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This paper presents a schedule-informed Temporal Fusion Transformer framework for forecasting hourly airport security-checkpoint throughput, using flight schedules converted into temporally aligned screening-load signals. The model achieves lower prediction errors compared to RNN and LSTM baselines on Atlanta airport data.