Spatio-Temporal Data Mining for Aviation Delay Prediction

TL;DR

This paper introduces a novel LSTM-based system for predicting aviation delays by leveraging large-scale spatio-temporal data, including en-route spatial information and historical trajectories, to improve accuracy over previous methods.

Contribution

The paper presents a new delay prediction system using stacked LSTM networks that incorporate en-route spatial data and historical trajectories, enhancing prediction accuracy.

Findings

More robust delay predictions at large hub airports.

Improved accuracy over previous data-driven methods.

Effective integration of climatic, air traffic, and human factors.

Abstract

To accommodate the unprecedented increase of commercial airlines over the next ten years, the Next Generation Air Transportation System (NextGen) has been implemented in the USA that records large-scale Air Traffic Management (ATM) data to make air travel safer, more efficient, and more economical. A key role of collaborative decision making for air traffic scheduling and airspace resource management is the accurate prediction of flight delay. There has been a lot of attempts to apply data-driven methods such as machine learning to forecast flight delay situation using air traffic data of departures and arrivals. However, most of them omit en-route spatial information of airlines and temporal correlation between serial flights which results in inaccuracy prediction. In this paper, we present a novel aviation delay prediction system based on stacked Long Short-Term Memory (LSTM) networks for commercial flights. The system learns from historical trajectories from automatic dependent surveillance-broadcast (ADS-B) messages and uses the correlative geolocations to collect indispensable features such as climatic elements, air traffic, airspace, and human factors data along posterior routes. These features are integrated and then are fed into our proposed regression model. The latent spatio-temporal patterns of data are abstracted and learned in the LSTM architecture. Compared with previous schemes, our approach is demonstrated to be more robust and accurate for large hub airports.