An Integrated Pedestrian Behavior Model Based on Extended Decision Field Theory and Social Force Model



A novel pedestrian behavior model is proposed, which integrates (1) extended decision field theory (EDFT) for tactical level human decision-making, (2) social force model (SFM) to represent physical interactions and congestions among people and the environment, and (3) dynamic planning algorithm involving AND/OR graphs. Furthermore, SFM is enhanced with the vision of each individual, and both individual and group behaviors are considered. The proposed model is illustrated and demonstrated with a shopping mall scenario (a typical mall in the city of Tucson, AZ). Literature survey and observations have been conducted at the mall for data collection and partial validation of the proposed model. The computational environment for human-in-the-loop experiment is also conceptually developed, which will be used to collect more human data in the future. We then developed a simulation model of the considered mall using AnyLogic® software, where each individual in the simulation executes a planning algorithm to select a destination, EDFT for choosing a direction, and extended social force model (ESFM) to adjust its velocity. Using the constructed crowd simulation model, several experiments have been conducted to test the impact of various factors (e.g. consideration of human’s vision, group shopping behavior, arrangement of stores, complexity of the model) on several metrics such as the average distance among neighboring shoppers, the movement speed of pedestrians, profit of the shopping mall, and scalability.


Shopping Mall Crowd Behavior Group Shopper Crowd Density Potential Destination 
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  1. Babin B, William D et al (1994) Work and/or fun: measuring hedonic and utilitarian shopping value. J Consumer Res 20(4):644–656CrossRefGoogle Scholar
  2. Batra R, Ahtola O (1991) Measuring the hedonic and utilitarian sources of consumer attitudes. Mark Lett 2(2):159–170CrossRefGoogle Scholar
  3. Baumann D, Robert C et al (1981) Altruism as hedonism: helping and self-gratification as equivalent responses. J Pers Soc Psychol 40(6):1039–1046CrossRefGoogle Scholar
  4. Blue VJ, Adler LJ (2001) Cellular automata microsimulation for modeling bi-directional pedestrian walkways. Transp Res Part B 35:293–312CrossRefGoogle Scholar
  5. Busemeyer JR, Diederich A (2002) Survey of decision field theory. Math Soc Sci 43:345–370MathSciNetMATHCrossRefGoogle Scholar
  6. Busemeyer JR, Townsend JT (1993) Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. Psychol Rev 100:432–459CrossRefGoogle Scholar
  7. Gaskell GD, Benewick RJ (1987) The crowd in contemporary Britain. Sage, LondonGoogle Scholar
  8. Hamagami T, Hirata H (2003) Method of crowd simulation by using multiagent on cellular automata. In: Proceedings of IEEE/WIC international conference on intelligent agent technology (IAT’03), Halifax, Canada, pp 46–52Google Scholar
  9. Helbing D, Farkas I et al (2000) Simulating dynamical features of escape panic. Nature 407:487–490CrossRefGoogle Scholar
  10. Helbing D, Buzna A et al (2005) Self-organized pedestrian crowd dynamics: experiments, simulations, and design solutions. Transp Sci 39:1–24CrossRefGoogle Scholar
  11. Hu Q, Fang W et al (2009) The simulation and analysis of pedestrian crowd and behavior. Sci China Ser E Technol Sci 52(6):1762–1767MATHCrossRefGoogle Scholar
  12. Kuruvilla S, Joshi N et al (2009) Do men and women really shop differently? an exploration of gender differences in mall shopping in India. Int J Cons Stud 33:715–723CrossRefGoogle Scholar
  13. Lee S, Son Y (2008) Integrated human decision behavior modeling using extended decision field theory and soar under BDI framework. IERC, Vancouver, CanadaGoogle Scholar
  14. Lee S, Son Y et al (2005) Decision field theory extensions for behavior modeling in dynamic environment using Bayesian belief network. Inf Sci 178:2297–2314MathSciNetCrossRefGoogle Scholar
  15. Lee S, Zhao X et al (2008) Fully dynamic epoch (FDE) time synchronization method for distributed supply chain simulation. Int J Comput Appl Technol 31(3–4):249–262CrossRefGoogle Scholar
  16. Ma J, Song W et al (2010) k-Nearest-neighbor interaction induced self-organized pedestrian counter flow. Phys A 389:2101–2117CrossRefGoogle Scholar
  17. Moussaïd M, Helbing D et al (2009) Experimental study of the behavioural mechanisms underlying self-organization in human crowds. In: Proceedings of Royal Society B, vol 276, pp 2755--27Google Scholar
  18. Muramatsu M, Irie T et al (1999) Jamming transition in pedestrian counter flow. Phys A 267:487–498CrossRefGoogle Scholar
  19. Parisi D, Gilman M et al (2009) A modification of the fial force model can reproduce experimental data of pedestrian flows in normal conditions. Phys A 388:3600–3608CrossRefGoogle Scholar
  20. Rathore A, Balaraman B et al (2005) Development and benchmarking of an epoch time synchronization method for distributed simulation. J Manuf Syst 24(2):69–78CrossRefGoogle Scholar
  21. Shendarkar A, Vasudevan S et al (2008) Crowd simulation for emergency response using BDI agents based on immersive virtual reality. Simul Model Pract Theory 16:1415–1429CrossRefGoogle Scholar
  22. Son Y, Jin J (2006) Extended BDI framework and technologies for modeling partial human decision-making. AFOSR Cognition & decision program review workshop, Fairborn, OHGoogle Scholar
  23. Vasudevan K, Son Y (2008) Concurrent consideration of evacuation safety and productivity in manufacturing facility planning using multi-paradigm simulations. 18th international conference on flexible automation and intelligent manufacturing, Skovde, SwedenGoogle Scholar
  24. Xia Y, Wong SC et al (2009) Dynamic continuum pedestrian flow model with memory effect. Phys Rev 79(066113):1–8Google Scholar
  25. Zacharias J, Bernhardt T et al (2005) Computer-simulated pedestrian behavior in shopping environment. J Urb Plan Dev 131(3):195–200CrossRefGoogle Scholar
  26. Zhao X, Son Y (2008) BDI-based human decision-making model in automated manufacturing systems. Int J Model Simul 28(2):1–10Google Scholar
  27. Zhuang G, Tsang A et al (2006) Impacts of situational factors on buying decisions in shopping malls. Eur J Mark 40(1/2):17–43CrossRefGoogle Scholar

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© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Systems and Industrial Engineering DepartmentThe University of ArizonaTucsonUSA
  2. 2.Mechatronics and Manufacturing Technology CenterSamsung Electronics Co., Ltd.Suwon-CityRepublic of Korea

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