Differentiable generative models for trajectory data analytics - Li, Xiucheng (Nanyang Technological University, 2020)
With the proliferation of GPS-enabled devices, trajectory data is being generated at an unprecedented speed. The trajectories are typically represented as sequences of discrete sample points, which carry rich spatiotemporal information. Mining patterns and distilling knowledge from such a large amount of trajectory data could potentially help address many real-world problems and improve our daily life experience. For instance, accurately and eﬃciently quantifying the similarity between two trajectories is a foundation for many trajectory based applications, such as tracking migration patterns of animals, mining hot routes in cities, trajectory clustering and moving group discovery.