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Optimizing stadium evacuation by integrating geocomputation and affordance theory.
Joslyn Zale1, Bandana Kar2, David Cochran1
1Department of Geography and Geology, The University of Southern Mississippi, Hattiesburg, Mississippi.
Journal of Emergency Management (Weston, Mass.)
|May 24, 2018
Summary
Agent-based modeling optimized stadium evacuation times by considering individual factors like age and gender. Using location-specific data significantly reduced estimated evacuation times compared to general models.
Area of Science:
- Disaster Management
- Computational Social Science
- Public Health
Background:
- Football stadiums are critical infrastructure in the US, necessitating robust hazard event protection.
- Optimizing evacuation procedures is crucial for public safety in large-capacity venues.
Purpose of the Study:
- To develop and implement an agent-based evacuation model for M.M. Roberts Stadium.
- To assess the impact of individual characteristics on evacuation efficiency.
- To compare evacuation times using location-specific versus location-unspecific data.
Main Methods:
- An agent-based evacuation model was utilized.
- Evacuee data included age, gender, physical fitness, alcohol consumption, and prior hazard experience.
- Locomotion speeds were location-specific, accounting for individual variations.
Main Results:
- Individual age and gender significantly influence locomotion speed during evacuation.
- Evacuation route choice is affected by perceived safety and effectiveness.
- Estimated evacuation times (stadium-only: 20.82 min; stadium+campus: 165.01 min) were shorter than those from models using general locomotion speeds.
- Statistically significant differences (p ≤ 0.001) were found between location-specific and location-unspecific evacuation times.
Conclusions:
- Agent-based modeling incorporating individual and location-specific data enhances evacuation time accuracy.
- Tailoring evacuation strategies to individual characteristics and local conditions is vital for critical infrastructure safety.
- Findings underscore the importance of using local data for precise hazard event response planning.
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