基于ARIMA与LSTM的铁路车站客流预测方法比较

Comparison of railway passenger flow forecast methods based on ARIMA and LSTM

精准的客流预测是车站客运组织优化的基础,是提高运营安全和运输效率的有效途径。以江门东站全年进站客流数据为研究对象,分别构建ARIMA时间序列模型与LSTM神经网络模型,从预测精度、计算速度、误差指标评价、模型适应性等方面分析比较两种预测模型对客流预测结果的差异性。结果表明,LSTM模型预测精度和拟合精确度更优,ARIMA模型计算速度更快。研究结果对客流预测方法选择有借鉴意义。

Accurate passenger flow forcast is the basis of the optimization of passenger transportation organization and an effective way to improve operational safety and transportation efficiency.Taking the annual passenger flow data of Jiangmen East Railway Station as research object,and the ARIMA time series model and the LSTM neural network model were constructed respectively.From the perspectives of forecast accuracy,calculation speed,error index evaluation,and model adaptability,the differences in passenger flow forecast results of two forecast models were analyzed.The results show that the LSTM model has better forecast accuracy and fitting accuracy,and the ARIMA model has faster calculation speed.This study result can provide significant practical implications for the selection of passenger flow forecast methods.