Analysis of Simulated Crowd Flow Exit Data: Visualization, Panic Detection and Exit Time Convergence, Attribution, and Estimation

  • Anna Grim
  • Boris Iskra
  • Nianqiao Ju
  • Alona Kryshchenko
  • F. Patricia MedinaEmail author
  • Linda Ness
  • Melissa Ngamini
  • Megan Owen
  • Randy Paffenroth
  • Sui Tang
Part of the Association for Women in Mathematics Series book series (AWMS, volume 17)


This paper describes the results of exploratory analyses of black box simulation data modeling crowds exiting different configurations of a one-story building. The simulation data was created using the SteerSuite platform. Exploratory analysis was performed on the simulation data without knowledge of simulation algorithm. The analysis effort provided a hands-on introduction to issues in crowd dynamics. Analyses focused on visualization, panic detection, exit convergence pattern discovery, identification of parameters influencing exit times, and estimation of exit times. A variety of mathematical and statistical methods were used: k-means clustering, principal component analysis, normalized cut grouping, product formula representation of dyadic measures, logistic regression, auto-encoders, and neural networks. The combined set of results provided insight into the algorithm and the behavior modeled by the algorithm and revealed the need for quantitative features modeling and distinguishing the shapes of the building configurations.



This research started at the Women in Data Science and Mathematics Research Collaboration Workshop (WiSDM), July 17–21, 2017, at the Institute for Computational and Experimental Research in Mathematics (ICERM). The workshop was partially supported by grant number NSF-HRD 1500481-AWM ADVANCE and co-sponsored by the Brown’s Data Science Initiative. Subsequently, the team of collaborators expanded to include Boris Iskra, F. Patricia Medina, and Randy Paffenroth. We gratefully acknowledge their interest in and contributions to the research.

Additional support for some participant travel was provided by DIMACS in association with its Special Focus on Information Sharing and Dynamic Data Analysis. Linda Ness worked on this project during a visit to DIMACS, partially supported by the National Science Foundation under grant number CCF-1445755 and by DARPA SocialSim-W911NF-17-C-0098. Her work has also been funded in part by DARPA SocialSim-W911NF-17-C-0098. F. Patricia Medina received partial travel funding from Worcester Polytechnic Institute, Mathematical Science Department. This work was partially supported by a grant from the Simons Foundation (355824, MO).


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Copyright information

© The Author(s) and the Association for Women in Mathematics 2019

Authors and Affiliations

  • Anna Grim
    • 1
  • Boris Iskra
    • 2
  • Nianqiao Ju
    • 3
  • Alona Kryshchenko
    • 4
  • F. Patricia Medina
    • 2
    Email author
  • Linda Ness
    • 5
  • Melissa Ngamini
    • 6
  • Megan Owen
    • 7
  • Randy Paffenroth
    • 2
  • Sui Tang
    • 8
  1. 1.Brown UniversityProvidenceUSA
  2. 2.Worcester Polytechnic InstituteWorcesterUSA
  3. 3.Harvard UniversityCambridgeUSA
  4. 4.California State University of Channel IslandsCamarilloUSA
  5. 5.Rutgers UniversityNew BrunswickUSA
  6. 6.Morehouse CollegeAtlantaUSA
  7. 7.Lehman College, City University of New YorkBronxUSA
  8. 8.Johns Hopkins UniversityBaltimoreUSA

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