  In recent years, there has been a surge in the development of models for the
generation of synthetic mobility data. These models aim to facilitate the
sharing of data while safeguarding privacy, all while ensuring high utility and
flexibility regarding potential applications. However, current utility
evaluation methods fail to fully account for real-life requirements. We
evaluate the utility of five state-of-the-art synthesis approaches, each with
and without the incorporation of differential privacy (DP) guarantees, in terms
of real-world applicability. Specifically, we focus on so-called trip data that
encode fine granular urban movements such as GPS-tracked taxi rides. Such data
prove particularly valuable for downstream tasks at the road network level.
Thus, our initial step involves appropriately map matching the synthetic data
and subsequently comparing the resulting trips with those generated by the
routing algorithm implemented in OpenStreetMap, which serves as an efficient
and privacy-friendly baseline. Out of the five evaluated models, one fails to
produce data within reasonable computation time and another generates too many
jumps to meet the requirements for map matching. The remaining three models
succeed to a certain degree in maintaining spatial distribution, one even with
DP guarantees. However, all models struggle to produce meaningful sequences of
geo-locations with reasonable trip lengths and to model traffic flow at
intersections accurately. It is important to note that trip data encompasses
various relevant characteristics beyond spatial distribution, such as temporal
information, all of which are discarded by these models. Consequently, our
results imply that current synthesis models fall short in their promise of high
utility and flexibility.
