Using Machine Learning to Predict Airport Passenger Throughput

9 pages•Published: March 22, 2023

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.

Keyphrases: machine learning, passenger prediction, polynomial regression model

In: Ajay Bandi, Mohammad Hossain and Ying Jin (editors). Proceedings of 38th International Conference on Computers and Their Applications, vol 91, pages 146-154.

Links:	https://easychair.org/publications/paper/bk2D
	https://doi.org/10.29007/tkhf

BibTeX entry

@inproceedings{CATA2023:Using_Machine_Learning_Predict,
  author    = {Samuel Yi and Jiang Guo},
  title     = {Using Machine Learning to Predict Airport Passenger Throughput},
  booktitle = {Proceedings of 38th International Conference on Computers and Their Applications},
  editor    = {Ajay Bandi and Mohammad Hossain and Ying Jin},
  series    = {EPiC Series in Computing},
  volume    = {91},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/bk2D},
  doi       = {10.29007/tkhf},
  pages     = {146-154},
  year      = {2023}}

Download PDF Open PDF in browser