# A Review on Flight Delay Prediction

## TL;DR

This paper provides a comprehensive review of flight delay prediction models, emphasizing the evolution of methods, especially machine learning, and offering a taxonomy and timeline of key research developments in the field.

## Contribution

It introduces a taxonomy for flight delay prediction approaches and presents a timeline highlighting research trends and the increasing use of machine learning techniques.

## Key findings

- Machine learning methods are increasingly used in flight delay prediction.
- The review categorizes approaches based on scope, data, and computational methods.
- A timeline of significant research illustrates evolving trends in the field.

## Abstract

Flight delays hurt airlines, airports, and passengers. Their prediction is crucial during the decision-making process for all players of commercial aviation. Moreover, the development of accurate prediction models for flight delays became cumbersome due to the complexity of air transportation system, the number of methods for prediction, and the deluge of flight data. In this context, this paper presents a thorough literature review of approaches used to build flight delay prediction models from the Data Science perspective. We propose a taxonomy and summarize the initiatives used to address the flight delay prediction problem, according to scope, data, and computational methods, giving particular attention to an increased usage of machine learning methods. Besides, we also present a timeline of significant works that depicts relationships between flight delay prediction problems and research trends to address them. The published version of this paper is made available at \url{https://doi.org/10.1080/01441647.2020.1861123}. Please cite as: L. Carvalho, A. Sternberg, L. Maia Gon\c{c}alves, A. Beatriz Cruz, J.A. Soares, D. Brand\~ao, D. Carvalho, e E. Ogasawara, 2020, On the relevance of data science for flight delay research: a systematic review, Transport Reviews