摘要
In order to improve the accuracy and stability of terminal traffic flow prediction in convective weather,a multi-input deep learning(MICL)model is proposed.On the basis of previous studies,this paper expands the set of weather characteristics affecting the traffic flow in the terminal area,including weather forecast data and Meteorological Report of Aerodrome Conditions(METAR)data.The terminal airspace is divided into smaller areas based on function and the weather severity index(WSI)characteristics extracted from weather forecast data are established to better quantify the impact of weather.MICL model preserves the advantages of the convolution neural network(CNN)and the long short-term memory(LSTM)model,and adopts two channels to input WSI and METAR information,respectively,which can fully reflect the temporal and spatial distribution characteristics of weather in the terminal area.Multi-scene experiments are designed based on the real historical data of Guangzhou Terminal Area operating in typical convective weather.The results show that the MICL model has excellent performance in mean squared error(MSE),root MSE(RMSE),mean absolute error(MAE)and other performance indicators compared with the existing machine learning models or deep learning models,such as Knearest neighbor(KNN),support vector regression(SVR),CNN and LSTM.In the forecast period ranging from 30 min to 6 h,the MICL model has the best prediction accuracy and stability.
为提高对流天气下终端区流量预测的准确性和稳定性,提出了一种多输入深度学习模型(Multiinput deep learning,MICL)。在前人研究的基础上,扩展了影响终端区交通流的天气特征集,涵盖天气预报数据和机场气象报告(Meteorological Report of Aerodrome Conditions,METAR)数据。将终端空域根据功能划分为较小的空域,并通过天气预报数据建立天气危险指数(Weather severity index,WSI)特征,以更好地量化天气的影响。MICL模型结合了卷积神经网络(Convolution neural network,CNN)和长短期记忆网络(Long shortterm memory,LSTM)模型的优点,采用双通道分别输入WSI数据和METAR报告数据,可以充分反映终端区天气的时间与空间分布特征。以广州终端区在典型对流天气下运行的真实历史数据设计多场景实验,结果表明MICL模型与K近邻算法(Knearest neighbor,KNN)、支持向量回归(Support vector regression,SVR)、CNN、LSTM等既有机器学习或深度学习模型相比,在均方误差(Mean squared error,MSE)、均方根误差(Root MSE,RMSE)和平均绝对误差(Mean absolute error,MAE)等性能指标上表现优秀,在30 min至6 h不等的预测时间范围内均具有最佳的预测精度和稳定性。
基金
supported by the Civil Aviation Safety Capacity Building Project.
作者简介
Corresponding author:彭瑛,received the B.S.degree in air traffic management and dispatch from Nanjing University of Aeronautics and Astronautics(NUAA)in 2003 and the Ph.D.degree in air traffic management and dispatch from NUAA in 2014.She joined the College of Civil Aviation,NUAA in 2003.She is currently the director of the Airspace and Flow Collaborative Research Office of the National Air Traffic Control Flight Flow Management Technology Key Laboratory.Her research is focused on airspace capacity assessment and relevant fields.E-mail address:py423@sina.com.
