MobilityDL: A Review of Deep Learning From Trajectory Data
Anita Graser Anahid Jalali Jasmin Lampert Axel Weißenfeld Krzysztof Janowicz
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 review paper provides the first comprehensive overview of deep
learning approaches for trajectory data. We have identified eight specific
mobility use cases which we analyze with regards to the deep learning models
and the training data used. Besides a comprehensive quantitative review of the
literature since 2018, the main contribution of our work is the data-centric
analysis of recent work in this field, placing it along the mobility data
continuum which ranges from detailed dense trajectories of individual movers
(quasi-continuous tracking data), to sparse trajectories (such as check-in
data), and aggregated trajectories (crowd information).