基于VMD-BP神经网络模型的铁路车站月度客流发送量预测研究

Forecast of Monthly Passenger Flow Transport Volume at Railway Station Based on VMD-BP Neutral Network Model

为确定合理的列车开行对数,首先分别以BP神经网络(BP Neutral Network)和极限学习机(Extreme Leaning Machine,ELM)构建基准模型;在此基础上,结合经验模态分解算法(Empirical Mode Decomposition,EMD)和变分模态分解算法(Variational Mode Decomposition,VMD)适合处理非线性、非平稳数据的特点,对客流发送数据进行处理,然后分别与基准模型组合形成组合模型,并提出了月度客流发送量的概念,建立了滚动的月度客流发送量预测机制。最后,以兰州西站衔接各线的客流日发送数据为原始数据,以各基准模型和组合模型对月度客流发送量进行了预测,并对预测结果和指标加以分析对比。研究结果表明:(1)以客流发送量作为原始数据,VMD算法分解效果优于EMD算法;(2)对比各基准模型和组合模型,VMD-BP神经网络组合模型预测精度最高,适用于铁路车站月度客流发送量预测问题。

In order to determine the reasonable pairs of trains, the benchmark model was established firstly based on BP neural network and ELM (Extreme Leaning Machine) respectively. On this basis, EMD (Empirical Mode Decomposition) algorithm and VMD (Variational Mode Decomposition) algorithm that were suitable for dealing with the nonlinear and non-stationary data were used to process the passenger flow transport volume. Then combined with the benchmark models, combined models were formed. The concept of monthly passenger flow transport volume was proposed, and the rolling monthly passenger flow transport volume forecast mechanism was established. Finally, taking the daily passenger flow transport volume of LanzhouWest Railway Station as the original data, monthly passenger flow transport volume was forecasted by each benchmark model and combined model, and the forecast results and indicators were analyzed and compared. The results show that:(1) taking the passenger flow transport volume as the original data, the decomposition effect of VMD algorithm is better than EMD algorithm;(2) comparing benchmark models with combined models, the VMD-BP neural network combined model has the highest forecast accuracy and is suitable for forecasting the monthly passenger flow transport volume at railway stations.