A Non-Parametric Generative Model for Human Trajectories

Modeling human mobility and generating synthetic yet realistic location trajectories play a fundamental role in many (privacy-aware) analysis and design processes that operate on location data. In this paper, we propose a non-parametric generative model for location trajectories that can capture high-order geographic and semantic features of human mobility. We design a simple and intuitive yet effective embedding for locations traces, and use generative adversarial networks to produce data points in this space, which will finally be transformed back to a sequential 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. Our empirical results prove the capability of our generative model in preserving various useful properties of real data.