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Supervised Neural Network with multilevel input layers for predicting of air traffic delays

机译：具有多层输入层的监督神经网络，用于预测空中交通延误

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摘要

Air delay is a problem in most airports around the world, resulting in increased costs for airlines and discomfort for passengers. Air Traffic Flow Management (ATFM) programs were implemented with the main objective to reduce the delay levels in the whole air transportation sector. The question is to find a suitable way to predict possible delay scenarios to better apply ATFM measures. The present work seeks to enrich the academic literature on the subject and aims to present the application of Artificial Neural Networks (ANN) to a prediction model of delays in the air route between São Paulo (Congonhas) - Rio de Janeiro (Santos Dumont). The configuration of ANN exerts a great influence on its predictive power. To better adjust the parameters of the proposed ANN and for the hyperparameterization of the network to occur, the Random Search technique is used. By using the recall, precision and Fscore metrics in the performance measurement, the prediction results show the satisfactory in the case study.
机译：空中延误是世界上大多数机场的一个问题，导致航空公司成本增加和乘客不适。实施空中交通流量管理（ATFM）计划的主要目的是减少整个航空运输部门的延误水平。问题是找到一种预测可能的延迟情况的合适方法，以更好地应用空中交通流量管理措施。本工作旨在丰富有关该主题的学术文献，旨在介绍人工神经网络（ANN）在圣保罗（Congonhas）-里约热内卢（Santos Dumont）之间的航线延误的预测模型中的应用。人工神经网络的配置对其预测能力有很大的影响。为了更好地调整所提出的人工神经网络的参数并发生网络的超参数化，使用了随机搜索技术。通过在性能测量中使用召回率，精度和Fscore指标，预测结果在案例研究中显示出令人满意的效果。

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