  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.
