# Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty

arXiv: https://arxiv.org/abs/2605.23378 · HTML: https://mlsanity.com/paper/2605.23378/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 104 references that are in the map.
- First posted on arXiv 22 May 2026.

## Builds on (most cited first, 4 of 4)

- [Searching for Activation Functions](https://mlsanity.com/paper/1710.05941/) (2017)
- [A Survey on Metric Learning for Feature Vectors and Structured Data](https://mlsanity.com/paper/1306.6709/) (2013)
- [Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting](https://mlsanity.com/paper/1707.01926/) (2017)
- [On the Complexity of Finding Stationary Points in Nonconvex Simple Bilevel Optimization](https://mlsanity.com/paper/2507.23155/) (2025)

## Abstract (arXiv)

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.