Analysis of Factors in Flight Delay

DOI

10.2991/mmsta-19.2019.36How to use a DOI?

Keywords

flight delay; multiple linear regression; modeling, prediction; machine learning

Abstract

With the advancement of air transport industry, airline travelling is becoming increasingly prevalent in recent years. Flight delay, however, has been a headache and costly issue for both air companies and travelers, leading to inconvenience in our daily life. We can't help asking: what's the major cause for flight delay? In this paper, visualization and multiple linear regression are implemented based on Chinese flight data. Temperature, previous delay rate, month and weekday are figured out as influential factors. Further improvements are made by extracting variables to explore the optimal regression mathematical model. Then, machine learning algorithms are introduced to make future predictions.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yiyang Xu
AU  - Luyao Liu
AU  - Xichen Gao
AU  - Fanyu Frank Zeng
PY  - 2019/12
DA  - 2019/12
TI  - Analysis of Factors in Flight Delay
BT  - Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019)
PB  - Atlantis Press
SP  - 170
EP  - 176
SN  - 2352-538X
UR  - https://doi.org/10.2991/mmsta-19.2019.36
DO  - 10.2991/mmsta-19.2019.36
ID  - Xu2019/12
ER  -