Paper Info
Title
Inferring Crowd Conditions from Pedestrians' Location Traces for Real-Time Crowd Monitoring during City-Scale Mass Gatherings
Abstract
There is a need for event organizers and emergency response personnel to detect emerging, potentially critical crowd situations at an early stage during city-wide mass gatherings. In this work, we introduce and describe mathematical methods based on pedestrian-behavior models to infer and visualize crowd conditions from pedestrians' GPS location traces. We tested our approach during the 2011 Lord Mayor's Show in London by deploying a system able to infer and visualize in real-time crowd density, crowd turbulence, crowd velocity and crowd pressure. To collection location updates from festival visitors, a mobile phone app that supplies the user with event-related information and periodically logs the device's location was distributed. We collected around four million location updates from over 800 visitors. The City of London Police consulted the crowd condition visualization to monitor the event. As an evaluation of the usefulness of our approach, we learned through interviews with police officers that our approach helps to assess occurring crowd conditions and to spot critical situations faster compared to the traditional video-based methods. With that, appropriate measure can be deployed quickly helping to resolve a critical situation at an early stage.
Year
DOI
Venue
2012
10.1109/WETICE.2012.26
WETICE
Keywords
Field
DocType
behavioural sciences computing,pedestrians,GPS location traces,city-scale mass gatherings,city-wide mass gatherings,critical crowd situations,crowd conditions,crowd pressure,crowd turbulence,crowd velocity,mathematical methods,mobile phone app,pedestrian-behavior models,real-time crowd density,real-time crowd monitoring,Coeno Sense,Collective Behavior,Crowd Sensing,Participatory Sensing
Data science,Visualization,Crowd monitoring,Computer science,Computer security,Crowd density,Global Positioning System,Mobile phone,Participatory sensing,Distributed computing
Conference
ISSN
Citations 
PageRank 
1524-4547
22
1.29
References 
Authors
5
6
Name
Order
Citations
PageRank
Martin Wirz123313.96
Tobias Franke21148.02
Daniel Roggen31851137.05
Eve Mitleton-Kelly4524.05
Paul Lukowicz53287376.79
Gerhard Troster668768.96