Spice: a cognitive agent framework for computational crowd simulations in complex environments

  • 366 Accesses

  • 4 Citations


Pedestrian behavior is an omnipresent topic, but the underlying cognitive processes and the various influences on movement behavior are still not fully understood. Nonetheless, computational simulations that predict crowd behavior are essential for safety, economics, and transport. Contemporary approaches of pedestrian behavior modeling focus strongly on the movement aspects and seldom address the rich body of research from cognitive science. Similarly, general purpose cognitive architectures are not suitable for agents that can move in spatial domains because they do not consider the profound findings of pedestrian dynamics research. Thus, multi-agent simulations of crowd behavior that strongly incorporate both research domains have not yet been fully realized. Here, we propose the cognitive agent framework Spice. The framework provides an approach to structure pedestrian agent models by integrating concepts of pedestrian dynamics and cognition. Further, we provide a model that implements the framework. The model solves spatial sequential choice problems in sufficient detail, including movement and cognition aspects. We apply the model in a computer simulation and validate the Spice approach by means of data from an uncontrolled field study. The Spice framework is an important starting point for further research, as we believe that fostering interdisciplinary modeling approaches will be highly beneficial to the field of pedestrian dynamics.

This is a preview of subscription content, log in to check access.

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14


  1. 1.

    The term scenario defines the virtual environment in which the model is applied. Here, one has to differentiate between the environment and the model, because the model can be applied to diverse scenarios without changing the model’s mathematical features.

  2. 2.

    In pedestrian dynamics, operational models describe movement/walking behavior. Throughout this paper, we will refer to operational models (in pedestrian dynamics sense) as walking or movement models.

  3. 3.

  4. 4.

    The tool was provided by Prof. Köster and her team, University of Applied Sciences Munich.


  1. 1.

    AlGadhi, S. A. H., & Mahmassani, H. (1991). Simulation of crowd behavior and movement: Fundamental relations and application. Transportation Research Record, 1320, 260–268.

  2. 2.

    Allik, J., & Tuulmets, T. (1991). Occupancy model of perceived numerosity. Perception & Psychophysics, 49(4), 303–314.

  3. 3.

    Alonso-Marroquín, F., Busch, J., Chiew, C., Lozano, C., & Ramírez-Gómez, Á. (2014). Simulation of counterflow pedestrian dynamics using spheropolygons. Physical Review E, 90(6), 063305.

  4. 4.

    Anderson, J. R. (1983). A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior, 22(3), 261–295.

  5. 5.

    Anderson, J. R. (1993). Problem solving and learning. American Psychologist, 48(1), 35.

  6. 6.

    Anderson, J. R. (2010). Cognitive psychology and its implications (7th ed.). New York: Worth Publishing.

  7. 7.

    Anderson, J. R., Matessa, M., & Lebiere, C. (1997). ACT-R: A theory of higher level cognition and its relation to visual attention. Human-Computer Interaction, 12, 439–462.

  8. 8.

    Anderson, J. R., & Schooler, L. J. (1991). Reflections of the environment in memory. Psychological Science, 2(6), 396–408.

  9. 9.

    Arentze, T. A., Ettema, D., & Timmermans, H. J. (2011). Estimating a model of dynamic activity generation based on one-day observations: method and results. Transportation Research Part B: Methodological, 45(2), 447–460.

  10. 10.

    Arentze, T. A., & Timmermans, H. J. (2011). A dynamic model of time-budget and activity generation: Development and empirical derivation. Transportation Research Part C: Emerging Technologies, 19(2), 242–253.

  11. 11.

    Aumann, Q., & Kielar, P. M. (2016). A modular routing graph generation method for pedestrian simulation. In 28. Forum Bauinformatik (pp. 241–253).

  12. 12.

    Baddeley, A. D., & Hitch, G. (1974). Working memory. Psychology of Learning and Motivation, 8, 47–89.

  13. 13.

    Balke, T., & Gilbert, N. (2014). How do agents make decisions? A survey. Journal of Artificial Societies and Social Simulation, 17(4), 13.

  14. 14.

    Bandini, S., Rubagotti, F., Vizzari, G., & Shimura, K. (2011). An agent model of pedestrian and group dynamics: Experiments on group cohesion. In Congress of the Italian association for artificial intelligence (pp. 104–116).

  15. 15.

    Benedikt, M. L. (1979). To take hold of space: Isovists and isovist fields. Environment and Planning B: Planning and design, 6(1), 47–65.

  16. 16.

    Bierlaire, M., & Robin, T. (2009). Pedestrians choices. In H. Timmermans (Ed.), Pedestrian behavior. Models, data collection and applications (pp. 1–26). Bingley: Emerald Group Publishing.

  17. 17.

    Blue, V. J., & Adler, J. L. (2001). Cellular automata microsimulation for modeling bi-directional pedestrian walkways. Transportation Research Part B: Methodological, 35(3), 293–312.

  18. 18.

    Borgers, A., & Timmermans, H. (2014). Indices of pedestrian behavior in shopping areas. Procedia Environmental Sciences, 22, 366–379.

  19. 19.

    Borgers, A. W. J., & Timmermans, H. J. P. (1986). A model of pedestrian route choice and demand for retail facilities within inner-city shopping areas. Geographical Analysis, 18(2), 115–128.

  20. 20.

    Bresenham, J. E. (1965). Algorithm for computer control of a digital plotter. IBM Systems journal, 4(1), 25–30.

  21. 21.

    Canca, D., Zarzo, A., Algaba, E., & Barrena, E. (2013). Macroscopic attraction-based simulation of pedestrian mobility: A dynamic individual route-choice approach. European Journal of Operational Research, 231(2), 428–442.

  22. 22.

    Chu, M. L., & Law, K. (2013). Computational framework incorporating human behaviors for egress simulations. Journal of Computing in Civil Engineering, 27(6), 699–707.

  23. 23.

    Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–114.

  24. 24.

    de Sevin, E., & Thalmann, D. (2005). A motivational model of action selection for virtual humans. In International 2005 computer graphics (pp. 213–220).

  25. 25.

    Dai, J., Li, X., & Liu, L. (2013). Simulation of pedestrian counter flow through bottlenecks by using an agent-based model. Physica A, 392(9), 2202–2211.

  26. 26.

    Danalet, A., Tinguely, L., de Lapparent, M., & Bierlaire, M. (2016). Location choice with longitudinal WiFi data. Journal of Choice Modelling, 18, 1–17.

  27. 27.

    Dijkstra, J., Timmermans, H. J. P., & Jessurun, J. (2014). Modeling planned and unplanned store visits within a framework for pedestrian movement simulation. Transportation Research Procedia, 2, 559–566.

  28. 28.

    Dong, X., Ben-Akiva, M. E., Bowman, J. L., & Walker, J. L. (2006). Moving from trip-based to activity-based measures of accessibility. Transportation Research Part A: Policy and Practice, 40(2), 163–180.

  29. 29.

    DOrazio, M., Spalazzi, L., Quagliarini, E., & Bernardini, G. (2014). Agent-based model for earthquake pedestrians evacuation in urban outdoor scenarios: Behavioural patterns definition and evacuation paths choice. Safety Science, 62, 450–465.

  30. 30.

    Duives, D. C., Daamen, W., & Hoogendoorn, S. P. (2013). State-of-the-art crowd motion simulation models. Transportation Research Part C: Emerging Technologies, 37, 193–209.

  31. 31.

    Dyer, J. R. G., Johansson, A., Helbing, D., Couzin, I. D., & Krause, J. (2009). Leadership, consensus decision making and collective behaviour in humans. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 364(1518), 781–789.

  32. 32.

    Förster, J., Liberman, N., & Friedman, R. S. (2007). Seven principles of goal activation: A systematic approach to distinguishing goal priming from priming of non-goal constructs. Personality and Social Psychology Review, 11(3), 211–233.

  33. 33.

    Förster, J., Liberman, N., & Higgins, E. T. (2005). Accessibility from active and fulfilled goals. Journal of Experimental Social Psychology, 41(3), 220–239.

  34. 34.

    Frith, C. D., & Frith, U. (2012). Mechanisms of social cognition. Annual Review of Psychology, 63, 287–313.

  35. 35.

    Gärling, T. (1994). Processing of time constraints on sequence decisions in a planning task. European Journal of Cognitive Psychology, 6(4), 399–416.

  36. 36.

    Gärling, T. (1995). Tradeoffs of priorities against spatiotemporal constraints in sequencing activities in environments. Journal of Environmental Psychology, 15(2), 155–160.

  37. 37.

    Gärling, T. (1999). Human information processing in sequential spatial choice. In Wayfinding behavior: Cognitive mapping and other spatial processes (pp. 81–98).

  38. 38.

    Gärling, T., & Gärling, E. (1988). Distance minimization in downtown pedestrian shopping. Environment and Planning A, 20(4), 547–554.

  39. 39.

    Gärling, T., Kwan, Mp, & Golledge, R. G. (1994). Computational-process modelling of household activity scheduling. Transportation Research Part B: Methodological, 28(5), 355–364.

  40. 40.

    Gärling, T., Säisä, J., Book, A., & Lindberg, E. (1986). The spatiotemporal sequencing of everyday activities in the large-scale environment. Journal of Environmental Psychology, 6(4), 261–280.

  41. 41.

    Gillner, S., & Mallot, H. A. (2007). These maps are made for walking—task hierarchy of spatial cognition. In Robotics and cognitive approaches to spatial mapping (pp. 181–201).

  42. 42.

    Graf, P., & Schacter, D. L. (1985). Implicit and explicit memory for new associations in normal and amnesic subjects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11(3), 501–518.

  43. 43.

    Hartmann, D. (2010). Adaptive pedestrian dynamics based on geodesics. New Journal of Physics, 12(4), 043032.

  44. 44.

    Hartmann, D., & von Sivers, I. (2013). Structured first order conservation models for pedestrian dynamics. Networks and Heterogeneous Media, 8(4), 985–1007.

  45. 45.

    Helbing, D., Buzna, L., Johansson, A., & Werner, T. (2005). Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions. Transportation Science, 39(1), 1–24.

  46. 46.

    Helbing, D., Farkas, I., & Vicsek, T. (2000). Simulating dynamical features of escape panic. Nature, 407(6803), 487–490.

  47. 47.

    Helbing, D., Johansson, A., & Al-Abideen, H. Z. (2007). Dynamics of crowd disasters: An empirical study. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 75(4), 1–7.

  48. 48.

    Helbing, D., & Mukerji, P. (2012). Crowd disasters as systemic failures: Analysis of the Love Parade disaster. EPJ Data Science, 1(1), 1–40.

  49. 49.

    Höcker, M., Berkhahn, V., Kneidl, A., Borrmann, A., & Klein, W. (2010). Graph-based approaches for simulating pedestrian dynamics in building models. In eWork and eBusiness in architecture, engineering and construction (pp. 389–394).

  50. 50.

    Hollmann, C. (2015). A cognitive human behaviour model for pedestrian behaviour simulation. Dissertation, University of Greenwich.

  51. 51.

    Hölscher, C., Tenbrink, T., & Wiener, J. M. (2011). Would you follow your own route description? Cognitive strategies in urban route planning. Cognition, 121(2), 228–247.

  52. 52.

    Hoogendoorn, S. P., & Bovy, P. H. L. (2004). Pedestrian route-choice and activity scheduling theory and models. Transportation Research Part B: Methodological, 38(2), 169–190.

  53. 53.

    Hoogendoorn, S. P., Bovy, P. H. L., & Daamen, W. (2001). Microscopic pedestrian wayfinding and dynamics modelling. In 1th international conference on pedestrian and evacuation dynamics (pp. 124–154).

  54. 54.

    Johansson, F., Peterson, A., & Tapani, A. (2015). Waiting pedestrians in the social force model. Physica A: Statistical Mechanics and its Applications, 419(419), 95–107.

  55. 55.

    Jorgensen, C. J., & Lamarche, F. (2014). Space and time constrained task scheduling for crowd simulation. Technical Report hal-00940570, PI 2013.

  56. 56.

    Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society, 47, 263–291.

  57. 57.

    Kielar, P. M., Biedermann, D. H., & André, B. (2016). MomenTUMv2: A modular, extensible, and generic agent-based pedestrian behavior simulation framework. Technical Report TUM-I1643, Technische Universität Müchen.

  58. 58.

    Kielar, P. M., Biedermann, D. H., Kneidl, A., & Borrmann, A. (2017). A unified pedestrian routing model for graph-based wayfinding built on cognitive principles. Transportmetrica A: Transport Science.

  59. 59.

    Kielar, P. M., & Borrmann, A. (2016). Coupling spatial task solving models to simulate complex pedestrian behavior patterns. In 8th international conference on pedestrian and evacuation dynamics (pp. 229–235).

  60. 60.

    Kielar, P. M., & Borrmann, A. (2016). Modeling pedestrians interest in locations: A concept to improve simulations of pedestrian destination choice. Simulation Modelling Practice and Theory, 61, 47–62.

  61. 61.

    Kielar, P. M., Handel, O., Biedermann, D. H., & Borrmann, A. (2014). Concurrent hierarchical finite state machines for modeling pedestrian behavioral tendencies. Transportation Research Procedia, 2, 584–593.

  62. 62.

    Kieras, D. E., & Meyer, D. E. (1995). An overview of the EPIC architecture for cognition and performance with application to human-computer interaction. Technischer Bericht 5, University of Michigan.

  63. 63.

    Klüpfel, H. (2007). The simulation of crowd dynamics at very large events calibration, empirical data, and validation. In 3th international conference on pedestrian and evacuation dynamics (pp. 285–296).

  64. 64.

    Kneidl, A. (2015). How do people queue a study of different queuing models. In Proceedings of the 11th conference on traffic and granular flow.

  65. 65.

    Kneidl, A., Borrmann, A., & Hartmann, D. (2012). Generation and use of sparse navigation graphs for microscopic pedestrian simulation models. Advanced Engineering Informatics, 26(4), 669–680.

  66. 66.

    Köster, G., Treml, F., & Gödel, M. (2013). Avoiding numerical pitfalls in social force models. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 87(6), 1–13.

  67. 67.

    Kwak, J., Jo, H. H., Luttinen, T., & Kosonen, I. (2014). Modeling pedestrian switching behavior for attractions. Transportation Research Procedia, 2, 612–617.

  68. 68.

    Laird, J. E. (2008). Extending the soar cognitive architecture. Frontiers in Artificial Intelligence and Applications, 171, 224–235.

  69. 69.

    Laird, J. E., Newell, A., & Rosenbloom, P. S. (1987). Soar: An architecture for general intelligence. Artificial Intelligence, 33(1), 1–64.

  70. 70.

    Langley, P., Laird, J. E., & Rogers, S. (2009). Cognitive architectures: Research issues and challenges. Cognitive Systems Research, 10(2), 141–160.

  71. 71.

    Lappe, M., Jenkin, M., & Harris, L. R. (2007). Travel distance estimation from visual motion by leaky path integration. Experimental Brain Research, 180(1), 35–48.

  72. 72.

    Lewandowsky, S., & Farrell, S. (2010). Computational modeling in cognition: Principles and practice. Thousand Oaks, CA: Sage Publications.

  73. 73.

    Lewin, K., & Cartwright, D. (1952). Field theory in social science: Select theoretical papers (edited by Dorwin Cartwright). London: Tavistock.

  74. 74.

    Liddle, J., Seyfried, A., Klingsch, W., Rupprecht, T., Schadschneider, A., & Winkens, A. (2009). An experimental study of pedestrian congestions: influence of bottleneck width and length. arXiv preprint arXiv:0911.4350.

  75. 75.

    Lindberg, E. (2013). Adults’ memory representations of the spatial properties of their everyday physical environment. In The development of spatial cognition (p. 141).

  76. 76.

    Masicampo, E., & Ambady, N. (2014). Predicting fluctuations in widespread interest: Memory decay and goal-related memory accessibility in Internet search trends. Journal of Experimental Psychology: General, 143(1), 205–214.

  77. 77.

    Moussaïd, M., Helbing, D., & Theraulaz, G. (2011). How simple rules determine pedestrian behavior and crowd disasters. Proceedings of the National Academy of Sciences, 108(17), 6884–6888.

  78. 78.

    Moussaïd, M., Perozo, N., Garnier, S., Helbing, D., & Theraulaz, G. (2010). The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PLoS ONE, 5(4), 1–7.

  79. 79.

    Newell, A., Simon, H. A., et al. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.

  80. 80.

    Pan, X., Han, C. S., Dauber, K., & Law, K. H. (2007). A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations. Ai & Society, 22(2), 113–32.

  81. 81.

    Paris, S., & Donikian, S. (2009). Activity-driven populace: A cognitive approach to crowd simulation. IEEE Computer Graphics and Applications, 29(4), 34–43.

  82. 82.

    Park, J. H., Rojas, F. A., & Yang, H. S. (2013). A collision avoidance behavior model for crowd simulation based on psychological findings. Computer Animation and Virtual Worlds, 24(3–4), 173–183.

  83. 83.

    Pelechano, N., O’Brien, K., Silverman, B. G., & Badler, N. (2005). Crowd simulation incorporating agent psychological models, roles and communication. Center for Human Modeling and Simulation University of Pennsylvania.

  84. 84.

    Peters, C., & Ennis, C. (2009). Modeling groups of plausible virtual pedestrians. IEEE Computer Graphics and Applications, 29(4), 54–63.

  85. 85.

    Phillips, F., & Layton, O. (2009). The traveling salesman problem in the natural environment. Journal of Vision, 9(8), 1145.

  86. 86.

    Rumbaugh, J., Jacobson, I., & Booch, G. (2004). Unified modeling language reference manual. London: The Pearson Higher Education.

  87. 87.

    Russell, S. J., Norvig, P., Canny, J. F., Malik, J. M., & Edwards, D. D. (2003). Artificial intelligence: A modern approach (Vol. 2). Englewood Cliffs, NJ: Prentice-Hall.

  88. 88.

    Säisä, J., & Gärling, T. (1987). Sequential spatial choices in the large-scale environment. Environment and Behavior, 19(5), 614–635.

  89. 89.

    Scheiner, J. (2014). The gendered complexity of daily life: effects of life-course events on changes in activity entropy and tour complexity over time. Travel Behaviour and Society, 1(3), 91–105.

  90. 90.

    Seitz, M., Köster, G., & Pfaffinger, A. (2014). Pedestrian group behavior in a cellular automaton. Pedestrian and Evacuation Dynamics, 2012, 807–814.

  91. 91.

    Shao, W., & Terzopoulos, D. (2007). Autonomous pedestrians. Graphical Models, 69(5–6), 246–274.

  92. 92.

    Taatgen, N. A., Lebiere, C., & Anderson, J. R. (2006). Modeling paradigms in ACT-R. In Cognition and multi-agent interaction: From cognitive modeling to social simulation (pp. 29–52). New York: Cambridge University Press.

  93. 93.

    Timmermans, H. J. P., van der Hagen, X., & Borgers, A. W. J. (1992). Transportation systems, retail environments and pedestrian trip chaining behaviour: Modelling issues and applications. Transportation Research Part B: Methodological, 26(1), 45–59.

  94. 94.

    Tulving, E. (1972). Episodic and semantic memory. Organization of Memory. London: Academic, 381(4), 382–404.

  95. 95.

    Tulving, E. (1986). Episodic and semantic memory: Where should we go from here? Behavioral and Brain Sciences, 9(3), 573–577.

  96. 96.

    Urbani, L. (2012). Commuters rail sations and pedestrians flows: The Hardbrücke station in Zurich, Switzerland. Procedia-Social and Behavioral Sciences, 53, 146–154.

  97. 97.

    von Sivers, I., Seitz, M. J., & Köster, G. (2016). How do people search: A modelling perspective. In Proceedings of the 11th international conference of parallel processing and applied mathematics (pp. 487–496).

  98. 98.

    Wagner, N., & Agrawal, V. (2014). An agent-based simulation system for concert venue crowd evacuation modeling in the presence of a fire disaster. Expert Systems with Applications, 41(6), 2807–2815.

  99. 99.

    Wang, R. F. (2004). Between reality and imagination: When is spatial updating automatic? Perception & Psychophysics, 66(1), 68–76.

  100. 100.

    Wiener, J. M., Büchner, S. J., & Hölscher, C. (2009). Taxonomy of human wayfinding tasks: A knowledge-based approach. Spatial Cognition & Computation, 9(2), 152–165.

  101. 101.

    Wijermans, N., Conrado, C., van Steen, M., Martella, C., & Li, J. (2016). A landscape of crowd-management support: An integrative approach. Safety Science, 86, 142–164.

  102. 102.

    Wijermans, N., Jorna, R., Jager, W., van Vliet, T., & Adang, O. (2013). CROSS: Modelling crowd behaviour with social-cognitive agents. Journal of Artificial Societies and Social Simulation, 16(4), 1.

  103. 103.

    Williams, L. (1978). Casting curved shadows on curved surfaces. ACM Siggraph Computer Graphics, 12(3), 270–274.

  104. 104.

    Willingham, D. B., Nissen, M. J., & Bullemer, P. (1989). On the development of procedural knowledge. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15(6), 1047.

  105. 105.

    Wolbers, T., & Hegarty, M. (2010). What determines our navigational abilities? Trends in Cognitive Sciences, 14(3), 138–146.

  106. 106.

    Wooldridge, M. (2009). An introduction to multiagent systems (second ed.). New York: Wiley.

Download references


This work was partially supported by the Federal Ministry for Education and Research (Bundesministerium für Bildung und Forschung, BMBF), project MultikOSi, under Grant FKZ 13N12823. We would like to thank Prof. Hölscher, Chair of Cognitive Sciences at the ETH-Zürich and his team for fruitful discussions. Also, we thank our student assistants for contributing to the pedestrian simulation framework MomenTUM.

Author information

Correspondence to Peter M. Kielar.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (mp4 15193 KB)

Supplementary material 1 (mp4 15193 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kielar, P.M., Borrmann, A. Spice: a cognitive agent framework for computational crowd simulations in complex environments. Auton Agent Multi-Agent Syst 32, 387–416 (2018).

Download citation


  • Spatial sequential choice
  • Cognitive agent
  • Multi-agent simulation
  • Pedestrian dynamics
  • Crowd simulation