License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GISCIENCE.2018.1
URN: urn:nbn:de:0030-drops-93293
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2018/9329/
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Amores, David ; Vasardani, Maria ; Tanin, Egemen

Early Detection of Herding Behaviour during Emergency Evacuations

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LIPIcs-GISCIENCE-2018-1.pdf (0.5 MB)

Abstract

Social scientists have observed a number of irrational behaviours during emergency evacuations, caused by a range of possible cognitive biases. One such behaviour is herding - people following and trusting others to guide them, when they do not know where the nearest exit is. This behaviour may lead to safety under a knowledgeable leader, but can also lead to dead-ends. We present a method for the automatic early detection of herding behaviour to avoid suboptimal evacuations. The method comprises three steps: (i) people clusters identification during evacuation, (ii) collection of clusters' spatio-temporal information to extract features for describing cluster behaviour, and (iii) unsupervised learning classification of clusters' behaviour into 'benign' or 'harmful' herding. Results using a set of different detection scores show accuracies higher than baselines in identifying harmful behaviour; thus, laying the ground for timely irrational behaviour detection to increase the performance of emergency evacuation systems.

BibTeX - Entry

@InProceedings{amores_et_al:LIPIcs:2018:9329,
  author =	{David Amores and Maria Vasardani and Egemen Tanin},
  title =	{{Early Detection of Herding Behaviour during Emergency Evacuations}},
  booktitle =	{10th International Conference on Geographic Information  Science (GIScience 2018)},
  pages =	{1:1--1:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Stephan Winter and Amy Griffin and Monika Sester},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/9329},
  URN =		{urn:nbn:de:0030-drops-93293},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.1},
  annote =	{Keywords: spatio-temporal data, emergency evacuations, herding behaviour}
}

Keywords: spatio-temporal data, emergency evacuations, herding behaviour
Collection: 10th International Conference on Geographic Information Science (GIScience 2018)
Issue Date: 2018
Date of publication: 02.08.2018

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