Aircraft Trajectory Segmentation-based Contrastive Coding: A Framework for Self-supervised Trajectory Representation

TL;DR

This paper presents ATSCC, a self-supervised framework for aircraft trajectory representation learning that improves classification and clustering performance without relying on labeled data or predefined inputs.

Contribution

It introduces a novel segmentation-based contrastive coding method that captures semantic information in air traffic trajectories for the first time.

Findings

ATSCC outperforms existing methods in classification and clustering tasks.

The framework is adaptable to various airport configurations.

ATSCC works effectively with incomplete trajectories.

Abstract

Air traffic trajectory recognition has gained significant interest within the air traffic management community, particularly for fundamental tasks such as classification and clustering. This paper introduces Aircraft Trajectory Segmentation-based Contrastive Coding (ATSCC), a novel self-supervised time series representation learning framework designed to capture semantic information in air traffic trajectory data. The framework leverages the segmentable characteristic of trajectories and ensures consistency within the self-assigned segments. Intensive experiments were conducted on datasets from three different airports, totaling four datasets, comparing the learned representation's performance of downstream classification and clustering with other state-of-the-art representation learning techniques. The results show that ATSCC outperforms these methods by aligning with the labels defined by aeronautical procedures. ATSCC is adaptable to various airport configurations and scalable to incomplete trajectories. This research has expanded upon existing capabilities, achieving these improvements independently without predefined inputs such as airport configurations, maneuvering procedures, or labeled data.