More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation Learning

TL;DR

This paper introduces JGRM, a self-supervised framework that jointly models GPS data and routes to improve trajectory representation learning by capturing motion details often ignored by traditional routing-based methods.

Contribution

The paper proposes a novel joint modeling framework using self-supervised learning and inter-modal interaction to enhance trajectory representations.

Findings

JGRM outperforms existing methods in road segment representation.

JGRM improves trajectory representation accuracy.

The approach effectively captures motion details in GPS data.

Abstract

Trajectory representation learning plays a pivotal role in supporting various downstream tasks. Traditional methods in order to filter the noise in GPS trajectories tend to focus on routing-based methods used to simplify the trajectories. However, this approach ignores the motion details contained in the GPS data, limiting the representation capability of trajectory representation learning. To fill this gap, we propose a novel representation learning framework that Joint GPS and Route Modelling based on self-supervised technology, namely JGRM. We consider GPS trajectory and route as the two modes of a single movement observation and fuse information through inter-modal information interaction. Specifically, we develop two encoders, each tailored to capture representations of route and GPS trajectories respectively. The representations from the two modalities are fed into a shared transformer for inter-modal information interaction. Eventually, we design three self-supervised tasks to train the model. We validate the effectiveness of the proposed method on two real datasets based on extensive experiments. The experimental results demonstrate that JGRM outperforms existing methods in both road segment representation and trajectory representation tasks. Our source code is available at Anonymous Github.