A research-grade Operations Research + Machine Learning system for optimizing ambulance deployment, emergency response routing, and EMS system performance using real-world city-scale data.
CityGuard is a full-stack urban emergency response optimization platform that combines:
- 📈 Machine Learning demand forecasting
- 📍 Facility location optimization
- 🚑 Vehicle routing algorithms
- ⏳ Queueing theory
- 🎲 Stochastic simulation
- 🧮 Integer programming
to model and optimize emergency medical service (EMS) systems using publicly available city-scale data and real road networks.
The system predicts emergency demand hotspots, determines optimal ambulance station placement, dispatches ambulances efficiently under traffic constraints, and evaluates system performance through simulation.
Emergency response time directly impacts survival rates in:
- cardiac arrest,
- trauma,
- stroke,
- respiratory emergencies.
Even a small reduction in ambulance response time can significantly improve patient outcomes.
Unlike toy ML projects, CityGuard solves a real public-systems optimization problem with measurable operational impact.
Minimize EMS response times while maximizing emergency coverage under limited ambulance and infrastructure constraints.
Historical EMS Data
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ML Demand Forecasting
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Spatial Demand Heatmaps
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Facility Location Optimization
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Ambulance Allocation (ILP)
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Vehicle Routing + Dispatch
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Queueing + System Simulation
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Performance Evaluation