Time-dependent ambulance allocation considering data-driven empirically required coverage

Area of Science:

• Operations Research
• Public Health
• Transportation Science

Background:

• Existing emergency medical service (EMS) models often use fixed coverage areas, ignoring dynamic daily changes in demand, travel times, and ambulance speeds.
• This static approach can lead to inaccurate fleet size estimations and suboptimal ambulance positioning, potentially compromising emergency care quality.
• Recent advancements in data collection enable precise, dynamic determination of coverage needs based on real-world variations.

Purpose of the Study:

• To develop a data-driven optimization approach for locating and relocating EMS vehicles.
• To maximize flexible, empirically determined coverage that adapts to time-of-day and site-specific variations.
• To enhance the cost-effectiveness and quality of emergency medical services by ensuring better ambulance availability.

Main Methods:

• Formulation of an integer linear programming model for ambulance location and relocation.
• Utilizing extensive, empirically collected data to inform coverage requirements.
• Incorporating time-dependent variations in demand, travel time, and vehicle speed into the optimization model.

Main Results:

• The proposed model successfully maximizes flexible, empirically determined coverage, adjusting for daily variations.
• It addresses the limitations of fixed coverage models, preventing system unavailability due to parallel operations.
• A comprehensive case study validates the model's effectiveness in improving emergency care quality and cost-effectiveness.

Conclusions:

• Data-driven optimization significantly enhances EMS operational efficiency and emergency response.
• Accounting for dynamic, real-world variations is crucial for accurate ambulance deployment and resource allocation.
• The developed integer linear programming model offers a practical solution for improving urban emergency medical services.