Locating automated external defibrillators in urban areas: considering different travel modes

Out-of-hospital cardiac arrest (OHCA) is a time-critical emergency in which rapid access to automated external defibrillators (AEDs) can improve survival. However, most AED location studies assume a single travel mode and often use population alone as a proxy for demand. This study investigates how travel-mode assumptions affect AED accessibility and optimal deployment and develops a risk-informed multimodal spatial optimization framework.

Using Wuhan, China, as a case study, an age-risk-weighted potential OHCA demand surface was constructed from 17,842 ambulance dispatch records and age-stratified population data across 1,161 1-km grid cells. The existing 3,569 AEDs were treated as fixed facilities. Accessibility was evaluated under driving, cycling, walking, Drive + Walk, and Cycling + Walk scenarios using a 4-min response threshold. Multimodal extensions of the Location Set Covering Problem (LSCP) and Maximal Covering Location Problem (MCLP) were applied to identify full-coverage requirements and priority locations.

Baseline demand-weighted coverage varied substantially by travel mode: 85.07% for driving, 68.27% for cycling, 31.55% for walking, 53.65% for Drive + Walk, and 45.41% for Cycling + Walk. The additional AEDs required for universal coverage ranged from 68 under driving to 521 under walking, with 327 required under Drive + Walk. Under the first-phase MCLP strategy, 65 new AEDs increased Drive + Walk demand-weighted coverage to 73.31%. Sensitivity analysis confirmed that the main findings were robust to alternative mode weights.

AED accessibility and optimal deployment are highly sensitive to responder mobility assumptions. Incorporating heterogeneous travel modes and age-specific OHCA risk can support more realistic, spatially targeted, and resource-efficient AED planning.