基于栈式自编码的高速铁路站间客流短期预测研究

Short-Term Passenger Flow Prediction between High-Speed Railway Stations Based on Stacked Auto-encoder

站间短期客流预测是高速铁路运营管理的重要依据。首先在提取原始客流数据特征的基础上得到样本和标签集,然后基于栈式自编码算法预训练神经网络模型参数,最后构建神经网络预测模型。以渝万高铁为例,采用2016年11月到2018年10月数据进行验证,结果表明:提出的模型预测误差为12.08%,与其它4种常用预测模型相比精度分别提高12.12%、1.12%、6.9%和19.12%,模型适用于短期客流预测。

Short-term passenger flow forecast between stations is an important basis for high-speed railway operation and management.Firstly,the samples and tag sets were obtained based on the extraction of the characteristics of the original passenger flow data;and then,the parameters of the neural network model were pre-trained based on the stacked auto-encoder algorithm;finally,the neural network prediction model was constructed.Taking Yuwan high-speed railway as an example,the data from November 2016 to October 2018 were used for verification.The results show that the prediction error of the proposed model is 12.08%,which improves the accuracy by 12.12%,1.12%,6.9%and 19.12%respectively,compared with the other four commonly used prediction models.The proposed model is suitable for short-term passenger flow prediction.