RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer. (arXiv:2211.13234v1 [cs.LG])

GPS trajectories are the essential foundations for many trajectory-based
applications, such as travel time estimation, traffic prediction and trajectory
similarity measurement. Most applications require a large amount of high sample
rate trajectories to achieve a good performance. However, many real-life
trajectories are collected with low sample rate due to energy concern or other
constraints.We study the task of trajectory recovery in this paper as a means
for increasing the sample rate of low sample trajectories. Currently, most
existing works on trajectory recovery follow a sequence-to-sequence diagram,
with an encoder to encode a trajectory and a decoder to recover real GPS points
in the trajectory. However, these works ignore the topology of road network and
only use grid information or raw GPS points as input. Therefore, the encoder
model is not able to capture rich spatial information of the GPS points along
the trajectory, making the prediction less accurate and lack spatial
consistency. In this paper, we propose a road network enhanced
transformer-based framework, namely RNTrajRec, for trajectory recovery.
RNTrajRec first uses a graph model, namely GridGNN, to learn the embedding
features of each road segment. It next develops a Sub-Graph Generation module
to represent each GPS point as a sub-graph structure of the road network around
the GPS point. It then introduces a spatial-temporal transformer model, namely
GPSFormer, to learn rich spatial and temporal features. It finally forwards the
outputs of encoder model into a multi-task decoder model to recover the missing
GPS points. Extensive experiments based on three large-scale real-life
trajectory datasets confirm the effectiveness of our approach.