Optimizing stadium evacuation by integrating geocomputation and affordance theory

Joslyn Zale, PhD; Bandana Kar, PhD; David Cochran, PhD

doi:10.5055/jem.2018.0357

Authors

Joslyn Zale, PhD
Bandana Kar, PhD
David Cochran, PhD

DOI:

https://doi.org/10.5055/jem.2018.0357

Keywords:

evacuation, agent-based model, GIS, hazard, stadium, network analysis, pedestrian evacuation, vehicular evacuation

Abstract

Football is culturally and economically important in the United States, and football stadiums are part of the country's critical infrastructure, thus receiving government protection against hazard events. In this project, an agent-based evacuation model was implemented to optimize evacuation time from The University of Southern Mississippi's M.M. Roberts Stadium (football) by accounting for evacuees’ age, gender, physical fitness, alcohol consumption, and prior experience with hazard events. The findings revealed that (i) the age and gender of an individual impact his/her locomotion speed and (ii) evacuation route choice is influenced by evacuees’ perception of its safety and effectiveness. The estimated evacuation times for all evacuees to exit only the stadium and the stadium plus the surrounding campus were 20.82 and 165.01 minutes, respectively. Both of these times were shorter than the evacuation times determined by models employing location-unspecific locomotion speeds. One-way analysis of variance revealed that there were statistically significant differences between use of location-specific and location-unspecific within-stadium evacuation times (p ≤ 0.001 with α = 0.05). These results suggest that using local data is vital to accurately estimate evacuation time.

Author Biographies

Joslyn Zale, PhD

Department of Geography and Geology, The University of Southern Mississippi, Hattiesburg, Mississippi

Bandana Kar, PhD

Research Scientist, Critical Infrastructure and Climate Team, Urban Dynamics Institute, Geographic Information Science and Technology Group, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee

David Cochran, PhD

Department of Geography and Geology, The University of Southern Mississippi, Hattiesburg, Mississippi

References

Wattles J: NFL sees big jump in revenue. CNN Money. July 20, 2015. Available at http://money.cnn.com/2015/07/20/news/greenbay-packers-revenue/. Accessed June 23, 2017.

ESPN: NFL attendance. 2016. Available at http://espn.go.com/nfl/attendance. Accessed June 23, 2017.

Department of Justice: Uniting and Strengthening America by Providing Appropriate Tools Required to Intercept and Obstruct Terrorism (USA PATRIOT ACT) Act of 2001. Available at https://www.gpo.gov/fdsys/pkg/PLAW-107publ56/pdf/PLAW-107publ56.pdf. Accessed June 23, 2017.

Johnson CW: The application of computational models for the simulation of large-scale evacuations following infrastructure failures and terrorist incidents. Paper presented at: Proceedings of NATO Research Workshop on Computational Models of Risk to Infrastructure, 2006, Primosten, Croatia.

Baker TA, III, Connaughton D, Zhang JJ, et al.: Perceived risk of terrorism and related risk management practices of NCAA Division IA football stadium managers. J Legal Aspects Sport. 2007; 13(2): 145-179.

Hall S: Effective security management of university sport venues. Sport J. 2008; 9(4). Available at http://thesportjournal.org/article/effective-security-management-of-university-sport-venues/. Accessed June 23, 2017.

Hall S, Marciani L, Cooper W: Sport venue security: Planning and preparedness for terrorist-related incidents. SMART J. 2008; 4(2): 6-15.

Phillips D, Hall S, Marciani L, et al.: Sport event security: How does it relate to customer satisfaction and marketing? Paper presented at: Sports Marketing Association Annual Conference, November 2-4, 2006, Denver, CO.

Zale J: Modeling Football Stadium Evacuation Using a Geographic Information System [master's thesis]. Hattiesburg, MS: The University of Southern Mississippi, 2010.

INCONTROL Simulation Solutions: Pedestrian Dynamics. 2017. Available at http://www.pedestrian-dynamics.com/. Accessed June 23, 2017.

Urbanik TE, Desrosiers A, Lindell MK, et al.: Analysis of Techniques for Estimating Evacuation Times for Emergency Planning Zones. Washington, DC: Office of Nuclear Reactor Regulation, U.S. Nuclear Regulatory Committee, 1980. Report NUREG/CR-1745.

Cova TJ, Church RL: Modeling community evacuation vulnerability using GIS. Int J Geogr Inf Sci. 1997; 11(8): 763-784.

Chen X: Microsimulation of hurricane evacuation strategies for Galveston Island. Prof Geogr. 2008; 60(2): 160-173.

Pal A, Graettinger AJ, Triche MH: Emergency evacuation modeling based on geographic information system data. Paper presented at: Transportation Review Board Annual Meeting, 2003, Washington, DC.

Cova TJ, Johnson JP: Microsimulation of neighborhood evacuations in the urban-wildland interface. Environ Plann A. 2002; 34: 2211-2229.

Georgiadou PS, Papazoglou IA, Kiranoudis CT, et al.: Modeling emergency evacuation for major hazard industrial sites. Reliab Eng Syst Safety. 2007; 92: 1388-1402.

Lo SM, Fang Z, Lin P, et al.: An evacuation model: The SGEM Package. Fire Safety J. 2004; 39: 169-190.

Yuan W, Tan KH: An evacuation model using cellular automata. Physica A. 2007; 384(2): 549-566.

Batty M: Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals. Cambridge, MA: MIT Press, 2007.

Ward DP, Murray AT, Phinn SR: Integrating spatial optimization and cellular automata for evaluating urban change. Ann Reg Sci. 2003; 37(1): 131-148.

Dijkstra J, Jessurun AJ, de Vries B, et al.: Agent architecture for simulating pedestrians in the built environment. Paper presented at: Proceedings of the Autonomous Agents and Multiagent Systems’ Workshop on Agents in Traffic and Transportation, May 8-12, 2006, Future University, Hakodate, Japan.

Yue H, Hao H, Chen X, et al.: Simulation of pedestrian flow on square lattice based on cellular automata model. Physica A. 2007; 384(12): 567-588.

Yassemi S, Dragicevic S, Schmidt M: Design and implementation of an integrated GIS-based cellular automate model to characterize forest fire behavior. Ecol Model. 2008; 210(1-2): 71-84.

Tonguz OK, Viriyasitavat W, Bai F: Modeling urban traffic: A cellular automata approach. IEEE Commun Mag. 2009; 47(5): 142-150.

Epstein JM, Axtell R: Growing Artificial Societies from the Ground Up. Cambridge, MA: MIT Press, 1996.

Alfarano S, Lux T, Wagner F: Estimation of agent-based models: The case of an asymmetric herding model. Computat Econ. 2005; 26: 19-49.

Ha V, Lykotrafitis G: Agent-based modeling of a multi-room multi-floor building emergency evacuation. Physica A. 2012; 391(8): 2740-2751.

Bonabeau E: Agent-based modeling: Methods and techniques for simulating human systems. Proc Natl Acad Sci USA. 2001; 99(3): 7280-7287.

Macy MW, Willer R: From factors to actors: Computations sociology and agent-based modeling. Annu Rev Sociol. 2002; 28: 143-166.

Laver M, Sergenti E: Party Competition: An Agent-Based Model. Princeton, NJ: Princeton University Press, 2012.

Macal CM, North MJ: Agent-based modeling and simulation. In Rossetti MD, Hill RR, Johansson B, et al. (eds.): Proceedings of the 2009 Winter Simulation Conference. IEEE: Austin, TX, 2009: 86-98. Available at http://www.informs-sim.org/wsc09papers/009.pdf. Accessed June 21, 2017.

Gibson JJ: The Senses Considered as Perceptual Systems. Boston, MA: Houghton Mifflin Co, 1966.

Gibson JJ: The Ecological Approach to Visual Perception. Boston, MA: Houghton Mifflin Co, 1979.

Joo J, Kim N, Wysk RA, et al.: Agent-based simulation of affordance-based human behaviors in emergency evacuation. Simul Model Pract Theory. 2013; 32: 99-115.

Jones J, Bright E, Bhaduri B: Estimating dynamic population at special events. Paper presented at: Association of American Geographers Annual Meeting, Las Vegas, NV, March 22-27, 2009.

Montello DR, Sutton PC: An Introduction to Scientific Research Methods in Geography and Environmental Studies. Los Angeles, CA: SAGE Publications, 2013.

Cunningham T, Hall S, Marciani L, et al.: Spectator perceptions of security management at a NASCAR (National Association for Stock Car Auto Racing) event. Sport J. 2009; 19. Available at http://thesportjournal.org/article/spectator-perceptions-of-securitymanagement-at-a-nascar-national-association-for-stock-car-autoracing-event/. Accessed June 23, 2017.

U.S. Census 2010: Summary file 1. Available at http://www.census.gov/2010census/news/press-kits/summary-file-1.html. Accessed June 23, 2017.

Zale J: Optimizing Stadium Evacuation by Integrating Geocomputation and Affordance Theory [dissertation]. Hattiesburg, MS: The University of Southern Mississippi, 2017.

Centers for Disease Control and Prevention: About BMI. 2014. Available at http://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/. Accessed June 23, 2017.

Widmark EMP: Principles and Applications of Medicolegal Alcohol Determination. Davis, CA: Biomedical Publications, 1981.

Centers for Disease Control and Prevention: Impaired driving: Get the facts. 2016. Available at http://www.cdc.gov/motorvehiclesafety/impaired_driving/impaired-drv_factsheet.html. Accessed June 23, 2017.

Carey N: Established Pedestrian Walking Speeds. Portland, OR: Portland State University, 2005. Draft results.

Parisi DR, Dorso CO: Microscopic dynamics of pedestrian evacuation. Physica A. 2005; 354: 606-618.

Costa M: Interpersonal distances in group walking. J Nonverbal Behav. 2010; 34(1): 15-26.

Still GK: Crowd safety and risk analysis. 2014. Available at http://www.gkstill.com/Support/crowd-density/CrowdDensity-1.html. Accessed June 23, 2017.

Nuwer H: Wrongs of Passage: Fraternities, Sororities, Hazing, and Binge Drinking. Bloomington, IN: Indiana University Press, 2001.

Vicary JR, Karshin CM: College alcohol abuse: A review of the problems, issues, and prevention approaches. J Primary Prevent. 2002; 22(3): 299-331.

Maggs JL, Williams LR, Lee CM: Ups and downs of alcohol use among first-year college students: Number of drinks, heavy drinking, and stumble and pass out drinking days. Addict Behav. 2010; 36(3): 197-202.