Using Machine Learning to Predict Airport Passenger Throughput

Samuel Yi, Jiang Guo
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Abstract

The American commercial airline industry is a crucial part of United States infrastructure and is so large and widespread that it affects all of its citizens in one way or another. There are many moving pieces involved in this industry, but we believe that we can make a significant impact when it comes to forecasting future passenger throughput. We look to utilize machine learning to create a prediction model which can eventually be used by the Department of Homeland Security to improve security and overall customer experience at airport terminals. The results of this study show that a polynomial regression model can provide utility as well as predictions with an acceptable margin of error.
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利用机器学习预测机场旅客吞吐量
美国商业航空业是美国基础设施的重要组成部分,规模庞大,分布广泛,以这样或那样的方式影响着所有公民。这个行业有很多变数,但我们相信,在预测未来的旅客吞吐量方面,我们可以产生重大影响。我们希望利用机器学习来创建一个预测模型,最终可以被国土安全部用于改善机场航站楼的安全性和整体客户体验。本研究的结果表明,多项式回归模型可以提供效用以及具有可接受的误差范围的预测。
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ARCH-COMP23 Category Report: Hybrid Systems Theorem Proving ARCH-COMP23 Category Report: Continuous and Hybrid Systems with Linear Continuous Dynamics ARCH-COMP23 Category Report: Continuous and Hybrid Systems with Nonlinear Dynamics ARCH-COMP23 Repeatability Evaluation Report ARCH-COMP23 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants
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