Generation of synthetic delay time series for air transport applications

TL;DR

This paper compares deep learning models and a genetic algorithm for generating realistic synthetic airport delay time series, demonstrating the genetic algorithm's effectiveness and providing data for further research.

Contribution

It introduces a simple genetic algorithm approach for generating realistic delay time series and validates its effectiveness against deep learning models.

Findings

Genetic algorithm produces nearly indistinguishable delay series from real data.

Deep learning models are compared but the genetic algorithm shows high variability and realism.

Synthetic data is made publicly available for research use.

Abstract

The generation of synthetic data is receiving increasing attention from the scientific community, thanks to its ability to solve problems like data scarcity and privacy, and is starting to find applications in air transport. We here tackle the problem of generating synthetic, yet realistic, time series of delays at airports, starting from large collections of operations in Europe and the US. We specifically compare three models, two of them based on state of the art Deep Learning algorithms, and one simplified Genetic Algorithm approach. We show how the latter can generate time series that are almost indistinguishable from real ones, while maintaining a high variability. We further validate the resulting time series in a problem of detecting delay propagations between airports. We finally make the synthetic data available to the scientific community.