Design Loop: Calibration of a Simulation of Productive Congestion Through Real-World Data for Generative Design Frameworks

  • Lorenzo VillaggiEmail author
  • James Stoddart
  • Pan Zhang
  • Alex Tessier
  • David Benjamin
Conference paper


This paper extends the applicability of generative design for space planning frameworks for ongoing and guided post-occupancy modifications. It involves the comparison of a graph-based productive-congestion simulation with empirical data and the use of a metaheuristic search algorithm to calibrate and fine-tune simulation parameters for greater accuracy. This methodology is demonstrated through a real-world generative designed case-study and the post-occupancy collection and processing of movement data through custom computer vision workflows.


Simulation calibration Post-occupancy Generative design 



We thank Liviu Calin and John Yee from Autodesk Research for leading and assisting with the development of the tracking pipeline and its implementation.


  1. 1.
    Nagy, D., Lau, D., Locke, J., Stoddart, J., Villaggi, L., Wang, R., Zhao, D., Benjamin, D.: Project discover: an application of generative design for architectural space planning. In: Proceedings of the Symposium on Simulation for Architecture and Urban Design, p. 7. Society for Computer Simulation International (2017)Google Scholar
  2. 2.
    Duffy, F.: Work and the City. Edge Futures/Black Dog Publishing, London (2008)Google Scholar
  3. 3.
    Brand, S.: How Buildings Lear: What Happens After They’re Built. Penguin Books, New York (1994)Google Scholar
  4. 4.
    Lindsay, G.: Engineering serendipity. New York Times. Accessed 09 June 2019
  5. 5.
    Silverman, R.E.: The science of serendipity in the workplace. Accessed 09 June 2019
  6. 6.
    Brown, C., Efstratiou, C., Leontiadis, I., Quercia, D., Mascolo, C.: Tracking serendipitous interactions: how individual cultures shape the office. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 1072–1081. ACM (2014)Google Scholar
  7. 7.
    Nagy, D., Villaggi, L., Stoddart J., Benjamin, D.: The Buzz Metric: a graph-based method for quantifying productive congestion in generative space planning for architecture. Technol.|Arch. + Des. 1, 64–73 (2017)Google Scholar
  8. 8.
    Villaggi, L., Nagy, D.: Generative design for architectural space planning: The Case of Autodesk University 2017 Layout. Accessed 08 June 2019
  9. 9.
    Nagy, D., Villaggi, L., Stoddart, J., Benjamin, D.: Beyond heuristics: a novel design space model for generative space planning in architecture. In: ACADIA Conference Proceeding, pp. 436–445 (2017)Google Scholar
  10. 10.
    Dahabreh, I., Issa, J., Chan, J.A., et al.: A review of validation and calibration methods for health care modeling and simulation. In: Modeling and Simulation in the Context of Health Technology Assessment: Review of Existing Guidance, Future Research Needs, and Validity Assessment, Accessed 31 Mar 2019
  11. 11.
    Oreskes, N., Shrader-Frechette, K., Belitz, K.: Verification, validation, and confirmation of numerical models in the earth sciences. Science 263(5147), 641–646 (1994)CrossRefGoogle Scholar
  12. 12.
    Yuan, J., Hui Ng, S., Leung Tsui, K.: Calibration of stochastic computer models using stochastic approximation methods. IEEE Trans. Autom. Sci. Eng. 10(1), 171–186 (2013)CrossRefGoogle Scholar
  13. 13.
    Edwards, R.E.: Automating large-scale simulation calibration to real-world sensor data. Ph.D. dissertation, University of Tennessee (2013)Google Scholar
  14. 14.
    Zhong, J., Hu, N., Cai, W., Lees, M., Luo, L.: Density-based evolutionary framework for crowd model calibration. J. Comput. Sci. 6, 11–22 (2015)CrossRefGoogle Scholar
  15. 15.
    Wolinski, D., Guy, S.J., Olivier, A.H., Lin, M., Manocha, D., Pettré, J.: Parameter estimation and comparative evaluation of crowd simulations. Comput. Graph. Forum 33(2), 303–312 (2014)CrossRefGoogle Scholar
  16. 16.
    Johansson, A., Helbing, D., Shulka, P.K.: Specification of a microscopic pedestrian model by evolutionary adjustment to video tracking data. In: Advances in Complex System, vol. 25. World Scientific Publishing Company (2008)Google Scholar
  17. 17.
    Goodier, R.: The curious science of counting a crowd. Popular Mechanic. Accessed 09 June 2019
  18. 18.
    Cao, Z., Hidalgo, G., Simon, T., Wei, S., Sheikh, Y.: OpenPose: realtime multi-person 2D pose estimation using part affinity fields. arXiv preprint arXiv:1812.08008 (2018)
  19. 19.
    Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools 25, 120–125 (2000)Google Scholar
  20. 20.
    Pele, O., Werman, M.: Fast and robust earth mover’s distance. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 460–467. IEEE (2009)Google Scholar
  21. 21.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.A.M.T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  22. 22.
    Eiben, A.E., Selmar, K.S.: Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm Evol. Comput. 1(1), 19–31 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Lorenzo Villaggi
    • 1
    Email author
  • James Stoddart
    • 1
  • Pan Zhang
    • 2
  • Alex Tessier
    • 2
  • David Benjamin
    • 1
  1. 1.The Living, an Autodesk StudioNew YorkUSA
  2. 2.Autodesk ResearchTorontoCanada

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