Leveraging Deep Learning for Trajectory Similarity Learning and Trajectory Pathlet Dictionary Construction

The rapid development of geospatial technologies and location-based devices have motivated the research community of trajectory data mining, due to numerous applications including route planning and navigation services. Of interest are similarity search tasks that several works addressed through representation learning. Our method ST2Box offers refined representations by first representing trajectories as sets of roads, then adapting set-to-box architectures for learning accurate, versatile, and generalizable set representations of trajectories for preserving similarity. Experimentally, ST2Box outperforms baselines by up to ~38%.

Another related problem involves constructing small sets of building blocks that can represent wide-ranging trajectories (pathlet dictionaries). However, currently-existing methods in constructing PDs are memory-intensive. Thus, we propose PathletRL for generating dictionaries that offer significant memory-savings. It initializes unit-length pathlets and iteratively merges them while maximizing utility -- that is approximated using deep reinforcement learning-based method. Empirically, PathletRL can reduce its dictionary's size by up to 65.8% against state-of-the-art methods.