地铁车站客流预测方法比较研究
Comparative Study of the Passenger Flow Prediction Method for Metro Stations
余伟之 1夏三县 2篮杰 1刘军 2勾宇鹏 3何大四 3王亚勇 1白晓燕2
作者信息
- 1. 中铁第四勘察设计院集团有限公司,湖北 武汉 460063
- 2. 郑州地铁集团有限公司,河南 郑州 450001
- 3. 中原工学院,河南 郑州 450001
- 折叠
摘要
为了更加合理地进行地铁车辆调度和制定人员配置方案,并在满足人们出行需求的基础上实现资源利用最大化,对地铁客流量进行准确地短时预测是非常必要的,同时客流预测对地铁站厅空调系统的运行调节也具有重要作用.文章通过对郑州某地铁车站 2014 年 6-7 月的进站小时客流量数据进行统计分析,构建季节性差分自回归滑动平均(SARIMA)模型、非线性自回归神经网络(NAR)模型和长短期记忆网络(LSTM)模型,用统计数据进行模型训练并实施预测.通过在工作日客流预测中,发现 LSTM模型在 MAE、RMSE和R2 上均优于其他模型,拟合系数R2 达到 0.981 4,MAE为 55.84,均方根误差为 88.56;在非工作日客流预测中,LSTM模型同样表现出最好的效果,R2 达到 0.981 7;SARIMA 模型精度接近 LSTM模型.这说明在对具有明显周期性数据预测时,无论是经典的时间序列方法还是先进的深度学习方法预测结果都很好,传统的神经网络因为无法捕捉周期性所以预测效果较差,预测精度相对较低.
Abstract
In order to dispatch the metro vehicles and establish the staffing scheme more reasonably,and maximize the use of resources on the basis of satisfying the people's travel needs,it's very necessary to make accurate short-term predictions of the metro passenger flow,meanwhile,the passenger flow prediction is also important for adjusting the operation of the air conditioning system in the metro station hall.This article conducts a statistical analysis of the passenger flow data entered per hour during June and July of 2014 at a metro station in Zhengzhou,constructs a seasonal autoregressive integrated moving average(SARIMA)model,a non-linear autoregressive(NAR)neural network model,and a long and short-term memory network(LSTM)model,trains the models with statistical data,and generates predictions.From the passenger flow predictions of working days,it turns out that the LSTM model is better than the other models in MAE,RMSE and R2,with the fit coefficients R2=0.981 4,MAE=55.84 and RMSE=88.56.In the passenger flow prediction of non-working days,the LSTM model also gives the best effect,with R2=0.981 7;and the accuracy of the SARIMA model is close to that of the LSTM model.It shows that in the prediction of data with obvious periodicity,the prediction result is good both with the classical time series method and with the advanced deep learning approach,while the conventional neural network has a worse effect of prediction and a lower accuracy of prediction because it is incapable of catching the periodicity.
关键词
地铁车站/客流/SARIMA模型/NAR神经网络/长短期记忆网络模型/短时预测Key words
metro station/passenger flow/SARIMA model/NAR neural network/long-and short-term memory network model/short-term prediction引用本文复制引用
出版年
2024
NETL
NSTL
万方数据