Crowd Spatial Patterns at Bus Stops: Security Implications and Effects of Warning Messages

  • Réka Solymosi
  • Hervé Borrion
  • Taku Fujiyama
Part of the Crime Prevention and Security Management book series (CPSM)


As demonstrated throughout this book, the risk of certain types of crime can increase in congested spaces. Contact crimes, crimes which require the offender to make physical contact with the victim, are especially common in more crowded transport networks and can discourage many would-be passengers (Brand and Price, 2000). Pickpocketing makes up a substantial portion of this, accounting for around 50 per cent of all crime on London’s transport network (Transport for London, 2012). Other chapters in this volume have emphasized the link between pickpocketing and bus stops, and this chapter will delve deeper into the mechanics of crowding at bus stops, and implications for pickpocketing and risk.


Crime Prevention Threshold Distance Warning Message Peripersonal Space Transportation Research Record 
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  1. Antonini, G., Venegas, S., Thiran, J., Bierlaire, M. (2004) A Discrete Choice Pedestrian Behavior Model for Pedestrian Detection in Visual Tracking Systems. RO 2004 09 09, proceeding; TRANSP-OR-REPORT-2004-001.Google Scholar
  2. Baguley, T. (2012) Serious Stats: A Guide to Advanced Statistics for the Behavioral Sciences. Basingstoke: Palgrave Macmillan.Google Scholar
  3. Beller, A., Garelik, S. and Cooper, S. (1980) Sex Crimes in the Subway. Criminology, 18(1), 35–52.CrossRefGoogle Scholar
  4. Brand, S., and Price, R. (2000) The Economic and Social Costs of Crime. London, Home Office Online Report 30/05.Google Scholar
  5. British Transport Police (2011) Give Them an Inch and They’ll Take All They Can. Available at:, accessed 7 August 2012.
  6. Cepolina, E., and Tyler, N. (2005) Understanding Capacity Drop for Designing Pedestrian Environments. Available at:, accessed August 30, 2013.
  7. Davies, A. C., and Velastin, S. A. (2005) A Progress Review of Intelligent CCTV Surveillance Systems. In: IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications; 05–07 Sept 2005, Sofia, Bulgaria.Google Scholar
  8. Eck, J. E., and Liu, L. (2008) Contrasting Simulated and Empirical Experiments in Crime Prevention. Journal of Experimental Criminology, 4(3), 195–213.CrossRefGoogle Scholar
  9. Ekblom, P. (1995) Less Crime, by Design. Annals of the American Academy of Political and Social Science, 539(1), 114–129.CrossRefGoogle Scholar
  10. Evans, G. (2009) Accessibility, Urban Design and the Whole Journey Environment. Built Environment, 35(3), 366–385.CrossRefGoogle Scholar
  11. Felson, M., and Cohen, E. L. (1979) Social Change and Crime Rate Trends: A Routine Activity Approach. American Sociological Review, 44(4), 588–608.CrossRefGoogle Scholar
  12. Fernández, Rodrigo, Pablo Zegers, Gustavo Weber, and Nick Tyler (2010) Influence of Platform Height, Door Width, and Fare Collection on Bus Dwell Time. Transportation Research Record: Journal of the Transportation Research Board, 2143(1), 59–66.CrossRefGoogle Scholar
  13. Fruin, J. (1992) Designing for Pedestrians. Public Transportation. United States. Available online [].
  14. Fujiyama, T., Childs, C. R., Boampomg, D. and Tyler, N. (2005) Investigation of Lighting Levels for Pedestrians: Some questions about Lighting Levels of Current Lighting Standards. Walk 21-VI, Everyday Walking Culture, The 6th International Conference on Walking in the 21st Century, 22–23 September 2005, Zurich, Switzerland.Google Scholar
  15. Helbing, D., Molnar, P., Farkas, I. J., and Bolay, K. (2001) Self-Organizing Pedestrian Movement. Environment and Planning B: Planning and Design, 28(3), 361–383.CrossRefGoogle Scholar
  16. Helbing, D. et al. (2005) Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions. Transportation Science, 39(1), 1–24.CrossRefGoogle Scholar
  17. Helbing, D., and Balietti, S. (2011) How to Do Agent-Based Simulations in the Future: From Modeling Social mechanisms to Emergent Phenomena and Interactive Systems Design. SFI Working Paper: 11-06-024; Available online
  18. Holmes, N. P., and Spence, C. (2004) The Body Schema and the Multisensory Representation(s) of Peripersonal Space. Cognitive processing, 5(2), 94–105.CrossRefGoogle Scholar
  19. Hoogendoorn, S. P., and Daamen, W. (2005) Pedestrian Behavior at Bottlenecks. Transportation Science, 39(2), pp.147–159.CrossRefGoogle Scholar
  20. Jacoby, J., Johar, G. and Morrin, M. (1998) Consumer Behavior: A Quadrennium. Annual Review of Psychology, 49(1), 319–344.CrossRefGoogle Scholar
  21. Kabundi, M., and Normandeau, A. (1987) Crime in the Montreal Subway. International Criminal Police Review, 42(406), 24–27.Google Scholar
  22. Kenney, D. J. (1986) Crime on the Subways: Measuring the Effectiveness of the Guardian Angels. Justice Quarterly, 3(4), 481–496.CrossRefGoogle Scholar
  23. King, B. G. (1948) Measurements of Man for Making Machinery. American Journal of Physical Anthropology, 6(3), 341–351.CrossRefGoogle Scholar
  24. Landon, E. (1971) Order Bias, the Ideal Rating, and the Semantic Differential. Journal of Marketing Research, 8(3), 375–378.CrossRefGoogle Scholar
  25. Liggett, R., Loukaitou-Sideris, A. and Isekl, H. (2001) Bus Stop-Environmental Connection: Do Characteristics of the Built Environment Correlate with Bus Stop Crime? Transportation Research Record, Paper No. 0(613), 20–27.Google Scholar
  26. Loukaitou-Sideris, A. (1999) Hot Spots of Bus Stop Crime: The Importance of Environmental Attributes. Journal of the American Planning Association, 65(4), 395–411.CrossRefGoogle Scholar
  27. Loukaitou-Sideris, A. (2012) Safe on the Move: The Importance of the Built Environment In: Ceccato, V. (ed) The Urban Fabric of Crime and Fear. New York, Dordrecht, London: Springer, 85–110.Google Scholar
  28. Loukaitou-Sideris, A. and Liggett, R. (2000) On Bus-Stop Crime. ACCESS, 16, 18–21.Google Scholar
  29. Loukaitou-Sideris, A. et al. (2001) Measuring the Effects of Built Environment on Bus Stop Crime. Environment and Planning B: Planning and Design, 28(2), 255–280.CrossRefGoogle Scholar
  30. Macal, C. M., and North, M. J. (2010) Tutorial on Agent-Based Modelling and Simulation. Journal of Simulation, 4(3), 151–162. Available at:, accessed 12 July 2012.CrossRefGoogle Scholar
  31. Macintyre, S., and Ross, H. (1996) Danger on the Dance Floor: A Study of the Interior Design, Crowding and Aggression in Nightclubs. In: Ross, H. (ed.) Policing for Prevention: Reducing Crime, Public Intoxication, and Injury. In Crime Prevention Studies, Vol. 7. Monsey, NY: Criminal Justice Press: Criminal Justice Press.Google Scholar
  32. Metropolitan Police (2011) Pickpockets Targeted in Transport Crackdown. Available at:, accessed 5 August 2012.
  33. Moussaïd, M. et al. (2009) Experimental Study of the Behavioural Mechanisms Underlying Self-Organization in Human Crowds. Proceedings. Biological Sciences/The Royal Society, 276(1668), 2755–2762.CrossRefGoogle Scholar
  34. Scholl, H. J. J. (2001) Agent-Based and System Dynamics Modeling: A Call for Cross Study and Joint Research, Proceeding HICSS ’01: Proceedings of the 34th Annual Hawaii International Conference on System Sciences (HICSS-34)-Volume 3 — Volume 3 Page 3003 IEEE Computer Society Washington, DC, USA.Google Scholar
  35. Schultz, P. W., and Tabanico, J. J. (2009) Criminal Beware: A Social Norms Perspective on Posting Public Warning Signs. Criminology, 47(4), 1201–1222.CrossRefGoogle Scholar
  36. Seyfried, A. et al. (2009) New Insight into Pedestrian Flow through Bottlenecks. Transportation Science, 43(3), 395–406.CrossRefGoogle Scholar
  37. Shellow, R., Romualdi, J. P. and Bartel, E. W. (1974) Crime in Rapid Transit Systems: An Analysis and a Recommended Security and Surveillance System. Transportation Research Record, 487, 1–12.Google Scholar
  38. Smith, M. J., and Clarke, R. V. (2000) Crime and Public Transport. Crime and Justice, 27, 169–233.CrossRefGoogle Scholar
  39. Stewart, D. W., and Martin, I. M. (1994) Intended and Unintended Consequences of Warning Messages: A Review and Synthesis of Empirical Research. Journal of Public Policy and Marketing, 13(1), 1–19.Google Scholar
  40. Teknomo, K. (2002) Microscopic Pedestrian Flow Characteristics: Development of an Image Processing Data Collection and Simulation Model. Doctoral Dissertation. Tohoku University.Google Scholar
  41. Transport for London (2006) Accessible Bus Stop Design Guidance (January), 1–64. Available at: [Accessed 31st March 2015).
  42. Transport for London (2011) Travel in London: Report 4. Available at:, accessed 14 August 2012.
  43. Transport for London (2012) 2011/2012 Crime Statistics Bulletin. Available at:, accessed 24 July 2012.
  44. Wijermans, N. et al. (2007) Modelling Crowd Dynamics: Influence Factors Related to the Probability of a Riot. In: Proceedings of the Fourth European Social Simulation Association Conference (ESSA), Toulouse University of Social Sciences 531–541.Google Scholar
  45. Wogalter, M. S., and Laughery, K. R. (1985) Behavioural Effectiveness of Warnings. In: Proceedings of the Human Factors Society 29th Annual Meeting, Santa Monica, CA Human Factors and Ergonomics Society, 679–683.Google Scholar
  46. Yavuz, N., Welch, E.W. and Sriraj, P. S. (2007) Individual and Neighborhood Determinants of Perceptions of Bus and Train Safety in Chicago, Illinois: Application of Hierarchical Linear Modeling. Transportation Research Record, 2034(1), 19–26.CrossRefGoogle Scholar

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© Réka Solymosi, Hervé Borrion and Taku Fujiyama 2015

Open Access This Chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Réka Solymosi
  • Hervé Borrion
  • Taku Fujiyama

There are no affiliations available

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