Predicting Social Density in Mass Events to Prevent Crowd Disasters.


Bernhard Anzengruber, Danilo Pianini, Jussi Nieminen, Alois Ferscha

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

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) dblp
 @inproceedings{socinfo2013,
 added-at = {2013-11-20T00:00:00.000+0100},
 author = {Anzengruber, Bernhard and Pianini, Danilo and Nieminen, Jussi and Ferscha, Alois},
 biburl = {http://www.bibsonomy.org/bibtex/2845c2785c0e54626803bd0581698c568/dblp},
 booktitle = {SocInfo},
 crossref = {conf/socinfo/2013},
 editor = {Jatowt, Adam and Lim, Ee-Peng and Ding, Ying and Miura, Asako and Tezuka, Taro and Dias, Gaël and Tanaka, Katsumi and Flanagin, Andrew J. and Dai, Bing Tian},
 ee = {http://dx.doi.org/10.1007/978-3-319-03260-3_18},
 interhash = {85881e57156ecd6a86b4a779fc055fae},
 intrahash = {845c2785c0e54626803bd0581698c568},
 isbn = {978-3-319-03259-7},
 keywords = {dblp},
 pages = {206-215},
 publisher = {Springer},
 series = {Lecture Notes in Computer Science},
 timestamp = {2013-11-20T00:00:00.000+0100},
 title = {Predicting Social Density in Mass Events to Prevent Crowd Disasters.},
 url = {http://dblp.uni-trier.de/db/conf/socinfo/socinfo2013.html#AnzengruberPNF13},
 volume = 8238,
 year = 2013
}
 

Events

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

Tags:

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

— sort

paper in proceedings

Venue

— volume

SocInfo

— series

Lecture Notes in Computer Science

— volume

8238

— pages

206-215

— publication date

2013

URLs

original page

Identifiers

— print ISBN

978-3-319-03259-7

BibTeX

— BibTeX ID
conf/socinfo/AnzengruberPNF13
— BibTeX category
inproceedings
— BibTeX cross reference
conf/socinfo/2013

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