IDEAL is a selective dual ambulance dispatch framework that learns context-specific travel times via weakly supervised bilevel networks and models uncertainty with Burg-divergence perturbations to achieve better response-time and resource trade-offs than region-based or map-based baselines.
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Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty
IDEAL is a selective dual ambulance dispatch framework that learns context-specific travel times via weakly supervised bilevel networks and models uncertainty with Burg-divergence perturbations to achieve better response-time and resource trade-offs than region-based or map-based baselines.