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This paper proposes a simulation-based methodology to generate augmented traffic datasets by replacing physical sensors with virtual ones, extending sensor coverage in urban networks while preserving traffic patterns.
This paper proposes a framework that uses entropy-based diagnostics to harmonize spatial and temporal feature representations, achieving substantial accuracy gains on large-scale spatiotemporal prediction tasks across urban traffic, meteorology, and epidemic datasets.