# A Review on Flight Delay Prediction

arXiv: https://arxiv.org/abs/1703.06118 · HTML: https://mlsanity.com/paper/1703.06118/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2017.

## Built on by (most cited first, 4 of 4)

- [Airport Delay Prediction with Temporal Fusion Transformers](https://mlsanity.com/paper/2405.08293/) (2024)
- [A Modern Approach to Real-Time Air Traffic Management System](https://mlsanity.com/paper/2504.03652/) (2025)
- [Queue up for takeoff: a transferable deep learning framework for flight delay prediction](https://mlsanity.com/paper/2507.09084/) (2025)
- [Aeolus: A Multi-structural Flight Delay Dataset](https://mlsanity.com/paper/2510.26616/) (2025)

## Abstract (arXiv)

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 urlhttps://doi.org/10.1080/01441647.2020.1861123. Please cite as: L. Carvalho, A. Sternberg, L. Maia Gonccalves, 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