Time-Critical Multimodal Medical Transportation: Organs, Patients, and Medical Supplies

2026-02-02

AI Summary

This study addresses delays in emergency medical transportation caused by traffic congestion and adverse weather by proposing a multimodal cooperative scheduling system that integrates ground ambulances, drones, and electric vertical take-off and landing (eVTOL) aircraft. The system innovatively combines aerial and terrestrial resources to enable dynamic payload consolidation, real-time responsiveness to traffic and meteorological conditions, and efficient task allocation through a constructive greedy heuristic algorithm. Experimental evaluations across four fleet configurations demonstrate that the fully integrated multimodal fleet significantly outperforms conventional approaches in reducing operational costs, energy consumption, and total transportation time, thereby validating the effectiveness and practicality of the proposed methodology.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Transportation
• Intelligent Robots: Multi-Robot Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Timely transportation of organs, patients, and medical supplies is critical to modern healthcare, particularly in emergencies and transplant scenarios where even short delays can severely impact outcomes. Traditional ground-based vehicles such as ambulances are often hindered by traffic congestion; while air vehicles such as helicopters are faster but costly. Emerging air vehicles -- Unmanned Aerial Vehicles and electric vertical take-off and landing aircraft -- have lower operating costs, but remain limited by range and susceptibility to weather conditions. A multimodal transportation system that integrates both air and ground vehicles can leverage the strengths of each to enhance overall transportation efficiency. This study introduces a constructive greedy heuristic algorithm for multimodal vehicle dispatching for medical transportation. Four different fleet configurations were tested: (i) ambulances only, (ii) ambulances with Unmanned Aerial Vehicles, (iii) ambulances with electric vertical take-off and landing aircraft, and (iv) a fully integrated fleet of ambulances, Unmanned Aerial Vehicles, and electric vertical take-off and landing aircraft. The algorithm incorporates payload consolidation across compatible routes, accounts for traffic congestion in ground operations and weather conditions in aerial operations, while enabling rapid vehicle dispatching compared to computationally intensive optimization models. Using a common set of conditions, we evaluate all four fleet types to identify the most effective configurations for fulfilling medical transportation needs while minimizing operating costs, recharging/fuel costs, and total transportation time.

Problem

• multimodal transportation
• medical logistics
• time-critical delivery
• emergency medical services
• organ transplantation

Innovation

• multimodal transportation
• greedy heuristic algorithm
• medical logistics
• UAV
• eVTOL