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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 As Get 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

  1. Mohammed Alkhadim, Kassim Gidado, and Noel Painting. Risk management: The effect of fist on perceived safety in crowded large space buildings. Safety Science, 108(November 2017):29-38, 2018. URL: https://doi.org/10.1016/j.ssci.2018.04.021.
  2. A. Celil Cakici, Nurhayat Iflazoglu, and Levent Altinay. Impact of crowded restaurant perception on affectivity and behavioral intentions. Tourism, 69(3):429-492, 2021. URL: https://doi.org/10.37741/T.69.3.8.
  3. Thomas Campagnaro, Daniel Vecchiato, Arne Arnberger, Riccardo Celegato, Riccardo Da Re, Riccardo Rizzetto, Paolo Semenzato, Tommaso Sitzia, Tiziano Tempesta, and Dina Cattaneo. General, stress relief and perceived safety preferences for green spaces in the historic city of Padua (Italy). Urban Forestry and Urban Greening, 52(March):126695, 2020. URL: https://doi.org/10.1016/j.ufug.2020.126695.
  4. Clayson Celes, Azzedine Boukerche, and Antonio A.F. Loureiro. Crowd management: A new challenge for urban big data analytics. IEEE Communications Magazine, 57(4):20-25, 2019. URL: https://doi.org/10.1109/MCOM.2019.1800640.
  5. Achituv Cohen, Sagi Dalyot, and Asya Natapov. Machine learning for predicting pedestrian activity levels in cities. In Anahid Basiri, Georg Gartner, and Haosheng Huang, editors, Proceedings of the 16th International Conference on Location Based Services (LBS 2021), 2021. Google Scholar
  6. Tom Cox, Jonathan Houdmont, and Amanda Griffiths. Rail passenger crowding, stress, health and safety in britain. Transportation Research Part A: Policy and Practice, 40(3):244-258, 2006. URL: https://doi.org/10.1016/j.tra.2005.07.001.
  7. Laure De Cock, Nico Van de Weghe, Kristien Ooms, Nina Vanhaeren, Matteo Ridolfi, Eli De Poorter, and Philippe De Maeyer. Taking a closer look at indoor route guidance; usability study to compare an adapted and non-adapted mobile prototype. Spatial Cognition and Computation, pages 1-23, February 2021. URL: https://doi.org/10.1080/13875868.2021.1885411.
  8. Stijn Denis, Ben Bellekens, Maarten Weyn, and Rafael Berkvens. Sensing thousands of visitors using radio frequency. IEEE Systems Journal, 15(4):5090-5093, 2021. URL: https://doi.org/10.1109/JSYST.2020.3019189.
  9. Milad Haghani and Majid Sarvi. Crowd behaviour and motion: Empirical methods. Transportation Research Part B: Methodological, 107:253-294, 2018. URL: https://doi.org/10.1016/j.trb.2017.06.017.
  10. Dirk Helbing, Lubos Buzna, Anders Johansson, and Torsten Werner. Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions. Transportation Science, 39(1):1-24, 2005. URL: https://doi.org/10.1287/trsc.1040.0108.
  11. Bill Hillier and Julienne Hanson. The social logic of space. Cambridge University Press, 1984. Google Scholar
  12. Bin Jiang. Ranking spaces for predicting human movement in an urban environment. International Journal of Geographical Information Science, 23(7):823-837, 2009. URL: https://doi.org/10.1080/13658810802022822.
  13. Zheng Li and David A. Hensher. Crowding and public transport: A review of willingness to pay evidence and its relevance in project appraisal. Transport Policy, 18(6):880-887, 2011. URL: https://doi.org/10.1016/j.tranpol.2011.06.003.
  14. Zheng Li and David A. Hensher. Crowding in public transport: A review of objective and subjective measures. Journal of Public Transportation, 16(2):107-134, 2013. URL: https://doi.org/10.5038/2375-0901.16.2.6.
  15. Manuel Müller, Christina Ohm, Florin Schwappach, and Bernd Ludwig. The path of least resistance: Calculating preference adapted routes for pedestrian navigation. KI - Kunstliche Intelligenz, 31(2):125-134, 2017. URL: https://doi.org/10.1007/s13218-016-0472-6.
  16. Itzhak Omer and Nir Kaplan. Using space syntax and agent-based approaches for modeling pedestrian volume at the urban scale. Computers, Environment and Urban Systems, 64:57-67, 2017. URL: https://doi.org/10.1016/j.compenvurbsys.2017.01.007.
  17. Amos Rapoport. Toward a redefinition of density. Environment and behavior, pages 133-158, 1975. Google Scholar
  18. Martin Raubal, Dominik Bucher, and Henry Martin. Geosmartness for personalized and sustainable future urban mobility. In W. Shi, M. F. Goodchild, M. Batty, and M.-P. Kwan, editors, Urban Informatics, pages 59-83. Springer Singapore, 2021. URL: https://doi.org/10.1007/978-981-15-8983-6_6.
  19. Andreas Schadschneider, Mohcine Chraibi, Armin Seyfried, Antoine Tordeux, and Jun Zhang. Pedestrian dynamics: From empirical results to modeling. In Livio Gibelli and Nicola Bellomo, editors, Crowd Dynamics, Volume 1, volume 1, page 292. Birkhauser, 2018. Google Scholar
  20. Samia Sharmin and Md Kamruzzaman. Meta-analysis of the relationships between space syntax measures and pedestrian movement. Transport Reviews, 38(4):524-550, 2018. URL: https://doi.org/10.1080/01441647.2017.1365101.
  21. Utkarsh Singh, Jean François Determe, François Horlin, and Philippe De Doncker. Crowd monitoring: State-of-the-art and future directions. IETE Technical Review, 2020. URL: https://doi.org/10.1080/02564602.2020.1803152.
  22. Timmermans H. Teklenburg J., Borgers A. Space syntax as a design support system: evaluating alternative layouts for shopping centres. In A. D. Seidel, editor, Banking on design: proceedings of the 25th annual conference of the Environmental Design Research Association, pages 220-228, 1994. Google Scholar
  23. Ke Wang, Xiupeng Shi, Algena Pei Xuan Goh, and Shunzhi Qian. A machine learning based study on pedestrian movement dynamics under emergency evacuation. Fire Safety Journal, 106(April):163-176, 2019. URL: https://doi.org/10.1016/j.firesaf.2019.04.008.
  24. Nanda Wijermans, Claudine Conrado, Maarten van Steen, Claudio Martella, and Jie Li. A landscape of crowd-management support: An integrative approach. Safety Science, 86:142-164, 2016. URL: https://doi.org/10.1016/j.ssci.2016.02.027.
  25. Fábio Lúcio Zampieri, Décio Rigatti, and Cláudio Ugalde. Evaluated model of pedestrian movement based on space syntax, performance measures and artificial neural nets. In Daniel Koch, Lars Marcus, and Jesper Steen, editors, 7th International Space Syntax Symposium, pages 1-8, 2009. URL: https://www.sss7.org/Proceedings/09%20New%20Modes%20of%20Modelling%20and%20Methodological%20Development/S135_Zampieri.pdf.
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