Deep Learning From Trajectory Data: a Review of Deep Neural Networks and the Trajectory Data Representations to Train Them

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).