Explainable Trajectory Representation through Dictionary Learning
Yuanbo Tang Zhiyuan Peng Yang Li
Trajectory representation learning on a network enhances our understanding of
vehicular traffic patterns and benefits numerous downstream applications.
Existing approaches using classic machine learning or deep learning embed
trajectories as dense vectors, which lack interpretability and are inefficient
to store and analyze in downstream tasks. In this paper, an explainable
trajectory representation learning framework through dictionary learning is
proposed. Given a collection of trajectories on a network, it extracts a
compact dictionary of commonly used subpaths called "pathlets", which optimally
reconstruct each trajectory by simple concatenations. The resulting
representation is naturally sparse and encodes strong spatial semantics.
Theoretical analysis of our proposed algorithm is conducted to provide a
probabilistic bound on the estimation error of the optimal dictionary. A
hierarchical dictionary learning scheme is also proposed to ensure the
algorithm's scalability on large networks, leading to a multi-scale trajectory
representation. Our framework is evaluated on two large-scale real-world taxi
datasets. Compared to previous work, the dictionary learned by our method is
more compact and has better reconstruction rate for new trajectories. We also
demonstrate the promising performance of this method in downstream tasks
including trip time prediction task and data compression.