  In this work, we develop a privacy-by-design generative model for
synthesizing the activity diary of the travel population using state-of-art
deep learning approaches. This proposed approach extends literature on
population synthesis by contributing novel deep learning to the development and
application of synthetic travel data while guaranteeing privacy protection for
members of the sample population on which the synthetic populations are based.
First, we show a complete de-generalization of activity diaries to simulate the
socioeconomic features and longitudinal sequences of geographically and
temporally explicit activities. Second, we introduce a differential privacy
approach to control the level of resolution disclosing the uniqueness of survey
participants. Finally, we experiment using the Generative Adversarial Networks
(GANs). We evaluate the statistical distributions, pairwise correlations and
measure the level of privacy guaranteed on simulated datasets for varying
noise. The results of the model show successes in simulating activity diaries
composed of multiple outputs including structured socio-economic features and
sequential tour activities in a differentially private manner.
