Advertisement

Joint Parametric Modeling of Buildings and Crowds for Human-Centric Simulation and Analysis

  • Muhammad UsmanEmail author
  • Davide SchaumannEmail author
  • Brandon Haworth
  • Mubbasir Kapadia
  • Petros Faloutsos
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1028)

Abstract

Simulating groups of virtual humans (crowd simulation) affords the analysis and data-driven design of interactions between buildings and their occupants. For this to be useful in practice however, crowd simulators must be well coupled with modeling tools in a way that allows users to iteratively use simulation feedback to adjust their designs. This is a non-trivial research and engineering task as designers often use parametric exploration tools early in their design pipelines. To address this issue, we propose a platform that provides a joint parametric representation of (a) a building and the bounds of its permissible alterations, (b) a crowd that populates the environment, and (c) the activities that the crowd engages in. Based on this input, users can systematically run simulations and analyze the results in the form of data-maps, spatialized representations of human-centric analyses. The platform combines Dynamo with SteerSuite, two established tools for parametric design and crowd simulations, to create a familiar node-based workflow. We systematically evaluate the approach by tuning spatial, social, and behavioral parameters to generate human-centric analyses for the design of a generic exhibition space.

Keywords

Human-centric analytics Crowd simulation Parametric modeling Building occupancy Multi-agent systems 

Notes

Acknowledgement

This research has been partially funded by grants from the NSERC Discovery and Create programs, ISSUM and in part by NSF IIS-1703883, NSF S&AS-1723869 and the Murray Fellowship.

References

  1. 1.
    Dynamo: Open source graphical programming for design. https://dynamobim.org/
  2. 2.
    Bafna, S.: Space syntax: a brief introduction to its logic and analytical techniques. Environ. Behav. 35(1), 17–29 (2003)CrossRefGoogle Scholar
  3. 3.
    van den Berg, J., Guy, S.J., Lin, M., Manocha, D.: Reciprocal n-body collision avoidance. In: Pradalier, C., Siegwart, R., Hirzinger, G. (eds.) Robotics Research. Springer Tracts in Advanced Robotics, vol. 70, pp. 3–19. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-19457-3_1CrossRefGoogle Scholar
  4. 4.
    Berseth, G., Kapadia, M., Faloutsos, P.: Robust space-time footsteps for agent-based steering. Comput. Animation Virtual Worlds (2015). https://www.semanticscholar.org/paper/Robust-Space-Time-Footsteps-for-Agent-Based-Berseth/21d3e445853bf9852b6f10d56f473aef7b18f98c
  5. 5.
    Bhatt, M., Schultz, C., Huang, M.: The shape of empty space: human-centred cognitive foundations in computing for spatial design. In: IEEE Symposium on Visual Languages and Human-Centric Computing, pp. 33–40 (2012)Google Scholar
  6. 6.
    Bohannon, R.W.: Comfortable and maximum walking speed of adults aged 20–79 years: reference values and determinants. Age Ageing 26(1), 15–9 (1997)CrossRefGoogle Scholar
  7. 7.
    Brodeschi, M., Pilosof, N.P., Kalay, Y.E.: The definition of semantic of spaces in virtual built environments oriented to BIM implementation. In: Proceedings of Computer Aided Architectural Design Futures, pp. 331–346 (2015)Google Scholar
  8. 8.
    Chu, M.L., Parigi, P., Law, K., Latombe, J.C.: Modeling social behaviors in an evacuation simulator. Comput. Animation Virtual Worlds 25(3–4), 373–382 (2014)CrossRefGoogle Scholar
  9. 9.
    Goldstein, R., Tessier, A., Khan, A.: Schedule-calibrated occupant behavior simulation. In: Proceedings of the 2010 Spring Simulation Multiconference, p. 180. Society for Computer Simulation International (2010)Google Scholar
  10. 10.
    Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407(6803), 487–490 (2000)CrossRefGoogle Scholar
  11. 11.
    Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282 (1995)CrossRefGoogle Scholar
  12. 12.
    Hillier, B., Hanson, J.: The Social Logic of Space. Cambridge University Press, Cambridge (1989)Google Scholar
  13. 13.
    Hölscher, C., Büchner, S.J., Meilinger, T., Strube, G.: Adaptivity of wayfinding strategies in a multi-building ensemble: the effects of spatial structure, task requirements, and metric information. J. Environ. Psychol. 29(2), 208–219 (2009)CrossRefGoogle Scholar
  14. 14.
    Hong, S.W., Lee, Y.G.: The effects of human behavior simulation on architecture major students’ fire egress planning. J. Asian Archit. Build. Eng. 17(1), 125–132 (2018)CrossRefGoogle Scholar
  15. 15.
    Hong, S.W., Schaumann, D., Kalay, Y.E.: Human behavior simulation in architectural design projects: an observational study in an academic course. Comput. Environ. Urban Syst. 60, 1–11 (2016)CrossRefGoogle Scholar
  16. 16.
    Kalay, Y.E.: Architecture’s New Media: Principles, Theories, and Methods of Computer-Aided Design. MIT Press, Cambridge (2004)Google Scholar
  17. 17.
    Kapadia, M., Pelechano, N., Allbeck, J., Badler, N.: Virtual crowds: steps toward behavioral realism. Synth. Lect. Vis. Comput. Comput. Graph. Animat. Comput. Photogr. Imaging 7(2), 1–270 (2015)Google Scholar
  18. 18.
    Kapadia, M., Shoulson, A., Steimer, C., Oberholzer, S., Sumner, R.W., Gross, M.: An event-centric approach to authoring stories in crowds. In: Proceedings of the 9th International Conference on Motion in Games, pp. 15–24. ACM (2016)Google Scholar
  19. 19.
    Karamouzas, I., Heil, P., van Beek, P., Overmars, M.H.: A predictive collision avoidance model for pedestrian simulation. In: Egges, A., Geraerts, R., Overmars, M. (eds.) MIG 2009. LNCS, vol. 5884, pp. 41–52. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-10347-6_4CrossRefGoogle Scholar
  20. 20.
    Kim, T.W., Fischer, M.: Automated generation of user activity-space pairs in space-use analysis. J. Constr. Eng. Manag. 140(5), 04014007 (2014)CrossRefGoogle Scholar
  21. 21.
    LaPlante, J.N., Kaeser, T.P.: The continuing evolution of pedestrian walking speed assumptions. ITE J. Inst. Transp. Eng. 74(9), 32 (2004)Google Scholar
  22. 22.
    Lee, K.H., Choi, M.G., Hong, Q., Lee, J.: Group behavior from video: a data-driven approach to crowd simulation. In: Proceedings of the 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 109–118. Eurographics Association (2007)Google Scholar
  23. 23.
    Lerner, A., Chrysanthou, Y., Lischinski, D.: Crowds by example. In: Computer Graphics Forum, vol. 26, pp. 655–664. Wiley Online Library (2007)Google Scholar
  24. 24.
    Morad, M., Zinger, E., Schaumann, D., Pilosof, N.P., Kalay, Y.: A dashboard model to support spatio-temporal analysis of simulated human behavior in future built environments. In: Symposium on Simulation for Architecture and Urban Design, June 2018Google Scholar
  25. 25.
    Pan, X., Han, C.S., Dauber, K., Law, K.H.: A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations. AI Soc. 22(2), 113–132 (2007)CrossRefGoogle Scholar
  26. 26.
    Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. In: ACM SIGGRAPH Computer Graphics, vol. 21, no. 4, pp. 25–34 (1987)Google Scholar
  27. 27.
    Rittel, H.: Some principles for the design of an educational system for design. J. Architectural Educ. 26(1–2), 16–27 (1971)CrossRefGoogle Scholar
  28. 28.
    Schaumann, D., Breslav, S., Goldstein, R., Khan, A., Kalay, Y.E.: Simulating use scenarios in hospitals using multi-agent narratives. J. Build. Perform. Simul. 10(5–6), 636–652 (2017)CrossRefGoogle Scholar
  29. 29.
    Schaumann, D., Date, K., Kalay, Y.E.: An event modeling language (EML) to simulate use patterns in built environments. In: Proceedings of the Symposium on Simulation for Architecture and Urban Design, Toronto, pp. 189–196 (2017)Google Scholar
  30. 30.
    Shen, W., Shen, Q., Sun, Q.: Building information modeling-based user activity simulation and evaluation method for improving designer-user communications. Autom. Constr. 21, 148–160 (2012)CrossRefGoogle Scholar
  31. 31.
    Simon, H.A.: The Sciences of the Artificial. MIT Press, Cambridge (1969)Google Scholar
  32. 32.
    Singh, S., Kapadia, M., Faloutsos, P., Reinman, G.: An open framework for developing, evaluating, and sharing steering algorithms. In: Egges, A., Geraerts, R., Overmars, M. (eds.) MIG 2009. LNCS, vol. 5884, pp. 158–169. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-10347-6_15CrossRefGoogle Scholar
  33. 33.
    Singh, S., Kapadia, M., Hewlett, B., Reinman, G., Faloutsos, P.: A modular framework for adaptive agent-based steering. In: Symposium on Interactive 3D Graphics and Games, pp. 141–150. ACM (2011)Google Scholar
  34. 34.
    Turner, A., Doxa, M., O’Sullivan, D., Penn, A.: From isovists to visibility graphs: a methodology for the analysis of architectural space. Environ. Planning B: Planning Des. 28(1), 103–121 (2001)CrossRefGoogle Scholar
  35. 35.
    Usman, M., Schaumann, D., Haworth, B., Berseth, G., Kapadia, M., Faloutsos, P.: Interactive spatial analytics for human-aware building design. In: Proceedings of the 11th Annual International Conference on Motion, Interaction, and Games, p. 13. ACM (2018)Google Scholar
  36. 36.
    Woodbury, R.: Elements of Parametric Design. Taylor & Francis Group, Abingdon (2010)Google Scholar
  37. 37.
    Yan, W., Culp, C., Graf, R.: Integrating BIM and gaming for real-time interactive architectural visualization. Automa. Constr. 20(4), 446–458 (2011)CrossRefGoogle Scholar
  38. 38.
    Yan, W., Kalay, Y.E.: Simulating the behavior of users in built environments. J. Archit. Plann. Res. 21(4), 371–384 (2004)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.York UniversityTorontoCanada
  2. 2.Rutgers UniversityNew BrunswickUSA
  3. 3.UHN–TRITorontoCanada

Personalised recommendations