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Flight time prediction for fuel loading decisions with a deep learning approach
Transportation Research Part C: Emerging Technologies ( IF 8.3 ) Pub Date : 2021-05-08 , DOI: 10.1016/j.trc.2021.103179
Xinting Zhu , Lishuai Li

Under increasing economic and environmental pressure, airlines are constantly seeking new technologies and optimizing flight operations to reduce fuel consumption. However, the current practice on fuel loading, which has a significant impact on aircraft weight and fuel consumption, has yet to be thoroughly addressed by existing studies. Excess fuel is loaded by dispatchers and (or) pilots to handle fuel consumption uncertainties, primarily caused by flight time uncertainties, which cannot be predicted by current Flight Planning Systems (FPS). In this paper, we develop a novel spatial weighted recurrent neural network model to provide better flight time predictions by capturing air traffic information at a national scale based on multiple data sources, including Automatic Dependent Surveillance - Broadcast (ADS-B), Meteorological Aerodrome Reports (METAR), and airline records. In this model, a spatial weighted layer is designed to extract spatial dependences among network delay states (i.e. average flight delay at each airport and average flight delay of each Origin-Destination (OD) pair for a specific time interval). Then, a new training procedure associated with the spatial weighted layer is introduced to extract OD-specific spatial weights and then integrate into one model for a nationwide air traffic network. Long short-term memory (LSTM) networks are used after the spatial weighted layer to extract the temporal behavior patterns of network delay states. Finally, features from delays, weather, and flight schedules are fed into a fully connected neural network to predict the flight time of a particular flight. The proposed model was evaluated using one year of historical data from an airline’s real operations. Results show that our model can provide a more accurate flight time predictions than baseline methods, especially for flights with extreme delays. We also show that, with the improved flight time prediction, fuel loading can be optimized and resulting reduced fuel consumption by 0.016%–1.915% without increasing the fuel depletion risk.



中文翻译:

通过深度学习方法预测燃油装载量的飞行时间

在日益增加的经济和环境压力下,航空公司一直在寻求新技术并优化飞行操作以减少油耗。但是,目前的燃油装载做法对飞机的重量和燃油消耗有重大影响,但现有研究尚未彻底解决。调度员和(或)飞行员装载了过多的燃油,以处理主要由飞行时间不确定性引起的燃油消耗不确定性,而当前的飞行计划系统(FPS)无法预测这些不确定性。在本文中,我们开发了一种新颖的空间加权递归神经网络模型,该模型通过捕获基于多个数据源的全国范围内的空中交通信息(包括自动相关监视-广播(ADS-B),气象机场报告(METAR)和航空公司记录。在该模型中,空间加权层被设计为提取网络延迟状态之间的空间相关性(即,每个机场在特定时间间隔内的平均航班延误和每个始发地(OD)对的平均航班延误)。然后,引入了一种与空间加权层相关的新训练程序,以提取特定于OD的空间权重,然后将其集成到全国空中交通网络的一个模型中。在空间加权层之后使用长短期记忆(LSTM)网络来提取网络延迟状态的时间行为模式。最后,来自延误,天气和航班时刻表的特征被输入到完全连接的神经网络中,以预测特定航班的飞行时间。该模型是根据航空公司实际运营的一年历史数据进行评估的。结果表明,与基线方法相比,我们的模型可以提供更准确的飞行时间预测,尤其是对于极端延误的飞行。我们还表明,通过改进的飞行时间预测,可以优化燃油装载量,从而在不增加燃油消耗风险的情况下将燃油消耗量降低0.016%–1.915%。

更新日期:2021-05-08
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