An urban modeling study is asking whether a fixed supply of automated external defibrillators, or AEDs, should stay in buildings, move with ride-hailing vehicles, or be split between the two. The central comparison is not about buying more devices. It reallocates the same AED supply between stationary locations and mobile units, then compares modeled access to represented cardiac-arrest events. The empirical analysis combines EMS-derived OHCA-proxy incidents, public stationary-AED inventories and vehicle-trip mobility data from New York City and Toronto. In an analytical benchmark, the response-distance measure for a second scenario is no greater than the measure for the first, with equality when event intensity is constant and events are uniform.
The constraint is the point
Keeping capacity fixed changes the question from where a city can add devices to how it should deploy the supply it already has. The study uses the same supply when it shifts devices between stationary and mobile roles, explicitly separating mobilization from simply adding AEDs. Its hybrid ratio runs from 0% to 100%, covering the stationary and fully mobile endpoints. Each policy is therefore a different allocation of a limited network, not a comparison between a small system and a larger one.
Three layers of city data
To represent demand and supply, the empirical analysis brings together EMS-derived OHCA-proxy incidents, public inventories of stationary AEDs and vehicle-trip mobility records from Toronto and New York City. OHCA is shorthand for out-of-hospital cardiac arrest; here, the incidents serve as proxies in the model rather than a reported clinical cohort. The result is a city-access exercise built around where events, fixed devices and moving vehicles are represented in the data.
How the comparison works
At each event, the hybrid policy takes the faster of two modeled options: the nearest remaining stationary AED or an available mobile AED. The evaluation runs across the full stationary-to-mobile range. For each ratio, it samples the corresponding share of vehicle trips and averages 10 independent mobility samples. The assumptions are intended to favor the stationary benchmark, which gives the comparison a defined baseline as mobile capacity is introduced.
A mathematical check, not a field result
The analytical result is narrower than a citywide performance claim. It states that the response-distance measure in the second scenario is no greater than in the first. Under constant event intensity and uniform event distribution, the two measures are equal. That statement is a benchmark for the model’s spatial comparison. It does not, on its own, identify a universally best mix of stationary and mobile AEDs, because the benchmark describes a relation between scenarios rather than a single operating policy.
What the model can tell planners
Read on its own terms, the study offers a way to compare access under a fixed budget. Stationary, mobile and hybrid policies can be judged with the same city data and the same total capacity, while the faster option is selected event by event. That makes the framework relevant to planners weighing whether mobility should complement fixed sites. It does not answer the separate question of what happens when a city adds AEDs, because added capacity is outside the comparison.
The unanswered operational questions
The model also leaves real-world operation open. A counterfactual policy comparison cannot establish how an implemented mobile-AED service would perform in dispatch, vehicle availability or driver behavior. Nor does modeled access establish a patient-level clinical effect. The evidence concerns response-time, distance and reliability measures around represented events, not survival or other outcomes. Those gaps matter when a planning result is translated into an emergency-response program.
Why the preferred mix may vary
The supplied analysis does not point to one ratio for every city. The policy comparison is built around city-specific event, AED and mobility data, and the modeled balance can depend on how those layers line up. In practice, the value of a mobile layer is therefore a question of allocation and local conditions, not a universal replacement for stationary coverage. The framework treats mobility as another way to use a fixed AED supply and makes that trade-off visible.
Paper data and sources
Original title: Let AEDs Move: Urban Mobility Enhanced Defibrillator Deployment
Authors: Bahar Dehqani Viniche, Sheng Liu, Nooshin Salari
Journal/Repository: arXiv
Status: Preprint, not yet peer-reviewed
First online: 2026-08-24
DOI: Not available
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