An Agent-Based Simulation For Emergency Response Management

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An Agent-Based Simulation For Emergency Response
Management

• Timothy Schoenharl, R. Ryan McCune, Greg Madey
• of the University of Notre Dame du Lac
• This material is based upon work
• supported by the National Science
  Foundation under Grant No. CNS-0540348

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Wireless Phone-based Emergency Response System
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Purpose of Simulation

• Limited knowledge of events by emergency managers
• Understand crisis development
• Develop strategy for better response

http//www.nyc.gov/html/fdny/gif/photo_unit/fires/
122602/122602_157_truck.jpg
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Overview of Simulation

• Agent-based simulations model behavior of citys
  population
• Simulate normal and crisis behavior
• Output call activity and agent location on GIS map

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Implementation

• RePast 3.1 for Java
• Colt High Performance Scientific Library
• Geotools
• OpenMap

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Implementation

• Pattern Oriented Design
• Singleton Class

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The Agent

• Each agent represents pedestrian
• Randomly assigned initial position inside of
  Voronoi Cell
• Call activity based on empirical distributions
• Movement determined by given scenario

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The Environment

• Real city created with GIS files
• Cell phone tower locations
• Roads, Water, Political Boundaries
• Voronoi cells built around cell phone towers

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Simulation Scenarios

• Simulate Regular behavior
• Move and return
• Location and call activity based on empirical
  data
• Crisis behavior
• Flee Run from a point
• Bounded Flee Run a distance from a point

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Validation

• Movement Model
• Face Value
• Call Activity
• Empirical vs. Simulated results plotted
• Kolmogorov-Smirnov test

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Validation cont.
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Interface

• RePast GUI
• OpenMap Display
• Voronoi cell color changes with agent containment
• Call Activity Graph

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Screenshot of Repast GUI
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Runtime Performance

• Runtime vs. Graphical Output
• 500 Agents
• Move and Return Model
• Distribution-Based Calling Activity

GIS Agent Location Snapshot Time (s)
No No No 240
Yes No No 354
Yes Yes No 363
Yes Yes Yes 35200
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Runtime Performance

• Runtime vs. Number of Agents
• Linear Scaling demonstrates excellent runtime
  characteristics

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Summary

• Agent-based model to simulate crisis behavior
• Movement dependent on behavior model and
  outputted to GIS map
• Calling Activity based on empirical data
• Designed for WIPER System

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Future Work

• Implement more crisis scenarios
• Study of aggregate patterns
• More realistic agent paths
• Road and Highway use
• Vary Speed
• Traffic Jam Scenarios
• Water
• Social network structure

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Questions?