机场实时容量提取和深度融合时空分布预测建模

Airport Real-Time Capacity Extraction and Deep Ensemble Spatiotemporal Distribution Prediction Modeling

对于民航运输的网络式运输结构,评估单一机场运行容量仅可用于航班计划安排,而预测机场网络容量的时空分布对航班运行控制具备指导意义。为了反映复杂天气对机场实时容量的影响,采用我国民航天气和航班运行大数据,运用数据挖掘技术提取机场实时容量,研究天气与容量的非线性映射关系;运用神经网络等多种机器学习算法建立将天气时空分布预测转换为机场容量时空分布预测的模型;并在分析单一模型预测结果的基础上,运用神经网络加权特征融合层构建深度融合多种算法的集成预测模型,进一步提升机场实时容量预测的准确率。最后通过华东地区机场网络多季节天气数据,验证模型在复杂天气条件下预测机场实时容量的准确率。研究发现:各模型的预测精度随着单位时间的增加而减少,深度神经网络对非线性天气特征的学习能力较强,通过融合集成学习构建深度神经网络有效提升了机场实时容量的预测精度,满足在短时间内获得未来机场实时容量的要求,可以为民航运输提供决策依据,减少航班延误。

For the network structure of civil aviation transportation,evaluating the operation capacity of a single airport can only be used for flight scheduling,while predicting the spatiotemporal distribution of airport network capacity has guiding significance for flight operation control.In order to reflect the impact of complex weather on the airport real-time capacity,the big data of weather and flight operations of China civil aviation was adopted,and the data mining technology was used to extract the real-time capacity of the airport.And the nonlinear mapping relationship between weather and capacity was also studied.A model that converted weather spatiotemporal distribution prediction into airport capacity spatiotemporal distribution prediction was established by using various machine learning algorithms such as neural networks.Moreover,on the basis of analyzing the prediction results of a single model,the neural network weighted feature fusion layer was used to construct a deep ensemble prediction model,which deeply integrated multiple algorithms to further improve the accuracy of real-time airport capacity prediction.Finally,the multi-seasonal weather data of airport network in Eastern China was used to verify the accuracy of the proposed model in predicting the real-time capacity of the airport under complex weather conditions.It is found by the research that the prediction accuracy of each model decreases with the increase of unit time,and the deep neural network has a strong ability to learn nonlinear weather characteristics.The construction of deep neural network through fusion ensemble learning has effectively improved the prediction accuracy of real-time airport capacity.The proposed deep ensemble prediction model satisfies the requirements of obtaining the real-time capacity of future airports in a short time,which can provide decision-making basis for civil aviation transportation and reduce flight delays.It satisfies the requirement of obtaining the real-time capacity of future airports in a short time,and can provide decision basis for the dynamic operation of the airport and reduce flight delays.