Autonomous agents
Responders and citizens modeled as autonomous software agents that perceive, decide, interact and adapt to fast-changing post-earthquake conditions.
An agent-based modeling framework for simulating earthquake emergency response — a product in development by PayaShahr.
QuakeAgents is an intelligent, GIS-enabled agent-based simulation framework designed to model, analyze, and optimize earthquake emergency response operations in complex urban environments. The framework represents key stakeholders involved in disaster response as autonomous software agents that perceive their surroundings, make decisions based on predefined behaviors, interact with one another, and adapt to rapidly changing post-earthquake conditions.
The simulation environment is initialized using spatial data such as road networks, building inventories, damage maps, critical infrastructure, shelters, hospitals, and population distribution. Following an earthquake scenario, QuakeAgents simulates the dynamic activities of multiple emergency organizations — including firefighters, emergency medical services (EMS), police, Red Crescent, municipalities, military units, volunteers, and affected citizens — to reproduce realistic emergency response processes.
QuakeAgents also serves as a decision support system by enabling emergency managers to test alternative policies, compare coordination strategies, identify operational bottlenecks, and quantify performance indicators such as response time, rescue rate, resource utilization, operational cost, and community resilience. The framework is scalable, modular, and extensible, allowing the integration of remote sensing data, real-time information, machine learning algorithms, and optimization techniques to enhance situational awareness and decision-making.
Ultimately, QuakeAgents provides a virtual laboratory for understanding the complex interactions among emergency responders and affected communities, helping governments and disaster management organizations improve preparedness, response efficiency, and urban resilience against future earthquakes.
Responders and citizens modeled as autonomous software agents that perceive, decide, interact and adapt to fast-changing post-earthquake conditions.
Initialized from spatial data: road networks, building inventories, damage maps, critical infrastructure, shelters, hospitals and population.
Firefighters, EMS, police, Red Crescent, municipalities, military units, volunteers and affected citizens in one scene.
Test alternative policies, compare coordination strategies and identify operational bottlenecks.
Response time, rescue rate, resource utilization, operational cost and community resilience.
Modular and extensible; integrates remote sensing, real-time data, machine learning and optimization.
Tell us about your city, your data and your challenge — we usually reply within two working days.