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ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2024,11,21]]},"abstract":"<jats:p>Understanding crowd mobility is critical for many applications. In this paper, we propose CrowdMirage, a WiFi positioning-based crowd mobility digital twin for smart campuses. Specifically, we first design an end-to-end human mobility trace extraction pipeline from the comprehensive but noisy WiFi connection logs on a university campus. We then design two predictive and simulative models for the crowd flow prediction and simulation tasks, respectively. Considering the particularity of on-campus mobility, we propose a cross-grained crowd flow prediction model to forecast crowd flow at both building and floor levels. For crowd flow simulation, we design a conditional generative model based on conditional diffusion to simulate the crowd flow under given mobility-related contexts that are systematically identified. 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