MobilityDL: A Review of Deep Learning From Trajectory Data

TL;DR

This paper provides a comprehensive review of deep learning methods applied to trajectory data, analyzing various use cases and data types, and offering a data-centric perspective on recent advancements since 2018.

Contribution

It is the first to systematically review deep learning approaches for trajectory data and to analyze them along a mobility data continuum.

Findings

Identified eight key mobility use cases for deep learning.

Performed a quantitative review of literature since 2018.

Provided a data-centric analysis across different trajectory data types.

Abstract

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).