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

TL;DR

This paper introduces a deep learning method to detect Single Engine Taxiing (SET) in aircraft using ground trajectory data, enabling better environmental impact assessment despite limited proprietary data access.

Contribution

A novel deep learning approach for detecting SET operations from ground trajectory data, using open-source features, facilitating environmental analysis without proprietary data.

Findings

Deep learning accurately detects SET from trajectory data.

SET detection is feasible with open-source surveillance features.

Method supports environmental impact assessments.

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.