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).本数据集包含发表于《马德里医院间应急配送无人机：时间节省量与可预测性评估（Unmanned Aircraft for Emergency Deliveries Between Hospitals in Madrid: Estimating Time Savings and Predictability）》（作者：Ganić, E.、Barrado, C.、Krstić Simić, T.、Kuljanin, J.、Baena, M.，刊载于期刊《Drones》2025年第9卷第728篇文章，DOI：10.3390/drones9110728）一文的全部输入数据、处理后数据与最终产出数据。 本数据集可完整复现无人机（Unmanned Aircraft, UAs）配送与陆路应急配送的对比分析，涵盖马德里地区院际医疗运输的时间节省量、可预测性及相关性能指标的估算。其计算结果对应MUSE性能指标"AE-2：医疗相关配送行程时间缩减"，该指标量化了观测时段内，无人机相较陆路运输的医疗配送行程时间缩减幅度。 数据集包含以下组成部分： 1. 医院数据：分析纳入的全部医院的坐标与名称。 2. 原始谷歌路线API（Google Routes API）数据：针对2026年5月4日至10日连续7天的所有选定医院对及通行方向，采集的每小时陆路行程时间JSON响应结果。 3. 处理后陆路行程时间表：由API响应汇总得到的聚合行程时间CSV文件。 4. 无人机行程时间数据集：采用GEMMA工具计算得到的、用于对比分析的两款机型——大疆（DJI）Matrice 600与RigiTech Eiger无人机的估算最小、平均及最大行程时间CSV文件。 5. 最终指标计算结果：包含已计算的各项指标（时间节省量、可预测性及AE-2指标值）的Excel与CSV文件。 所有文件均采用开放、可互操作的格式（JSON、CSV、XLSX）。