Motivated by the large number of wearables offering geolocation, human mobility mining has emerged as an novel research field within AI. The study of mobility creates increasingly predictable models in which it is easy to find patterns of behaviour. However, this data is not publicly available and access to it is restricted to large telecommunications operators. In this context, this paper aims to solve one of the main problems of human mobility databases, i.e. the scarcity of data for the generation of human mobility models. For this purpose, Generative adversarial network (GANs) have been proposed to generate synthetic time-series mobility data. Moreover, several neural network models are proposed to assess the impact of synthetic data generation on the prediction of human mobility. Our results show that the use of synthetic data improves predictions of human mobility compared to models based on available measured data. Specifically, the reinforcement learning with synthetic data benchmark, when compared to using only ground truth data, achieved a 1.22% improvement in R², a 0.70% reduction in RMSE, a 2.97% decrease in MAE, a 27.07% reduction in MAPE, and an 18.18% improvement in CVRMSE, demonstrating its effectiveness in enhancing predictive accuracy.

Deep Learning, Synthetic time series data generation, Generative Adversary Networks, Graph Neural Networks, Time series forecasting, People mobility forecasting