  Location trajectories provide valuable insights for applications from urban
planning to pandemic control. However, mobility data can also reveal sensitive
information about individuals, such as political opinions, religious beliefs,
or sexual orientations. Existing privacy-preserving approaches for publishing
this data face a significant utility-privacy trade-off. Releasing synthetic
trajectory data generated through deep learning offers a promising solution.
Due to the trajectories' sequential nature, most existing models are based on
recurrent neural networks (RNNs). However, research in generative adversarial
networks (GANs) largely employs convolutional neural networks (CNNs) for image
generation. This discrepancy raises the question of whether advances in
computer vision can be applied to trajectory generation. In this work, we
introduce a Reversible Trajectory-to-CNN Transformation (RTCT) that adapts
trajectories into a format suitable for CNN-based models. We integrated this
transformation with the well-known DCGAN in a proof-of-concept (PoC) and
evaluated its performance against an RNN-based trajectory GAN using four
metrics across two datasets. The PoC was superior in capturing spatial
distributions compared to the RNN model but had difficulty replicating
sequential and temporal properties. Although the PoC's utility is not
sufficient for practical applications, the results demonstrate the
transformation's potential to facilitate the use of CNNs for trajectory
generation, opening up avenues for future research. To support continued
research, all source code has been made available under an open-source license.
