Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty

• URL: http://arxiv.org/abs/2605.23378v1
• Date: Fri, 22 May 2026 08:46:21 GMT
• Title: Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty
• Abstract summary: Ambulance dispatch is time-critical in out-of-hospital arrest (OHCA), where dispatchers balance timely arrivals with limited fleet capacity.<n>We propose I (Intelligent Dual dispatch of Emergency AmbuLances) that sends a second ambulance only when primary and secondary paths exceed a threshold.
• Score: 16.225780811704656
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ambulance response is time-critical in out-of-hospital cardiac arrest (OHCA), where dispatchers must balance timely arrivals with limited fleet capacity. Static territories and deterministic travel-time estimates are vulnerable to dynamic congestion, while always-dual dispatch adds redundancy but consumes fleet capacity. We propose IDEAL (Intelligent Dual dispatch of Emergency AmbuLances), a selective dual-dispatch framework that sends a second ambulance only when the optimistic gap between primary and secondary paths exceeds a threshold. IDEAL learns context-specific edge travel times from trip-level dispatch records, including unobserved routes, using a weakly supervised bilevel representation network. We train the nonsmooth model with mini-batch conservative gradients and prove an asymptotic convergence guarantee. IDEAL models uncertainty via Burg-divergence perturbations to a shared metric in the learned representation space, thereby inducing correlated changes in edge travel times and learning context-specific radii from historical underprediction errors. For real-time decisions, IDEAL casts optimistic-gap computation as a difference-of-convex program and derives an efficient oracle with complexity guarantees. In collaboration with the Hong Kong Fire Services Department, we evaluate IDEAL using historical OHCA records and real-time adaptive simulations. The results achieve a stronger response-time/resource trade-off relative to all region-based and Google-based baselines.

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