Predicting Social Density in Mass Events to Prevent Crowd Disasters.


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Bernhard Anzengruber, Danilo Pianini, Jussi Nieminen, Alois Ferscha

Adam Jatowt, Ee-Peng Lim, Ying Ding, Asako Miura, Taro Tezuka, Gaël Dias, Katsumi Tanaka, Andrew J. Flanagin, Bing Tian Dai (eds.)
“SocInfo”, pages 206-215
Lecture Notes in Computer Science 8238
Springer
2013

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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Events

  • The 5th International Conference on Social Informatics (SocInfo2013) — 25/11/2013–27/11/2013

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Publication

— authors

Bernhard Anzengruber, Danilo Pianini, Jussi Nieminen, Alois Ferscha

— editors

Adam Jatowt, Ee-Peng Lim, Ying Ding, Asako Miura, Taro Tezuka, Gaël Dias, Katsumi Tanaka, Andrew J. Flanagin, Bing Tian Dai

— status

published

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paper in proceedings

— publication date

2013

— volume

SocInfo

— series

Lecture Notes in Computer Science

— volume

8238

— pages

206-215

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original page

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— print ISBN

978-3-319-03259-7

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