Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior. As data availability and computing power have increased, so has the popularity of deep learning from trajectory data. This paper aims to provide an overview of deep neural networks designed to learn from trajectory data, focusing on recent work published between 2020 and 2022. We take a data-centric approach and distinguish between deep learning models trained using dense trajectories (quasi-continuous tracking data), sparse trajectories (such as check-in data), and aggregated trajectories (crowd information).
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