On the Detection of Aircraft Single Engine Taxi using Deep Learning Models

TL;DR

This paper presents a novel deep learning approach to detect SET operations using ground trajectory data of A320 flights, and demonstrates that SET can be inferred from ground movement patterns, paving the way for future work with non-proprietary data sources.

Abstract

The aviation industry is vital for global transportation but faces increasing pressure to reduce its environmental footprint, particularly CO2 emissions from ground operations such as taxiing. Single Engine Taxiing (SET) has emerged as a promising technique to enhance fuel efficiency and sustainability. However, evaluating SET's benefits is hindered by the limited availability of SET-specific data, typically accessible only to aircraft operators. In this paper, we present a novel deep learning approach to detect SET operations using ground trajectory data. Our method involves using proprietary Quick Access Recorder (QAR) data of A320 flights to label ground movements as SET or conventional taxiing during taxi-in operations, while using only trajectory features equivalent to those available in open-source surveillance systems such as Automatic Dependent Surveillance-Broadcast (ADS-B) or ground radar. This demonstrates that SET can be inferred from ground movement patterns, paving the way for future work with non-proprietary data sources. Our results highlight the potential of deep learning to improve SET detection and support more comprehensive environmental impact assessments.

Figures (6)

• Figure 1: Nm-Dp Conv1D Neural network architecture diagram is displayed at the bottom. On top Conv1D and Dense blocks are displayed.
• Figure 2: The best classification threshold is computed with F1 score curve on validation set.
• Figure 3: Confusion matrix of the best model on test set.
• Figure 4: Confusion matrix of the best model on the generalization set composed of 1910 A319 trajectories.
• Figure 5: Distribution of SET beginning index and predictions.