基于DTW-LSTM的空中交通流量短期预测
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作者单位:

南京航空航天大学民航学院

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中图分类号:

V355

基金项目:

国家重点研发计划(2018YFE0208700)


Short-term Prediction of Air Traffic Flow Based on DTW-LSTM
Author:
Affiliation:

College of Civil Aviation,Nanjing University of aeronautics and astronautics

Fund Project:

National Key Research and Development Program of China under Grant No. 2018YFE0208700.

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    摘要:

    空中交通短期流量预测的准确性对于精准实施空中交通流量管理具有重要意义。为提高空中交通短期流量预测准确性,充分利用历史运行数据,本文提出了一种基于DTW-LSTM的空中交通流量短期预测方法。首先,分析了空中交通流的时空相关性特征,基于此特征采用DTW算法衡量扇区之间的空间相关性;然后,由空间相关性度量结果选取不同扇区的数据进行组合,构建输入时间序列长度不同的数据集,将历史时间数据输入LSTM模型中训练;最后,对不同时空参数组合模型的预测结果进行分析,与不考虑时空相关性LSTM模型、考虑时空特性的SVR模型的预测结果进行对比。实验结果表明,相比于传统方法,本文提出的空中交通流量短期预测方法通过考虑交通流的时空相关性,提高了预测结果的准确性,相比LSTM模型,MAE降低24.5%,RMSE降低31.4%,相比时空相关SVR模型,MAE降低36.4%,RMSE降低30.6%。

    Abstract:

    The accuracy of short-term air traffic flow forecast is of great significance for the accurate implementation of air traffic management. In order to improve the accuracy of short-term air traffic flow prediction and make full use of historical operation data, this paper proposes a short-term air traffic flow prediction method based on DTW-LSTM. Firstly, the spatio-temporal correlation of air traffic flow is analyzed, based on this feature the spatial correlation between sectors is measured by DTW algorithm. Then, different sectors’s datas were selected and combined according to the spatial correlation measurement results to construct data sets with different input time series lengths, which were input into the LSTM model for training. Finally, the prediction results of different spatio-temporal parameter combination models are analyzed and compared with the prediction results of SVR model without considering spatio-temporal correlation and LSTM model. The experimental results show that compared with the traditional method, the proposed short-term air traffic flow prediction method improves the accuracy of the prediction results by considering the temporal and spatial correlation of the traffic flow, and has a certain improvement compared with other model methods.

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宋维凯,张洪海,万俊强,等. 基于DTW-LSTM的空中交通流量短期预测[J]. 科学技术与工程, , ():

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  • 收稿日期:2021-10-25
  • 最后修改日期:2022-04-26
  • 录用日期:2022-04-30
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