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Managing Crowds: The Possibilities and Limitations of Crowd Information During Urban Mass Events

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Planning Support Systems and Smart Cities

Abstract

This chapter, based on a mixed method research approach, offers insights into possibilities and limitations of using ICT measures for crowd management and distribution during urban mass events (UMEs). Based on literature, practical applications and analyses of research results, we propose crowd management should consider characteristics of both crowds and UMEs to increase information effectiveness. In relation to urban planning, results show that possibilities to influence a crowd’s behavior depend on available (and known) choice sets offered in various locations, while distances towards locations across city centers appear less important. Limitations appear to be related to scarce knowledge on what drives crowd members to adapt or adhere to their activity choice behavior. Such insights are essential for smart cities striving for an optimal use of infrastructural capacity, as both the ambiguous effects of ICT measures, as well as a crowd’s self-organizing capacity should be taken into account for delaying, solving and preventing disruptions of pedestrian flows in city centers.

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Notes

  1. 1.

    An annually organized 7-day free-access UME in Nijmegen city center, daily attracting up to 300.000 visitors to various attraction locations across the city center. Manifold information measures (both high and low-tech) are deployed to inform, advise, guide, steer and even enforce the crowd.

References

  • Abdel-Aty, M., Kitamura, R., & Jovanis, P. (1997). Using stated preference data for studying the effect of advanced traffic information on drivers’ route choice. Transportation Research Part C: Emerging Technologies, 5(1), 39–50.

    Article  Google Scholar 

  • Berlonghi, A. E. (1995). Understanding and planning for different spectator crowds. Safety Science, 18(4), 239–247.

    Article  Google Scholar 

  • Ben-Akiva, M. E., & Lerman, S. R. (1985). Discrete choice analysis: Theory and application to travel demand. Cambridge: MIT Press.

    Google Scholar 

  • Ben-Akiva, M. E., Bradley, M., Morikawa, T., Benjamin, J., Novak, T., Oppewal, H., & Rao, V. (1994). Combining revealed and stated preferences data. Marketing Letters, 5(4), 335–349.

    Article  Google Scholar 

  • Bharosa, N., Janssen, M., Meijer, S., & Brave, F. (2010). Designing and evaluating dashboards for multi-agency crisis preparation: a living lab. In Electronic Government (pp. 180–191). Berlin, Heidelberg: Springer.

    Google Scholar 

  • Challenger, R., Clegg, C., & Robinson, M. A. (2010). Understanding crowd behaviours, Vol. 1 Practical guidance and lessons identified. London: The Stationery Office (TSO).

    Google Scholar 

  • Chorus, C., Walker, J., & Ben-Akiva, M. (2013). A joint model of travel information acquisition and response to received messages. Transportation Research Part C: Emerging Technologies, 26, 61–77.

    Article  Google Scholar 

  • Dijk, J., Boeschoten, T., Tije, S., & Wijngaert, L. (2013). De weg naar Haren: de rol van jongeren, sociale media, massamedia en autoriteiten bij de mobilisatie voor Project X Haren: Deelrapport 2.

    Google Scholar 

  • Fruin, J. J. (1993). The causes and prevention of crowd disasters. In Engineering for crowd safety, (pp. 99–108). Amsterdam: Elsevier.

    Google Scholar 

  • Helbing, D., & Molnar, P. (1998). Self-organization phenomena in pedestrian crowds. arXiv preprint cond-mat/9806152.

    Google Scholar 

  • Helbing, D., Buzna, L., Johansson, A., & Werner, T. (2005). Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions. Transportation science, 39(1), 1–24.

    Article  Google Scholar 

  • Helbing, D., & Mukerji, P. (2012). Crowd disasters as systemic failures: Analysis of the love parade disaster. EPJ Data Science, 1(1), 1–40.

    Article  Google Scholar 

  • Hensher, D. A. (1994). Stated preference analysis of travel choices: The state of practice. Transportation, 21(2), 107–133.

    Article  Google Scholar 

  • Hoogendoorn, S. P., & Bovy, P. H. (2004). Pedestrian route-choice and activity scheduling theory and models. Transportation Research Part B Methodological, 38(2), 169–190.

    Article  Google Scholar 

  • Hoogendoorn, S. P., & Daamen, W. (2004). Self-organization in walker experiments. In Proceedings of the 5 th Symposium on Traffic and Granular Flow, (pp. 121–131).

    Google Scholar 

  • Hoogendoorn, S. P. (2013, Novemeber 21–23). Why traffic management works…and why coordinated traffic management will work even better. Presentation at the 3rd EU-US NAE Frontiers of Engineering Symposium, Chantilly.

    Google Scholar 

  • Hoogendoon-Lanser, S. (2005). Modeling travel behavior for multi-modal transport networks. Dissertation, TRAIL Research School, TU Delft.

    Google Scholar 

  • Meulman, J. J., & Heiser, W. J. (1989). IBM SPSS Categories 20.

    Google Scholar 

  • Kemperman, A. (2000). Temporal aspects of theme park choice behavior. Eindhoven, The Netherlands: Technische Universiteit Eindhoven.

    Google Scholar 

  • Kitamura, R. (1988). An evaluation of activity-based travel analysis. Transportation, 15(1–2), 9–34.

    Google Scholar 

  • Kurose, S., Borgers, A. W. J., & Timmermans, H. J. P. (2001). Classifying pedestrian shopping behaviour according to implied heuristic choice rules. Environment and Planning B: Planning and Design, 28(3), 405–418.

    Article  Google Scholar 

  • Mamoli, M., Michieletto, P., Bazzani, A., & Giorgini, B. (2012). Venice as pedestrian city and tourist magnet mass events and ordinary life. Ara Journal of Tourism Research, 93–102.

    Google Scholar 

  • Moussaïd, M., Perozo, N., Garnier, S., Helbing, D., & Theraulaz, G. (2010). The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PloS one, 5(4), e10047.

    Google Scholar 

  • Muizelaar, T. J. (2011). Non-recurrent traffic situations and traffic information: determining preferences and effects on route choice. Dissertation, TRAIL Research School, Twente University.

    Google Scholar 

  • Pan, X., Han, C. S., Dauber, K., & Law, K. H. (2007). A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations. AI & SOCIETY, 22(2), 113–132.

    Article  Google Scholar 

  • Sanko, N. (2001). Guidelines for stated preference experiment design. Ecole Nationale des Ponts et Chaussées: Master of Business Adminstration diss.

    Google Scholar 

  • Still, G. K. (2000). Crowd dynamics. Dissertation, University of Warwick.

    Google Scholar 

  • Tertoolen, G., Grotenhuis, J., Lankhuijzen, R. (2012). Human factors en dynamisch verkeersmanagement: Waar psychologie en techniek samenkomen. In Colloquium Vervoersplanologisch Speurwerk, Amsterdam, 22–23 November 2012.

    Google Scholar 

  • Timmermans, H. J. P. (2009). Pedestrian behavior: Models, data collection and applications. Gloucester: Emerald Group Publishing.

    Google Scholar 

  • Tonkin, E., Pfeiffer, H. D., & Tourte, G. (2012). Twitter, information sharing and the London riots? Bulletin of the American Society for Information Science and Technology, 38(2), 49–57.

    Article  Google Scholar 

  • Treurniet, W. (2014). Shaping Comprehensive Emergency Response Networks. In Network Topology in Command and Control: Organization, Operation, and Evolution: Organization, Operation, and Evolution, 26.

    Google Scholar 

  • Wegener, M. (2004). Overview of land-use transport models. Handbook of Transport Geography and Spatial Systems, 5, 127–146.

    Google Scholar 

  • World Health Organization. (2008). Communicable disease alert and response for mass gatherings: Key considerations. Accessed 20 May, 2013 from http://www.who.int/csr/mass_gathering/en/print.html.

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Acknowledgments

This research is mainly based on research conducted as part of a master thesis at Delft University of Technology, in collaboration with Royal Haskoning/DHV. We thank Jan Anne Annema, Hans Marinus and Marian Weltevreden for their valuable comments during the process. In addition we thank the anonymous reviewers for their comments and suggestions.

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Correspondence to Lara Britt Zomer .

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Zomer, L.B., Daamen, W., Meijer, S., Hoogendoorn, S.P. (2015). Managing Crowds: The Possibilities and Limitations of Crowd Information During Urban Mass Events. In: Geertman, S., Ferreira, Jr., J., Goodspeed, R., Stillwell, J. (eds) Planning Support Systems and Smart Cities. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-18368-8_5

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