Early Detection of Herding Behaviour during Emergency Evacuations

Authors David Amores, Maria Vasardani, Egemen Tanin

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David Amores
  • Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
Maria Vasardani
  • Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
Egemen Tanin
  • Computing and Information Systems, The University of Melbourne, Parkville, VIC 3010, Australia

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David Amores, Maria Vasardani, and Egemen Tanin. Early Detection of Herding Behaviour during Emergency Evacuations. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 1:1-1:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


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.

Subject Classification

ACM Subject Classification
  • Information systems → Location based services
  • Computing methodologies → Spatial and physical reasoning
  • spatio-temporal data
  • emergency evacuations
  • herding behaviour


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