Analysis of Simulated Crowd Flow Exit Data: Visualization, Panic Detection and Exit Time Convergence, Attribution, and Estimation
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).
- 1.M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems (2015). https://tensorflow.org
- 2.Y.S. Abu-Mostafa, M. Magdon-Ismail, H.-T. Lin, Learning From Data (e-Chapter) (AMLBook, 2012)Google Scholar
- 4.A. Alami, Morocco Food Stampede Leaves 15 Dead and a Country Shaken (The New York Times, 2017). https://www.nytimes.com/2017/11/19/world/africa/morocco-stampede.html. Accessed 1 Jan 2018
- 5.Y. Amit, P.F. Felzenszwalb, Object detection, in Computer Vision, a Reference Guide (2014), pp. 537–542Google Scholar
- 6.S. Bengio, F. Fessant, D. Collobert, A connectionist system for medium-term horizon time series prediction, in Proceedings of the International Workshop on Application Neural Networks to Telecoms (1995), pp. 308–315Google Scholar
- 11.G. Dorffner, Neural networks for time series processing. Neural Netw. World 6, 447–468 (1996)Google Scholar
- 12.T. Edwards, D.S.W. Tansley, R.J. Frank, N. Davey, N.T. (Nortel Limited), Traffic trends analysis using neural networks, in Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications (1997), pp. 157–164Google Scholar
- 15.R. Gladstone, Death Toll From Hajj Stampede Reaches 2,411 in New Estimate (The New York Times, 2015). https://www.nytimes.com/2015/12/11/world/middleeast/death-toll-from-hajj-stampede.html. Accessed 19 Dec 2017
- 23.A. Krontiris, K. Bekris, M. Kapadia, ACUMEN: activity-centric crowd monitoring using influence maps, in Proceedings of the 29th International Conference on Computer Animation and Social Agents (CASA ’16) (ACM, New York), pp. 61–69Google Scholar
- 24.H. Kumar, Stampede at Mumbai Railway Station Kills at Least 22 (The New York Times, 2017). https://www.nytimes.com/2017/09/29/world/asia/mumbai-railway-stampede-elphinstone.html. Accessed 1 Jan 2018
- 28.W. McKinney, Data structures for statistical computing in python, in Proceedings of the 9th Python in Science Conference, ed. by S. van der Walt, J. Millman (2010), pp. 51–56Google Scholar
- 31.L. Ness, Dyadic product formula representations of confidence measures and decision rules for dyadic data set samples, in Proceedings of the 3rd Multidisciplinary International Social Networks Conference on Social Informatics 2016, Data Science 2016 (ACM, New York, 2016), pp. 35:1–35:8Google Scholar
- 32.K. Pearson, LIII. On lines and planes of closest fit to systems of points in space. Lon. Edinb. Dublin Philos. Mag. J. Sci. 2, 559–572 (1901)Google Scholar
- 33.F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
- 35.S. Singh, M. Kapadia, P. Faloutsos, G. Reinman, An open framework for developing, evaluating, and sharing steering algorithms, in Proceedings of the 2nd International Workshop on Motion in Games (MIG ’09) (Springer, Berlin, 2009), pp. 158–169Google Scholar
- 36.N. Sjarif, S. Shamsuddin, S. Hashim, Detection of abnormal behaviors in crowd scene: a review. Int. J. Adv. Soft Comput. Appl. 4, 1–33 (2012)Google Scholar
- 37.H. Swathi, G. Shivakumar, H. Mohana, Crowd behavior analysis: a survey, in 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT) (IEEE, Piscataway, 2017), pp. 169–178Google Scholar
- 41.C. Zhou, R.C. Paffenroth, Anomaly detection with robust deep autoencoders, in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’17) (ACM, New York, 2017), pp. 665–674Google Scholar