Large-scale Human Mobility Data Regeneration for Open Urban Research

Large-scale human mobility data contains rich spatial and temporal information for urban sensing, crowd flow modeling, and urban planning. However, it is usually difficult to access wide-coverage, long-term, and consistent-time human mobility data. Most of the publicly available datasets are actually only records of discontinuous trajectories of a very small portion of urban citizens in asynchronous time due to the limited usage of apps for location data collection or the limited number of volunteers. To address this problem and empower open urban research, this paper constructs a high-quality human mobility dataset by generating large-scale citizen trajectories based on massive cellular signaling data. Particularly, we first propose a heatmap diffusion module to generate a probability heatmap that produces plausible trajectories at both the individual and city scales. Then, we propose a masked trajectory AutoEncoder, which can generate individual trajectory embeddings from partially given or empty trajectories. Third, a flexible framework is provided to incorporate the heatmap diffusion module with the masked trajectory embeddings, demonstrating significant flexibility in handling both fully masked trajectories for city-wide analysis and partially masked trajectories for specific locations. We have conducted extensive experiments to validate the utility of the regenerated trajectories at both individual and region levels for various applications. Numerous case studies further illustrate that our model learns not only the distribution of the trajectories but also the semantics of different urban areas.