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

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.

💡 Research Summary

This paper addresses the critical need for rapid, reliable transportation of organs, emergency patients, and essential medical supplies—a need that directly influences patient survival and treatment outcomes. Traditional reliance on ground ambulances and helicopter air ambulances suffers from traffic congestion and high operating costs, respectively. The authors propose a multimodal transportation framework that integrates ground ambulances with two emerging Advanced Air Mobility (AAM) technologies: Unmanned Aerial Vehicles (UAVs) and electric Vertical Take‑Off and Landing aircraft (eVTOLs).

A comprehensive literature review establishes that UAVs have already demonstrated cost‑effective, fast delivery of blood, vaccines, and laboratory samples, while eVTOLs promise higher speeds and larger payloads suitable for patient transport. However, both air modes are limited by battery range, charging infrastructure, and weather sensitivity. Consequently, a hybrid system that can switch between ground and air modes is argued to be essential for robust emergency logistics.

The core methodological contribution is a constructive greedy heuristic designed for real‑time vehicle dispatching. The algorithm proceeds as follows: (1) incoming transport requests are prioritized based on urgency (e.g., Cold Ischemia Time for organs) and location; (2) compatible requests sharing origin‑destination corridors are consolidated into a single leg to improve payload utilization; (3) travel times for ground legs are estimated using real‑time traffic congestion models, while aerial legs incorporate weather forecasts and battery consumption models; (4) for each leg the algorithm evaluates ambulances, UAVs, and eVTOLs against a cost‑time objective that includes operating expenses, fuel/electricity use, recharging or refueling frequency, and payload‑condition constraints (temperature, vibration); (5) the most suitable vehicle is assigned, with fallback options if the chosen asset is unavailable, allowing dynamic “ground‑air‑ground” chains. The heuristic runs in linear time relative to the number of requests and vehicles, making it far more suitable for on‑the‑fly decision making than exact mixed‑integer programming approaches.

Four fleet configurations are tested on a synthetic urban network (200 nodes, mixed road and air corridors) over a 24‑hour simulation horizon: (i) ambulances only, (ii) ambulances plus UAVs, (iii) ambulances plus eVTOLs, and (iv) a fully integrated fleet of all three. Performance metrics include average and maximum transport time, total operating cost (fuel, electricity, labor), number of charging/refueling events, and service level (percentage of requests completed within a predefined time window).

Results show that the fully integrated fleet (iv) achieves the best outcomes: average transport time is reduced by 27 % and the 90‑th‑percentile service level rises to 96 % compared with 78 % for the ambulance‑only case. Total operating cost drops by 22 % due to lower fuel consumption and efficient electric usage. Importantly, when adverse weather disables UAVs, eVTOLs seamlessly take over, preserving continuity; conversely, when battery depletion limits eVTOL range, ground ambulances fill the gap, demonstrating system resilience.

The paper’s contributions are threefold: (1) a practical, low‑complexity dispatch heuristic tailored to medical emergency logistics; (2) a quantitative integration of traffic, weather, and battery constraints into multimodal routing; and (3) empirical evidence that multimodal fleet integration yields substantial time and cost savings while enhancing reliability.

Limitations include reliance on simulated data, assumptions of fully deployed charging/vertiport infrastructure, and simplified modeling of temperature/vibration control for sensitive cargo. Future work is suggested in the form of field trials with real hospital and vertiport data, optimization of charging station placement, and the development of reinforcement‑learning based multi‑objective dispatch policies that simultaneously minimize time, cost, and carbon emissions.