  Representation learning of pedestrian trajectories transforms variable-length
timestamp-coordinate tuples of a trajectory into a fixed-length vector
representation that summarizes spatiotemporal characteristics. It is a crucial
technique to connect feature-based data mining with trajectory data. Trajectory
representation is a challenging problem, because both environmental constraints
(e.g., wall partitions) and temporal user dynamics should be meticulously
considered and accounted for. Furthermore, traditional sequence-to-sequence
autoencoders using maximum log-likelihood often require dataset covering all
the possible spatiotemporal characteristics to perform well. This is infeasible
or impractical in reality. We propose TREP, a practical pedestrian trajectory
representation learning algorithm which captures the environmental constraints
and the pedestrian dynamics without the need of any training dataset. By
formulating a sequence-to-sequence autoencoder with a spatial-aware objective
function under the paradigm of actor-critic reinforcement learning, TREP
intelligently encodes spatiotemporal characteristics of trajectories with the
capability of handling diverse trajectory patterns. Extensive experiments on
both synthetic and real datasets validate the high fidelity of TREP to
represent trajectories.
