A Non-Parametric Generative Model for Human Trajectories

Modeling human mobility and synthesizing realistic trajectories play a fundamental role in urban planning and privacy-preserving location data analysis. Due to its high dimensionality and also the diversity of its applications, existing trajectory generative models do not preserve the geometric (and more importantly) semantic features of human mobility, especially for longer trajectories. In this paper, we propose and evaluate a novel non-parametric generative model for location trajectories that tries to capture the statistical features of human mobility {\em as a whole}. This is in contrast with existing models that generate trajectories in a sequential manner. We design a new representation of locations, and use generative adversarial networks to produce data points in that representation space which will be then transformed to a time-series location trajectory form. We evaluate our method on realistic location trajectories and compare our synthetic traces with multiple existing methods on how they preserve geographic and semantic features of real traces at both aggregated and individual levels. The empirical results prove the capability of our model in preserving the utility of real data.