Disaster Response Planning Using Agent-Based Simulation

Disaster Response Planning Using Agent-Based Simulation


Battelle is the world’s largest, non-profit, independent R&D organization, and is a worldwide leader in the development, commercialization, and transfer of technology. They manage or co-manage laboratories for the U.S. Department of Energy, the U.S. Department of Homeland Security, and an international nuclear laboratory in the United Kingdom.


In an effort to find practical operational solutions for a fast and effective response to an unexpected crisis or natural disaster, Battelle needed to test the effectiveness of a 48 hour shelter-in-place order for an Improvised Nuclear Device scenario (IND). The intended goal was to reduce radiation dosages received during an uncoordinated mass evacuation, by comparing immediate evacuation and shelter-in-place order.

Modeling a disaster, whether natural or man-made, represents many unique challenges. There are distinctive environments and physical consequences, and numerous scenario possibilities and threat vectors. In addition, disaster response strategies are rarely implemented as planned, and there are unknown human reactions.


Simulation was chosen for the disaster response planning because it had the capability to evaluate the space of potential scenarios. Deterministic models had limitations incorporating factors, like fundamentally unpredictable human responses and the need to compare alternatives versus looking for exact answers.

AnyLogic software was a natural choice for Battelle, as the software was already being utilized in a broad range of projects within the organization, including:

In addition, AnyLogic’s agent-based capabilities allowed Battelle to capture the most important dynamics of a disaster event. Emergence, or emergent behavior, is a key principal in modeling human behavior. Also, a model can sometimes exhibit unexpected outcomes. Both of these issues can only be captured using agent-based modeling.

Disaster Response Planning Simulation Model

Disaster Response Planning Simulation Model Framework

The comprehensive model framework included an environment of road networks, vehicles, drivers, and disaster events. The road network was built with road layouts from GIS databases, local highway agency data (speed limits, lane capacity), and agents as node points for greater control. Changes to the network, such as the flooding of roads or destruction of bridges, were incorporated into dynamic events as the disaster unfolded.

The physical limitations of vehicles were governed by parameter data provided by the US Census, Bureau of Transportation. Data from past disaster response studies was used to represent driver agent behaviors, taking into account the changes in irrational drivers in normal circumstances versus during a mass evacuation. The model also incorporated dynamic route finding (several interlinked agent state sets that were dynamically tracked and updated). In addition, all behavior states were linked to physical vehicle movement parameters to initiate vehicle stoppages as drivers became incapacitated.

Agent behavior variables from initial values were calibrated, and evacuation data was used from past disasters to set accuracy targets, since calibration and validation were critical steps in proving the validity of the simulation model. If no historical data was available, Battelle used data from other major transportation events, sensitivity analysis based on other disaster events, and survey data.

Disaster Response Planning Simulation Model

Disaster Response Planning Simulation Model

Dynamic contours were used to track regions of disaster consequences, often derived from other simulation models, to compartmentalize processing requirements. Contours updated in real time based on predicted weather patterns, land cover, etc., and multiple interlinked contour sets could be adapted to represent almost any disaster scenario (for example, flooding levels, fire spread, damage path, contamination/fallout spread). In the IND scenario, two main contour sets were used; blast radius levels (fireball and overpressure force contours) and fallout distribution (radiation levels in air and deposition on ground from various radioactive particle types).


The disaster response planning simulation model built using AnyLogic software compared immediate evacuation versus shelter-in-place order and showed that shelter-in-place order significantly reduced radiation dosage received, as well as cases of severe radiation poisoning for large INDs.

The model also produced downstream outputs to test different disaster response strategies and find the best response strategy among several likely options. Battelle was able to incorporate emergency responder agents, multiple intervention scenarios, and interchangeable model components (different locations for same disaster scenario, or different scenario for same location), to achieve the goal of finding practical operational solutions for fast and effective responses to various unexpected crisises or natural disasters.

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