MUSE - Time Savings and Predictability for Emergency Deliveries Between Hospitals in Madrid

This dataset contains all input, processed, and final data used in the article “Unmanned Aircraft for Emergency Deliveries Between Hospitals in Madrid: Estimating Time Savings and Predictability” (Ganić, E.; Barrado, C.; Krstić Simić, T.; Kuljanin, J.; Baena, M. Drones 2025, 9, 728. https://doi.org/10.3390/drones9110728). The dataset enables full reproducibility of the comparison between drone-based and road-based emergency deliveries, including the estimation of time savings, predictability, and related performance indicators for hospital-to-hospital transport in Madrid. The results correspond to the MUSE performance indicator “AE-2: Reduced travel time for health care-related deliveries”, which quantifies the amount of time reduced for healthcare-related deliveries by UAs compared to the delivery by road transport during the observed time period. The dataset is organised into the following components: Hospital data – coordinates and names of all hospitals included in the analysis. Raw Google Routes API data – hourly road travel time JSON responses collected for seven consecutive days (04–10 May 2026) for all selected hospital pairs and directions. Processed road travel time tables – CSV files with aggregated travel times derived from the API responses. Drone travel time datasets – CSV file with estimated min, mean and max travel times for DJI Matrice 600 and RigiTech Eiger drone models used in the comparison obtained using GEMMA tool. Final indicator calculations – Excel and csv files containing the computed metrics (time savings, predictability, and AE-2 indicator values). All files are provided in open, interoperable formats (JSON, CSV, XLSX).