More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation Learning
This work addresses trajectory representation learning for applications like navigation and urban planning, but it is incremental as it builds on existing routing-based methods by incorporating GPS details.
The paper tackles the problem of noise in GPS trajectories by proposing a joint GPS and route modeling framework (JGRM) that fuses information from both modalities using self-supervised tasks, resulting in improved performance over existing methods in road segment and trajectory representation tasks.
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.