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Predicting Social Density in Mass Events to Prevent Crowd Disasters

  • Bernhard Anzengruber
  • Danilo Pianini
  • Jussi Nieminen
  • Alois Ferscha
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Support Vector Machine Support Vector Regression Time Series Forecast Density Pair Absolute Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Bernhard Anzengruber
    • 1
  • Danilo Pianini
    • 2
  • Jussi Nieminen
    • 1
  • Alois Ferscha
    • 1
  1. 1.Institute for Pervasive ComputingJohannes Kepler University LinzAustria
  2. 2.Department of Computer Science and Engineering - DISIAlma Mater Studiorum Università di BolognaItaly

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