# Machine Learning for Air Transport Planning and Management

arXiv: https://arxiv.org/abs/2112.01301 · HTML: https://mlsanity.com/paper/2112.01301/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 36 references that are in the map.
- First posted on arXiv 30 Nov 2021.

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

- [Applications of natural language processing in aviation safety: A review and qualitative analysis](https://mlsanity.com/paper/2501.06210/) (2025)
- [Sequential Classification of Aviation Safety Occurrences with Natural Language Processing](https://mlsanity.com/paper/2501.06490/) (2025)
- [Natural Language Processing and Deep Learning Models to Classify Phase of Flight in Aviation Safety Occurrences](https://mlsanity.com/paper/2501.06564/) (2025)
- [Utilizing AI for Aviation Post-Accident Analysis Classification](https://mlsanity.com/paper/2506.00169/) (2025)

## Abstract (arXiv)

In this work we compare the performance of several machine learning algorithms applied to the problem of modelling air transport demand. Forecasting in the air transport industry is an essential part of planning and managing because of the economic and financial aspects of the industry. The traditional approach used in airline operations as specified by the International Civil Aviation Organization is the use of a multiple linear regression (MLR) model, utilizing cost variables and economic factors. Here, the performance of models utilizing an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), a genetic algorithm, a support vector machine, and a regression tree are compared to MLR. The ANN and ANFIS had the best performance in terms of the lowest mean squared error.