预测模型无法精准的预测短期内的客流量.本文提出一种基于小波包分解与长短时记忆融合的深度学习预测模型(WPA-LSTM),首先用小波包分解将原始客运量时间序列分解重构成多个不同尺度的低频和高频序列,然后分别针对各个子序列进行LSTM模型训练和预测,最后将各子序列的预测值叠加作为WPA-LSTM模型的输出.采用某高铁367天的日旅客流量数据对模型进行实验验证,并与季节性模型和基于经验模态的长短时记忆融合模型进行对比,实验结果表明,WPA-LSTM模型可有效提高铁路旅客流量预测的精度.

Real-time accurate prediction of short-term railway passenger demand can provide basis for real-time adjustment of passenger service structure.Railway passenger flow data has characteristics such as time-varying,nonlinear and stochastic volatility.Traditional forecasting models cannot predict the short-term passenger traffic volume accurately.This study proposed a hybrid deep learning model based on Wavelet Packet Analysis and Long Short-Term Memory（WPA-LSTM）.Firstly,the original passenger volume time series is decomposed into several low-frequency and highfrequency sequences with different scales by wavelet packet.Then,the LSTM model training and prediction are carried out respectively for each sub-sequence.Finally,the prediction values of each sub-sequence are superimposed as the output of WPD-LSTM model.The model was validated by the daily passenger flow data of 367 days of one high-speed railway.The model was compared with the seasonal model and the empirical model.The experimental results show that WPD-LSTM model can effectively improve the accuracy of railway passenger traffic forecasting.