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International Conference on Social Informatics

SocInfo 2013: Social Informatics pp 206–215Cite as

Predicting Social Density in Mass Events to Prevent Crowd Disasters

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8238))

Abstract

Human mobility behavior emerging in social events involving huge masses of individuals bears potential hazards for irrational social densities. We study the emergence of such phenomena in the context of very large public sports events, analyzing how individual mobility decision making induces undesirable mass effects. A time series based approach is followed to predict mobility patterns in crowds of spectators, and related to the event agenda over the time it evolves. Evidence is collected from an experiment conducted in one of the biggest international sports events (the Vienna city marathon with 40.000 actives and around 300.000 spectators). A smartphone app has been developed to voluntarily engage people to provide mobility data (1503 high-quality GPS traces and 1092694 Bluetooth relations have been collected), based on which prediction analysis has been performed. Using this data as training set, we compare density estimation approaches and evaluate them based on their forecasting precision. The most promising approach using Support Vector Regression (SMOreg) achieved prediction accuracies below 2 (root-mean-squared deviation) when compared to actual evidenced density distributions for a 12 minute forecasting interval.

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Author information

Authors and Affiliations

  1. Institute for Pervasive Computing, Johannes Kepler University Linz, Austria

    Bernhard Anzengruber, Jussi Nieminen & Alois Ferscha

  2. Department of Computer Science and Engineering - DISI, Alma Mater Studiorum Università di Bologna, Italy

    Danilo Pianini

Authors
  1. Bernhard Anzengruber
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  2. Danilo Pianini
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  3. Jussi Nieminen
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  4. Alois Ferscha
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Editor information

Editors and Affiliations

  1. Department of Social Informatics, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, 606-8501, Sakyo-ku, Kyoto, Japan

    Adam Jatowt

  2. School of Information Systems, Singapore Management University, 80 Stamford Road, 178902, Singapore

    Ee-Peng Lim  & Bing Tian Dai  & 

  3. Department of Information and Library Science, School of Informatics and Computing, Indiana University, 1320 E. 10th St., LI 011, 47405, Bloomington, IN, USA

    Ying Ding

  4. Department of Integrated Psychological Sciences, Kwansei Gakuin University, 1-1-155 Uegahara, 662-8501, Nishinomiya, Hyogo, Japan

    Asako Miura

  5. Faculty of Library, Information and Media Science, University of Tsukuba, 1-2 Kasuga, 305-8550, Tsukuba City, Ibaraki, Japan

    Taro Tezuka

  6. Department of Computer Science, University of Caen Basse-Normandie, Campus Côte de Nacre, F-14032, Caen Cedex, France

    Gaël Dias

  7. Department of Social Informatics, Graduate School of Informatics,, Kyoto University, 606-8501, Kyoto, Japan

    Katsumi Tanaka

  8. Department of Communication, University of California, 93106, Santa Barbara, CA, USA

    Andrew Flanagin

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Anzengruber, B., Pianini, D., Nieminen, J., Ferscha, A. (2013). Predicting Social Density in Mass Events to Prevent Crowd Disasters. In: Jatowt, A., et al. Social Informatics. SocInfo 2013. Lecture Notes in Computer Science, vol 8238. Springer, Cham. https://doi.org/10.1007/978-3-319-03260-3_18

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  • DOI: https://doi.org/10.1007/978-3-319-03260-3_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03259-7

  • Online ISBN: 978-3-319-03260-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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