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Smart Crowd Management: The Data, the Users and the Solution (Short Paper)

Authors Laure De Cock , Steven Verstockt, Christophe Vandeviver, Nico Van de Weghe

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Author Details

Laure De Cock
  • Ghent University, Belgium
Steven Verstockt
  • imec, Ghent University, Belgium
Christophe Vandeviver
  • Ghent University, Belgium
Nico Van de Weghe
  • Ghent University, Belgium

Funding

  • De Cock, Laure: BOF/24J/2021/289.

Cite AsGet BibTex

Laure De Cock, Steven Verstockt, Christophe Vandeviver, and Nico Van de Weghe. Smart Crowd Management: The Data, the Users and the Solution (Short Paper). In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 16:1-16:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.COSIT.2022.16

Abstract

This research project is situated in the domain of smart crowd management, a domain that is gaining importance because of the challenges that arise from urbanization, but also the opportunities that come with smart cities. While our cities become more crowded every day, they also become smarter, for example by employing pedestrian tracking sensors. However, the datasets that are generated by these sensors do not allow smart crowd management yet, because they are sparse and not linked to the perception of the crowd. This research will tackle these issues in three steps. First, pedestrian counts will be estimated on streets that have no tracking data by use of deep learning and space syntax data. Next, the perception of crowdedness within the crowd will be linked to the objective pedestrian counts by conducting two user studies, and finally, the resulting subjective pedestrian counts will be used as weights for a routing algorithm. The last step has already been developed as a proof of concept. The routing algorithm, that uses partly simulated data and partly real-time tracking data, has been embedded in a webtool to show stakeholders the potential and goal of this innovative project.

Subject Classification

ACM Subject Classification
  • Software and its engineering → Software design engineering
  • Information systems → Sensor networks
  • Information systems → Location based services
Keywords
  • crowd tracking
  • crowd modeling
  • space syntax
  • deep learning
  • perception
  • routing

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References

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