Mobile Mapping of Sporting Event Spectators Using Bluetooth Sensors: Tour of Flanders 2011

Accurate spatiotemporal information on crowds is a necessity for a better management in general and for the mitigation of potential security risks. The large numbers of individuals involved and their mobility, however, make generation of this information non-trivial. This paper proposes a novel methodology to estimate and map crowd sizes using mobile Bluetooth sensors and examines to what extent this methodology represents a valuable alternative to existing traditional crowd density estimation methods. The proposed methodology is applied in a unique case study that uses Bluetooth technology for the mobile mapping of spectators of the Tour of Flanders 2011 road cycling race. The locations of nearly 16,000 cell phones of spectators along the race course were registered and detailed views of the spatiotemporal distribution of the crowd were generated. Comparison with visual head counts from camera footage delivered a detection ratio of 13.0 ± 2.3%, making it possible to estimate the crowd size. To our knowledge, this is the first study that uses mobile Bluetooth sensors to count and map a crowd over space and time.

[1]  Serge J. Belongie,et al.  Counting Crowded Moving Objects , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[2]  Wee-Seng Soh,et al.  A Comprehensive Study of Bluetooth Signal Parameters for Localization , 2007, 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications.

[3]  Hai Tao,et al.  Counting Pedestrians in Crowds Using Viewpoint Invariant Training , 2005, BMVC.

[4]  Paul S. F. Yip,et al.  How many were there when it mattered? , 2011 .

[5]  Marcus Foth,et al.  Handbook of Research on Urban Informatics: The Practice and Promise of the Real-Time City , 2008 .

[6]  N. V. D. Weghe,et al.  The use of Bluetooth for analysing spatiotemporal dynamics of human movement at mass events: a case study of the Ghent Festivities. , 2012 .

[7]  Sergio A. Velastin,et al.  How close are we to solving the problem of automated visual surveillance? , 2008, Machine Vision and Applications.

[8]  Alan Bensky,et al.  Wireless positioning technologies and applications , 2008 .

[9]  Rein Ahas,et al.  Evaluating passive mobile positioning data for tourism surveys: An Estonian case study , 2008 .

[10]  Jeroen van Schaick,et al.  Sensing Human Activity: GPS Tracking , 2009, Sensors.

[11]  Katherine Meyer,et al.  Collecting Data on Crowds and Rallies: A New Method of Stationary Sampling , 1976 .

[12]  Yinhai Wang,et al.  Pedestrian Travel Pattern Discovery Using Mobile Bluetooth Sensors , 2012 .

[13]  D. Getz Event tourism: Definition, evolution, and research , 2008 .

[14]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[15]  Thomas Liebig,et al.  Analytical workflow of monitoring human mobility in big event settings using Bluetooth , 2011, ISA '11.

[16]  M. Nixon,et al.  On crowd density estimation for surveillance , 2006 .

[17]  George F. Jenks,et al.  ERROR ON CHOROPLETHIC MAPS: DEFINITION, MEASUREMENT, REDUCTION , 1971 .

[18]  Feng Chen,et al.  ESTIMATION OF THE NUMBER OF PEOPLE IN A DEMONSTRATION , 2010 .

[19]  R. Prentice,et al.  FESTIVAL AS CREATIVE DESTINATION , 2003 .

[20]  Peter Reinartz,et al.  Automatic Crowd Analysis from Very High Resolution Satellite Images , 2013 .

[21]  Eric Paulos,et al.  The familiar stranger: anxiety, comfort, and play in public places , 2004, CHI.

[22]  Philip J Tarnoff,et al.  Data Collection of Freeway Travel Time Ground Truth with Bluetooth Sensors , 2010 .

[23]  Hal Berghel Wireless infidelity I: war driving , 2004, CACM.

[24]  Vassilis Kostakos,et al.  Cityware: Urban Computing to Bridge Online and Real-World Social Networks , 2008 .

[25]  Vassilis Kostakos,et al.  Instrumenting the City: Developing Methods for Observing and Understanding the Digital Cityscape , 2006, UbiComp.

[26]  E. Kasimati Economic aspects and the Summer Olympics: a review of related research. , 2003 .

[27]  Manuel Mazo,et al.  Stereo Vision Tracking of Multiple Objects in Complex Indoor Environments , 2010, Sensors.

[28]  Gordon R Waitt,et al.  Social impacts of the Sydney Olympics , 2003 .

[29]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[30]  Rongxing Li,et al.  Mobile Mapping: An Emerging Technology for Spatial Data Acquisition , 1997 .

[31]  Liu Mao,et al.  Analysis of trample disaster and a case study – Mihong bridge fatality in China in 2004 , 2008 .

[32]  Brian S. Peterson,et al.  Bluetooth Inquiry Time Characterization and Selection , 2006, IEEE Transactions on Mobile Computing.