Enhanced Generation of Human Mobility Trajectory with Multiscale Model

Over the past three years, the COVID-19 pandemic has highlighted the importance of understanding how people travel in contemporary urban areas in order to produce high-quality policies for public health emergencies. In this paper, we introduce a multiscale generative model called MScaleGAN that generates human mobility trajectories. Unlike existing models where both location and time were discretized, resulting in generated results that were concentrated on certain points, MScaleGAN can produce trajectories with higher detail for better capturing urban road systems spatially and human behaviors’ irregularity temporally. Experimental results show that our method generates better performance than previous models based on distribution similarities of individual and collective metrics compared with real GPS trajectories. Furthermore, we study the application of MScaleGAN on COVID-19 spread simulation and find that the spreading process under generated trajectories is similar to that under real data.