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Co-evolution of Intelligent Socio-technical Systems pp 19–41Cite as

Enhancing Crowd Evacuation and Traffic Management Through AmI Technologies: A Review of the Literature

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Part of the book series: Understanding Complex Systems ((UCS))

Abstract

This document is a review of the burgeoning literature on the utilisation of AmI (Ambient Intelligence) technology in two contexts: providing support and enhancing crowd evacuation during emergencies and improving traffic management.

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Notes

  1. 1.

    In fact, this sometimes rendered the choice of publications to be included in this review problematic. In part, the fact that the review contains important amounts of research led by SOCIONICAL partners is an effect of their more frequent, explicit utilisation of the term ‘AmI’. In the absence of this term, we opted for a somewhat conservative view, including only those publications in which the connection to AmI is obvious.

  2. 2.

    For a more comprehensive overview of the diversity of applications of AmI, see [8] and [69, pp. 73–74].

  3. 3.

    Although the study of abnormal crowd behaviour is not limited to emergencies, but rather, touches upon other subjects such as surveillance (see e.g. [42]).

  4. 4.

    A number of the articles we mention below are reviewed in more detail in Silveira Jacques Junior et al. [69].

  5. 5.

    Google Lat Long Blog: Arterial traffic available on Google Maps. http://google-latlong.blogspot.com/2009/08/arterial-traffic-available-on-google.html (2009).

References

  1. Aarts, E., Grotenhuis, F.: Ambient intelligence 2.0: towards synergetic prosperity. J. Ambient Intell. Smart Environ. 3, 3–11 (2011)

    Google Scholar 

  2. Aarts, E., de Ruyter, B.: New research perspectives on ambient intelligence. J. Ambient Intell. Smart Environ. 1, 5–14 (2009)

    Google Scholar 

  3. Abascal, J.: Ambient intelligence for people with disabilities and elderly people. In: ACM’s special interest group on computer-human interaction (SIGCHI), ambient intelligence for scientific discovery (AISD) workshop, Vienna (2004). http://www.andrew.cmu.edu/course/60-427/aisd/elderly.pdf, Accessed 5 Apr 2013

  4. Abowd, G.D., Dey, A.K., Brown, P.J., et al.: Towards a better understanding of context and context-awareness. Handheld Ubiquitous Comput. 1707/1999, 304–307 (1999)

    Google Scholar 

  5. Ali, S., Shah, M.: A Lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In: IEEE conference on computer vision and pattern recognition (CVPR&07), IEEE, Minneapolis (2007)

    Google Scholar 

  6. Andrade, E.L., Blunsden, S., Fisher, R.B.: Modelling crowd scenes for event detection. In: 18th international conference on pattern recognition (ICPR 2006), Vol. 1. IEEE, Hong Kong (2006)

    Google Scholar 

  7. Andrade, E.L., Fisher, R.B.: Simulation of crowd problems for computer vision. In: First international workshop on crowd simulation (V-CROWDS&05), Vol. 3, Lausanne (2005)

    Google Scholar 

  8. Augusto, J.-C., Nakashima, H., Aghajan, H.: Ambient intelligence and smart environments: a state of the art. J. Ambient Intell. Smart 1, 3–31 (2010)

    Google Scholar 

  9. Bainbridge, L.: Ironies of automation. Automatica 19, 2–27 (1983)

    Article  Google Scholar 

  10. Baur, M., Fullerton, M., Busch, F.: Realizing an effective and flexible ITS evaluation strategy through modular and multi-scaled traffic simulation. IEEE Intell. Transport. Syst. Mag. 2, 34–42 (2010)

    Google Scholar 

  11. Benmimoun, A.: Der Fahrer als Vorbild für Fahrerassistenzsysteme? Ein fahrermodell-basierter Ansatz zur Entwicklung von situationsadaptiven FAS. 13. Aachener Kolloquium, Aachen, 04.-06.10.2004 (2004)

    Google Scholar 

  12. Blaschke, T., Hay, G.J., Weng, Q., Resch, B.: Collective sensing: integrating geospatial technologies to understand urban systems—an overview. Remot. Sens. 3, 1743–1776 (2011)

    Article  Google Scholar 

  13. Boghossian, B.A., Velastin, S.A.: Motion-based machine vision techniques for the management of large crowds. In: The 6th IEEE international conference on electronics, circuits and systems, Proceedings of ICECS&99, Vol. 2. IEEE, Pafos (1999)

    Google Scholar 

  14. Bolla, R., Davoli, F.; Road traffic estimation from location tracking data in the mobile cellular network. In: 2000 I.E. Wireless Communications and Networking Conference. Conference Record (Cat. No.00TH8540), pp. 1107–1112. IEEE (2000)

    Google Scholar 

  15. Böhlen, M., Frei, H.: Ambient intelligence in the city overview and new perspectives. In: Nakashima, H., Aghajan, H., Augusto, J.C. (eds.) Handbook of ambient intelligence and smart environment, pp. 911–938. Springer, Boston (2010)

    Chapter  Google Scholar 

  16. Calabrese, F., Colonna, M., Lovisolo, P., Parata, D., Ratti, C.: Real-time urban monitoring using cell phones: a case study in Rome. IEEE Trans. Intell. Transp. Syst. 12, 141–151. Chicago, IL (2011)

    Article  Google Scholar 

  17. Calabrese, F., Ratti, C.: Real time Rome. Netw. Commun. Stud. 3–4, 247–258 (2006)

    Google Scholar 

  18. Cheriyadat, A., Radke, R.: Detecting dominant motions in dense crowds. IEEE J. Sel. Topics Signal Process. 2, 568–581 (2008)

    Article  Google Scholar 

  19. Cook, D.: Ambient intelligence: technologies, applications, and opportunities. Pervasive Mobile Comput. 5, 277–298 (2009)

    Article  Google Scholar 

  20. Cook, D.J., Das, S.K.: How smart are our environments? An updated look at the state of the art. Pervasive Mobile Comput. 3, 53–73 (2007)

    Article  Google Scholar 

  21. Cook, D.J., Das, S.K.: Pervasive computing at scale: transforming the state of the art. Pervasive Mobile Comput. 8, 22–35 (2012)

    Article  Google Scholar 

  22. Cooper, A.K., Ittmann, H.W., Stylianides, T., Schmitz, P.M.U.: Ethical issues in tracking cellular telephones at an event. Omega 37, 1063–1072 (2009)

    Article  Google Scholar 

  23. Dia, H., Panwai, S.: Modelling drivers’ compliance and route choice behaviour in response to travel information. Nonlinear Dynam. 49(4), 493–509 (2007)

    Article  MATH  Google Scholar 

  24. Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Hum. Fact. 37(1), 32–64 (1995)

    Article  Google Scholar 

  25. Ferscha, A., Emsenhuber, B., Riener, A., Holzmann, C., Hechinger, M., Hochreiter, D., Franz, M., Zeidler, A., dos Santos Rocha, M., Klein, C.: Vibro-tactile space-awareness. In: Adjunct proceedings – Ubicomp (2008).

    Google Scholar 

  26. Ferscha, A., Zia, K.: Lifebelt: silent directional guidance for crowd evacuation. In: International symposium on wearable computers (ISWC’09), IEEE, Linz (2009)

    Google Scholar 

  27. Ferscha, A., Zia, K.: LifeBelt: crowd evacuation based on vibro-tactile guidance. IEEE Pervasive Comput. 9, 33–42 (2010)

    Article  Google Scholar 

  28. Ferscha, A., Zia, K., Riener, A., Sharpanskykh, A.: Potential of social modelling in socio-technical systems. Procedia Comput. Sci. 7, 235–237 (2011)

    Article  Google Scholar 

  29. Franklin, D., Flachsbart, J., Hammond, K.: The intelligent classroom. IEEE Intell. Syst. 14, 2–5 (1999)

    Article  Google Scholar 

  30. Gawronski, P., Kułakowski, K., Kämpf, M., Kantelhardt, J.W.: Evacuation in the social force model is not stationary. Acta Phys. Pol. A 121, 7 (2011)

    Google Scholar 

  31. Gawronski, P., Kułakowski, K.: Crowd dynamics – being stuck. Comput. Phys. Commun. 182, 1924–1927 (2011)

    Article  Google Scholar 

  32. Gerritsen, C.: Using ambient intelligence to control aggression in crowds. In: 2011 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (WI-IAT), Vol. 3. IEEE, Lyon (2011)

    Google Scholar 

  33. Gershenfeld, N., Krikorian, R., Cohen, D.: The internet of things. Sci. Am. 291, 76–81 (2004)

    Article  Google Scholar 

  34. Gilbert, N.: Agent-Based Models. Sage, London (2007)

    Google Scholar 

  35. Hassenzahl, M.: The interplay of beauty, goodness and usability in interactive products. Hum. Comput. Interact. 19, 319–349 (2004)

    Article  Google Scholar 

  36. Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51, 4282–4286 (1995)

    Article  Google Scholar 

  37. Herring, R., et al.: Using mobile phones to forecast arterial traffic through statistical learning. In: 89th transportation research board annual meeting, Washington, DC (2010)

    Google Scholar 

  38. Hollands, R.G.: Will the real smart city please stand up? City 12, 303–320 (2008)

    Article  Google Scholar 

  39. Huuskonen, P.: Run to the hills! Ubiquitous computing meltdown. In: Augusto, J.-C., Shapiro, D. (eds.) Advances in Ambient Intelligence, pp. 157–172. Ios Press, Amsterdam (2007)

    Google Scholar 

  40. IOS PRESS: J. Ambient Int. Smart Environ. (2009) ISSN: 1876-1364, http://www.iospress.nl/journal/journal-of-ambient-intelligence-and-smart-environments/ and http://www.jaise-journal.org/

  41. Jarostaw Was, B.G., Matuszyk, P.J.: Social distances model of pedestrian dynamics. Lect. Notes Comput. Sci. 4173, 492–501 (2006)

    Article  Google Scholar 

  42. Jung, C.R., et al.: Detection of unusual motion using computer vision. In: 19th Brazilian symposium on computer graphics and image processing (SIBGRAPI&06), IEEE, Manaus (2006)

    Google Scholar 

  43. Kratz, L., Nishino, K.: Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In: IEEE conference on computer vision and pattern recognition (CVPR 2009), IEEE, Miami (2009)

    Google Scholar 

  44. Lei, W., Li, A., Gao, R., Hao, X., Deng, B.: Simulation of pedestrian crowds’ evacuation in a huge transit terminal subway station. Phys. A. Stat. Mech. Appl. 391, 5355–5365 (2012). (Available on-line: http://linkinghub.elsevier.com/retrieve/pii/S0378437112005377).

  45. Lorincz, K., Malan, D.J., Fulford-Jones, T.R.F., et al.: Sensor networks for emergency response: challenges and opportunities. IEEE Pervasive Comput. 3, 16–23 (2004)

    Article  Google Scholar 

  46. Lu, M., Wevers, K., Van Der Heijden, R.: Technical feasibility of advanced driver assistance systems (ADAS) for road traffic safety. Transp. Plan. Technol. 28(3), 167–187 (2005)

    Article  Google Scholar 

  47. Lukowicz, P., Pentland, S., Ferscha, A.: From context awareness to socially aware computing. IEEE Pervasive Comput. 11, 32–41 (2012)

    Article  Google Scholar 

  48. Ma, R., et al.: On pixel count based crowd density estimation for visual surveillance. In: 2004 IEEE conference on cybernetics and intelligent systems, Vol. 1, IEEE (2004)

    Google Scholar 

  49. Malinowski, J., Kułakowski, K.: Deterministic ants in labirynth – information gained by map sharing. arXiv preprint arXiv:1206.2460 (2012). http://arxiv.org/abs/1206.2460. Accessed 5 Apr 2013

  50. Marsden, G., Mcdonald, M., Brackstone, M.: Towards an understanding of adaptive cruise control. Transport. Res. Part C 9(1), 33–51 (2001)

    Article  Google Scholar 

  51. Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: IEEE conference on computer vision and pattern recognition (CVPR 2009), IEEE, Miami (2009)

    Google Scholar 

  52. Miles, J.C., Chen, K. (eds.): PIARC ITS Handbook, route2market, second edition, (2008)

    Google Scholar 

  53. Miyoshi, T., Nakayasu, H., Ueno, Y., Patterson, P.: An emergency aircraft evacuation simulation considering passenger emotions. Comput. Ind. Eng. 62, 746–754 (2012). (Available on-line: http://linkinghub.elsevier.com/retrieve/pii/S0360835211003354.

    Google Scholar 

  54. Morrison, A., Bell, M., Chalmers, M.: Visualisation of spectator activity at stadium events. In: 2009 13th international conference on information visualisation, IEEE, Barcelona (2009)

    Google Scholar 

  55. Nakasima, I., Aghajan, H., Augusto, J.-C.: In: Nakashima, H., Aghajan, H., Augusto, J.C. (eds.) Handbook of Ambient Intelligence and Smart Environments. Springer, Boston (2010)

    Chapter  Google Scholar 

  56. Ndiaye, A., Gebhard, P., Kipp, M., et al.: Ambient intelligence in edutainment: tangible interaction with life-like exhibit guides. Lect. Notes Comput. Sci. 3814, 104–113 (2005)

    Article  Google Scholar 

  57. Norman, D.: The Invisible Computer: Why Good Products Can Fail, the Personal Computer Is So Complex, and Information Appliances Are the Solution. The MIT Press, Cambridge (1999)

    Google Scholar 

  58. Olaru, A., Gratie, C.: Agent-based, context-aware information sharing for ambient intelligence. Int. J. Artif. Int. Tools 20, 985–1000 (2011)

    Article  Google Scholar 

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

    Article  Google Scholar 

  60. Parasuraman, R., Sheridan, T.B., Wickens, C.D.: A model for types and levels of human interaction with automation. IEEE Trans. Syst. Man Cybern.Part A Syst.Hum. 30, 286–297 (2000)

    Article  Google Scholar 

  61. Pollack, M.E.: Intelligent technology for an aging population: the use of AI to assist elders with cognitive impairment. AI Mag. 26, 9–24 (2005)

    Google Scholar 

  62. Ramos, C., Marreiros, G., Santos, R., Freitas, C.F.: Smart offices and intelligent decision rooms. In: Nakashima, H., Aghajan, H., Augusto, J.C. (eds.) Handbook of Ambient Intelligence and Smart Environments, pp. 851–880. Springer, Boston (2010)

    Chapter  Google Scholar 

  63. Reades, J., Calabrese, F., Sevtsuk, A., Ratti, C.: Cellular census: explorations in urban data collection. IEEE Pervasive Comput. 6, 30–38 (2007)

    Article  Google Scholar 

  64. Remagnino, P., Foresti, G.: Ambient intelligence: a new multidisciplinary paradigm. IEEE Trans. Syst. Man. Cybern. Part A. Syst. Hum. 35, 1–6 (2005)

    Article  Google Scholar 

  65. Sagun, A., Bouchlaghem, D., Anumba, C.J.: Computer simulations vs. building guidance to enhance evacuation performance of buildings during emergency events. Simulat. Model. Pract. Theor. 19, 1007–1019 (2011)

    Article  Google Scholar 

  66. Sharpanskykh, A., Zia, K.: Grouping behaviour in AmI-enabled crowd evacuation. Adv. Intell. Soft. Comput. 92, 233–240 (2011)

    Article  Google Scholar 

  67. Shi, Y., Qin, W., Suo, Y., Xiao, X.: Smart classroom: bringing pervasive computing into distance learning. In: Nakashima, H., Aghajan, H., Augusto, J.C. (eds.) Handbook of ambient intelligence and smart environments, pp. 881–910. Springer, Boston (2010)

    Chapter  Google Scholar 

  68. Sikora, W., Malinowski, J.: Symmetry approach to evacuation scenarios. Lect. Notes Comput. Sci. 6071(2010), 229–241 (2010)

    Article  Google Scholar 

  69. Silveira Jacques Junior, J., Musse, S., Jung, C.: Crowd analysis using computer vision techniques. IEEE Signal Process. Mag. 19, 345–357 (2010)

    Google Scholar 

  70. SOCIONICAL.: Social science literature review: emergency, queue and crowd: definitions and cultural comparisons’ prepared by the SOCIONICAL LSE team (2012)

    Google Scholar 

  71. Tango, F., Montanari, R.: Shaping the drivers’ interaction: how the new vehicle systems match the technological requirements and the human needs. Cogn. Technol. Work 8, 215–226 (2006)

    Article  Google Scholar 

  72. Vaccari, A., Rojas, F.,Ratti, C., Martino, M.: Pulse of the city : visualizing urban dynamics of special events. In: Proceedings of GraphiCon, St.Petersburg pp. 64–71. (2010)

    Google Scholar 

  73. Was, J., Lubas, R., Mysliwiec, W.: Proxemics in discrete simulation of evacuation. Lect. Notes Comput. Sci. 7495(2012), 768–775 (2012)

    Article  Google Scholar 

  74. Weiser, M.: The computer for the 21st century. Sci. Am. 265, 94–104 (1991)

    Article  Google Scholar 

  75. White, J., Quick, J., Philippou, P.: The use of mobile phone location data for traffic information. In: 12th IEE international conference on road transport information and control (RTIC 2004), IET, pp. 321–325 (2004)

    Google Scholar 

  76. Wirz, M., Roggen, D., Troster, G.: Decentralized detection of group formations from wearable acceleration sensors. In: International conference on computational science and engineering (CSE&09), Vol. 4, IEEE, Vancouver (2009)

    Google Scholar 

  77. Wirz, M., Franke, T.,Mitleton-kelly, E., et al.: CoenoSense: A framework for real-time detection and visualization of collective behaviors in human crowds by tracking mobile devices. In: European Conference on Complex Systems 2012 (ECCS’12) track on Social Dynamics 3–7 September 2012, Brussels (2012a)

    Google Scholar 

  78. Wirz, M., et al.: Inferring crowd conditions from pedestrians’ location traces for real-time crowd monitoring during city-scale mass gatherings. In: 2012 IEEE 21st international workshop on enabling technologies: infrastructure for collaborative enterprises (WETICE), IEEE, Toulouse (2012)

    Google Scholar 

  79. Wirz, M., Mitleton-Kelly, E., Franke, T., et al.: Using mobile technology and a participatory sensing approach for crowd monitoring and management during large-scale mass gatherings. In: Co-evolution of Intelligent Socio-Technical Systems: Modelling and Applications in Large Scale Emergency and Transport Domains. Berlin, Springer (2013)

    Google Scholar 

  80. Wright, D.: Alternative futures: AmI scenarios and minority report. Futures 40, 473–488 (2008)

    Article  Google Scholar 

  81. Yau, S.S., Gupta, S. K. S., Karim, F., Ahamed, S.I., Wang, Y., Wang, B.: Smart classroom: enhancing collaborative learning using pervasive computing technology. In: Proceedings of the 6th WFEO world congress on engineering education and the 2nd ASEE international colloquium on engineering education (ASEE &03), Nashville, pp. 13633–13642 (2003)

    Google Scholar 

  82. Zhan, B., Monekosso, D.N., Remagnino, P., Velastin, S.A., Xu, L.-Q.: Crowd analysis: a survey. Mach. Vis. Appl. 19, 345–357 (2008)

    Article  MATH  Google Scholar 

  83. Zheng, X., Zhong, T., Liu, M.: Modeling crowd evacuation of a building based on seven methodological approaches. Build. Environ. 44, 437–445 (2009)

    Article  Google Scholar 

  84. Zia, K., et al.: Scenario based modeling for very large scale simulations. In: 2010 IEEE/ACM 14th international symposium on distributed simulation and real time applications (DS-RT), IEEE, Fairfax (2010)

    Google Scholar 

  85. Munchner Kreis et al.: Zukunft und Zukunftsfahigkeit der Informations und Kommunikationstechnologien und Medien, International Delphi Studie 2030, Nationale IT Gipfel, Stuttgart, 2009

    Google Scholar 

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Authors and Affiliations

  1. The London School of Economics and Political Science, Complexity Research Group, London, UK

    Eve Mitleton-Kelly & Ivan Deschenaux

  2. University of Wurzburg (on traffic), Würzburg, Germany

    Christian Maag

  3. Technical University of Munich (TUM) (on traffic), Munich, Germany

    Matthew Fullerton & Nihan Celikkaya

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  1. Eve Mitleton-Kelly
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    Evangelia Mitleton-Kelly

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Mitleton-Kelly, E., Deschenaux, I., Maag, C., Fullerton, M., Celikkaya, N. (2013). Enhancing Crowd Evacuation and Traffic Management Through AmI Technologies: A Review of the Literature. In: Mitleton-Kelly, E. (eds) Co-evolution of Intelligent Socio-technical Systems. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36614-7_2

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