The invention belongs to the technical field of flight delay prediction and analysis, and discloses a regression analysis method for predicting an air traffic flow management delay value based on deep learning, and the method comprises the following steps: obtaining a flight data set; selecting features in the data set, and performing data cleaning; dividing the data set, and processing the data set into format requirements required by the model; the method comprises the following steps: constructing a TCNLSTMattention model; a sparrow search algorithm is used to adjust and optimize model hyper-parameters; training the model through the training set, and verifying the model through the verification set; and predicting the test set by using the optimized model, and comparing a prediction result with an actual result to evaluate the performance of the model. According to the method, the air traffic flow management delay value is predicted by using the neural network algorithm, the future flight air traffic flow management delay value is predicted, the possibility and the burstiness of flight delay are reduced, and the operation efficiency and the accuracy of an airline company are improved.
本发明属于航班延误预测及分析技术领域,公开了一种基于深度学习的预测空中交通流量管理延误值的回归分析方法,包括如下步骤:获取航班数据集;对数据集中特征进行选择,并进行数据清洗;对数据集进行划分,并处理成模型所需的格式要求;构建TCN_LSTM_attention模型;使用麻雀搜索算法对模型超参数进行调优;通过训练集对模型进行训练,通过验证集对模型进行验证;使用优化过的模型对测试集进行预测,并将预测结果与实际结果进行对比,以评估模型的性能。本发明通过使用神经网络算法预测空中交通流量管理延误值,预测未来航班空中交通流量管理延误值,降低航班延误的可能性和突发性,提高航空公司的运营效率和准确性。
Regression analysis method for predicting air traffic flow management delay value based on deep learning
一种基于深度学习的预测空中交通流量管理延误值的回归分析方法
2024-01-02
Patent
Electronic Resource
Chinese
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