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Article

Urban Medical Emergency Logistics Drone Base Station Location Selection

1
School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
2
China-Singapore International Joint Research Institute, Guangzhou 510555, China
3
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
4
Qianjin Street Community Hospital, Guangzhou 510655, China
5
Sinopharm Group Pharmaceutical Logistics Co., Ltd., Shanghai 200436, China
6
School of Architecture, South China University of Technology, Guangzhou 510641, China
*
Author to whom correspondence should be addressed.
Drones 2026, 10(1), 17; https://doi.org/10.3390/drones10010017 (registering DOI)
Submission received: 9 November 2025 / Revised: 25 December 2025 / Accepted: 26 December 2025 / Published: 28 December 2025

Abstract

In densely populated and traffic-congested major cities, medical emergency rescue incidents occur frequently, making the use of drones for emergency medical supplies delivery a new emergency distribution method. However, establishing drone transportation networks in urban areas requires balancing spatiotemporal fluctuations in emergency needs, meeting hospitals’ mandatory constraints on response time, and addressing factors like airspace restrictions and weather impacts. By analyzing the spatiotemporal distribution characteristics of medical emergency logistics in large cities, this study constructs a drone base station location optimization model integrating dynamic and static factors. The model combines multi-source data including emergency needs, geographic information, and airspace limitations. It employs kernel density estimation to identify hotspot areas, uses DBSCAN clustering to detect long-term stable demand hotspots, and applies LSTM methods to predict short-term and sudden demand fluctuations. The model optimizes coverage rate, response time, and cost budget control for drone transportation networks through a multi-objective genetic algorithm. Using Guangzhou as a case study, the results demonstrate that through “dynamic-static” collaborative deployment and multi-model drone coordination, the network achieves 96.18% demand coverage with an average response time of 673.38 s, significantly outperforming traditional vehicle transportation. Sensitivity analysis and robustness testing further validate the model’s effectiveness in handling demand fluctuations, weather changes, and airspace restrictions. This research provides theoretical support and decision-making basis for scientific planning of urban medical emergency drone transportation networks, offering practical significance for enhancing urban emergency rescue capabilities.
Keywords: medical emergency logistics; drone delivery; drone base station site selection; spatial–temporal analysis; multi-objective optimization; genetic algorithm medical emergency logistics; drone delivery; drone base station site selection; spatial–temporal analysis; multi-objective optimization; genetic algorithm

Share and Cite

MDPI and ACS Style

Zhang, H.; Zou, L.; Yang, Y.; Ma, J.; Xiao, J.; Lin, P. Urban Medical Emergency Logistics Drone Base Station Location Selection. Drones 2026, 10, 17. https://doi.org/10.3390/drones10010017

AMA Style

Zhang H, Zou L, Yang Y, Ma J, Xiao J, Lin P. Urban Medical Emergency Logistics Drone Base Station Location Selection. Drones. 2026; 10(1):17. https://doi.org/10.3390/drones10010017

Chicago/Turabian Style

Zhang, Hongbin, Liang Zou, Yongxia Yang, Jiancong Ma, Jingguang Xiao, and Peiqun Lin. 2026. "Urban Medical Emergency Logistics Drone Base Station Location Selection" Drones 10, no. 1: 17. https://doi.org/10.3390/drones10010017

APA Style

Zhang, H., Zou, L., Yang, Y., Ma, J., Xiao, J., & Lin, P. (2026). Urban Medical Emergency Logistics Drone Base Station Location Selection. Drones, 10(1), 17. https://doi.org/10.3390/drones10010017

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