基于SARIMA‑LSTM组合的机场起降量短时预测方法  

Short-Term Forecasting Method for Airport Takeoff and Landing Volume Based on SARIMA-LSTM Combination

作　　者：杨慧云 李印凤 段满珍 阮昌 YANG Huiyun;LI Yinfeng;DUAN Manzhen;RUAN Chang(School of Emergency Management and Safety Engineering,North China University of Science and Technology,Tangshan 063210,Hebei,China;Tangshan Key Laboratory of Air-Ground Intelligent Transportation,Tangshan 063210,Hebei,China;China Civil Aviation Air Traffic Management Bureau in North China,Beijing 100621,China)

机构地区：[1]华北理工大学应急管理与安全工程学院,河北唐山063210 [2]唐山市空地智慧交通重点实验室,河北唐山063210 [3]中国民用航空华北地区空中交通管理局,北京100621

出　　处：《指挥信息系统与技术》2024年第5期29-35,共7页Command Information System and Technology

基　　金：江苏省自然科学基金青年基金(BK20170157)资助项目。

摘　　要：机场起降量短时预测方法是根据空中交通流量管理需求,对机场未来24小时时间跨度内起降量情况进行预测。首先,构建了基于季节性差分自回归移动平均(SARIMA)和长短期记忆神经网络(LSTM)的机场起降量预测模型;然后,根据误差倒数法确定组合预测权重以期得到更好的预测效果;最后,使用天津滨海机场进行实例验证,以机场起降量的小时数据建立了SARIMA(0,1,7)×(0,1,1)_(24)和LSTM模型,并分别以0.600和0.400的权重建立了组合预测模型。验证结果显示,组合模型的预测指标R2达到0.904,较反向传播(BP)神经网络等其他单一模型预测性能更佳。Short-term forecasting methods according to the demand of air traffic flow management,predict the airport takeoff and landing volume within a 24-hour time span.Firstly,an airport takeoff and landing volume forecasting models based on seasonal auto regressive integrated moving average(SARIMA)and long short term memory network(LSTM)is constructed.Then,with the error re⁃ciprocal method,the combined forecasting weights are determined to achieve better prediction results.Finally,using Tianjin Binhai Airport as an example to verify the model,the SARIMA(0,1,7)×(0,1,1)_(24) and LSTM models based on hourly takeoff and landing volume data are built,and a com⁃bined prediction model with weights of 0.600 and 0.400 respectively is established.The verification re⁃sults show that the prediction indicator R^(2) of the combined model reaches 0.904.It demonstrates better prediction performance than back propagation(BP)neural network and other single models.

关 键 词：机场起降量 季节性差分自回归移动平均(SARIMA)模型 长短期记忆神经网络(LSTM)模型 误差倒数法 

分 类 号：V355.1[航空宇航科学与技术—人机与环境工程]