SCC-LSR: Integrating Probabilistic Digital Twinning and Dynamic Optimization to Enhance EMS Operations

Emergency Medical Services (EMS) are vital to public health and safety, providing life-saving care before patients reach a hospital. Yet EMS systems operate under intense pressure, making rapid decisions about ambulance deployment, dispatch, and transport in the face of constant uncertainty—from unpredictable emergency call volumes to traffic conditions. These decisions are often made using simple, rule-based approaches that can fall short under real-world complexity. This Smart and Connected Communities (SCC) project seeks to transform EMS operations using advanced data science, looking to create tools that allow EMS agencies to make more informed and adaptive decisions. By developing a digital shadow—a data-driven, virtual replica of EMS systems—this research seeks to enable agencies to test new policies in a safe virtual setting before deploying them in the field. The project builds on a unique partnership with the New York City Fire Department, ensuring that the outcomes directly inform real-world practice. Technically, the project intends to advance research in cyber-physical systems under uncertainty by developing a probabilistic digital shadow for EMS operations. This includes developing new machine learning models for ambulance travel time estimation and emergency call forecasting that incorporate granular uncertainty quantification and can be updated in real-time using multimodal data streams. The team will also look to develop novel methods for rare-event simulation and scalable, risk-averse optimization. These tools will be used to design and evaluate new EMS deployment and dispatch policies using simulation–optimization frameworks, with special attention to balancing computational efficiency and decision quality. By localizing simulation models and optimizing resource allocation strategies, the research looks to generate operationally meaningful policies tailored to urban environments like New York City. Continuous collaboration with FDNY will guide the research, ensuring that developed methods are actionable, scalable, and grounded in EMS system realities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.