基于ARMA-AE-LSTM模型的进场交通流预测方法  

Approach Traffic Flow Prediction Method Based on ARMA-AE-LSTM Model

作　　者：张召悦[1] 张红波 ZHANG Zhao-yue;ZHANG Hong-bo(College of Air Traffic Management,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学空中交通管理学院,天津300300

出　　处：《科学技术与工程》2024年第27期11919-11927,共9页Science Technology and Engineering

基　　金：中央高校基本科研业务费专项(3122022105)。

摘　　要：为建立准确有效的空中交通短期流量预测模型,提高终端区管理效率,以进场交通流为对象进行研究。首先采用自回归移动平均(autoregressive moving average,ARMA)模型对流量时间序列进行初步线性预测,然后通过长短期记忆网络(long short term memory,LSTM)模型对线性预测后的残差序列进行非线性修正预测。考虑到冗余特征会降低LSTM模型预测精度的问题,采用自编码器(autoencoder,AE)模型对LSTM模型的天气以及流量特征输入进行自适应压缩优化,最后设置对比实验对ARMA-AE-LSTM模型的准确性、鲁棒性以及时效性进行验证。实验结果表明:预测绝对误差在1.3架以内的占比达到75%;LSTM模型的平均每轮迭代时间降低为1.014 s;与其他常用深度学习预测模型相比,ARMA-AE-LSTM模型的均方根误差(root mean square error,RMSE)、平均绝对误差(mean absolute error,MAE)以及决定系数(r-squared,R2)评价指标分别改善了45.98%~67.66%、48.56%~67.35%、5.18%~21.07%;恶劣天气影响下,ARMA-AE-LSTM模型的鲁棒性更好。由此可见,该方法能够准确有效快速的预测空中交通流量。To develop an accurate and effective short-term air traffic flow prediction model to improve the efficiency of terminal area management,the arrival traffic flow was chosen as the research subject.Firstly,the ARMA(autoregressive moving average)model was adopted for initial linear prediction of the flow time series.Then,the residual sequence after linear prediction was subjected to non-linear correction using the LSTM(long short term memory)model.To address the issue of decreased prediction accuracy caused by redundant features,the AE(autoencoder)model was used to adaptively compress and optimize the input of weather and traffic flow features for the LSTM model.Finally,a comparative experiment was conducted to validate the accuracy,robustness,and timeliness of the ARMA-AE-LSTM model.Experimental results demonstrate that the proportion of predicted absolute errors within 1.3 aircraft reaches 75%.The average iteration time of the LSTM model is reduced to 1.014 seconds.When compared with other commonly used deep learning prediction models,the ARMA-AE-LSTM model improves the RMSE(root mean square error),MAE(mean absolute error),and R2(r-squared)evaluation metrics by 45.98%~67.66%,48.56%~67.35%,and 5.18%~21.07%,respectively.Furthermore,the ARMA-AE-LSTM model exhibits better robustness in adverse weather conditions.Thus,it can be concluded that this method enables accurate,effective,and rapid prediction of air traffic flow.

关 键 词：终端区 进场交通流 短期流量预测 深度学习 残差修正 

分 类 号：V355[航空宇航科学与技术—人机与环境工程]