Short-term forecast of airport flow based on twice decomposition integration
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
维普
万方数据
为实现准确的机场流量短期预测,本文建立了基于二次分解方法的分解集成预测模型.首先,应用局部加权回归周期趋势分解(STL,seasonal and trend decomposition procedure based on Loess)算法将原始时间序列分解为趋势项、季节项和余项3个分量,并计算其样本熵.其次,应用遗传算法(GA,genetic algorithm)优化变分模态分解(VMD,variational mode decomposition)参数,对熵值较大的分量进行二次分解.再次,使用极端梯度提升(XGBoost,extreme gradient boosting)对二次分解后的所有分量进行预测,采用加和集成得到最终的预测值.最后,采集国内典型机场实际运行数据进行实例分析.针对北京首都国际机场60 min进场、离场流量时序,本文模型预测的均等系数(EC,equal coefficient)值分别为0.9703、0.9959,相比其他常用模型均有所提高.此外,对于上海浦东、上海虹桥、广州白云3个大型国际机场,本文模型在60 min、30 min统计尺度下进场和离场流量预测的EC值均在0.9700以上,15 min统计尺度下预测的EC值均在0.9500以上.结果表明,本文建立的二次分解集成预测模型具有良好的准确性和普适性,用于机场流量短期预测是可行和有效的.
In order to achieve accurate short-term forecast of airport flow,a decomposition integration forecast model based on twice decomposition method is established in this paper. Firstly,the seasonal and trend decomposition procedure based on Loess (STL) algorithm is applied to decompose the original time series into three components,including trend term,seasonal term and residual term,and their sample entropy are calculated. Secondly,genetic algorithm (GA) is applied to optimize the parameters of variational mode decomposition (VMD),and the components with larger entropy values are subjected to twice decomposition. Thirdly,extreme gradient boosting (XGBoost) is applied to predict all components after twice decomposition,and the final predicted value is obtained by adding and inte-grating. Finally,the actual operation data of domestic typical airports are collected for case analysis. For the 60 min arrival and departure flow time series of Beijing Capital International Airport,the equal coefficient (EC) values pre-dicted in this paper are 0.9703 and 0.9959 respectively,which has an improvement compared to other common models. In addition,for the three large international airports of Shanghai Pudong,Shanghai Hongqiao,and Guangzhou Baiyun,the EC values predicted by the proposed model are all above 0.9700 for arrival and departure flow at 60 min and 30 min scales,and the predicted EC values at the 15 min scale are all above 0.9500. The results indicate that the twice decomposition integration forecast model established in this paper has good accuracy and u-niversality,and is feasible and effective for short-term forecast of airport flow.
air transportair traffic flow managementshort-term forecast of airport flowdecomposition integration forecasttwice decomposition
维普
万方数据
