Effective Assessment of AmI Intervention in Traffic Through Quantitative Measures

  • Richard Holzer
  • Matthew Fullerton
  • Nihan Celikkaya
  • Cristina Beltran Ruiz
  • Hermann de Meer
Chapter
Part of the Understanding Complex Systems book series (UCS)

Abstract

This chapter considers the challenge of quantifying the benefit of Ambient Intelligence (AmI) within a complex system, specifically a motorway traffic system. By nature, the deployment of AmI is distributed and inconsistent. Hence, an evaluation strategy must consider the individual to ensure desired or undesired effects are not hidden by only measuring at the whole-system level. For the evaluation we use quantitative measures for self-organizing properties of socio-technical systems. Although the measures are defined analytically for micro-level models, the systems are usually too complex to evaluate the measures analytically. Therefore we use approximation methods based on simulations: Time series received from simulations are used for the approximation of the measures for self-organizing properties. The results of the evaluation can be used for the analysis of the scenario, for the optimization of system parameters and for the assessment of AmI intervention in the system. For the considered devices, the main goal is the increase of safety in traffic by allowing system designers and infrastructure-operators to implement or dynamically choose the most appropriate device and parameters.

References

  1. 1.
    Holzer, R., de Meer, H., Bettstetter, C.: On autonomy and emergence in self-organizing systems. In: IWSOS 2008. Vienna, Austria (2008)Google Scholar
  2. 2.
    Holzer, R., de Meer, H.: Quantitative modeling of self organizing properties. In: Spyropoulos, T., Hummel, K.A. (eds.) IWSOS 2009, LNCS, vol. 5918, pp. 149–161. Zurich, Switzerland (2009)Google Scholar
  3. 3.
    Auer, C., Wuechner, P., de Meer, H.: The degree of global-state awareness in self-organizing systems. In: IWSOS 2009. Zurich, Switzerland (2009)Google Scholar
  4. 4.
    Fullerton, M., Holzer, R., De Meer, H., Beltrán, C.: Novel assessment of a peer-peer road accident survival system. In: IEEE Self-adaptive and Self-organizing Systems Workshop Eval4SASO’12, Lyon, September 2012. IEEE. (2012)Google Scholar
  5. 5.
    Killat, M., Schmidt-Eisenlohr, F., Hartenstein, H., Rössel, C., Vortisch, P., Assenmacher, S., Busch, F.: Enabling efficient and accurate large-scale simulations of VANETs for vehicular traffic management. In: Proceedings of the fourth ACM International Workshop on Vehicular Ad Hoc Networks (VANET), pp. 29–38. Montral, Canada (2007)Google Scholar
  6. 6.
    Highways Agency: M25 controlled motorways summary report. Highways Agency (UK), Technical Report, (2007)Google Scholar
  7. 7.
    Kesting, A., Treiber, M., Schnhof, M., Helbing, D.: Adaptive cruise control design for active congestion avoidance. Transport. Res. C, 16, 668–683. Elsevier, (2008)Google Scholar
  8. 8.
    Klein, A.: Untersuchung der Harmonisierungswirkung von Streckenbeeinflussungsanlagen. In: 4. Aachener Simulations Symposium. Aachen, Germany (2011)Google Scholar
  9. 9.
    Gettman, D., Head, L.: Surrogate safety measures from traffic simulation models. Final Report Federal Highway Administration, Technical Report, (2003)Google Scholar
  10. 10.
    Holzer, R., De Meer, H.: Methods for approximations of quantitative measures in self-organizing systems. In: Proceedings of the 5th International Workshop on Self-Organizing Systems (IWSOS 2011), Lecture Notes in Computer Science (LNCS). Karlsruhe, Germany (2011)Google Scholar
  11. 11.
    Gaugel, T., Schmidt-Eisenlohr, F., Hartenstein, H.: Bericht über die Umsetzung von Simulationsmodellen für die “Vehicle-to-X Communication” im Rahmen des Projektes simTD. Karlsruher Institut für Technologie (KIT), Technical Report, (2011)Google Scholar
  12. 12.
    Park, B., Qi, H.: Microscopic simulation model calibration and validation for freeway work zone network-a case study of VISSIM. In: Intelligent Transportation Systems Conference, 2006. ITSC’06. pp. 1471–1476. Toronto, Canada (2006)Google Scholar
  13. 13.
    Fellendorf, M., Vortisch, P.: Validation of the microscopic traffic flow model VISSIM in different real world situations. In: 80th Annual meeting of Transportation Research Board. Washington DC, USA (2001)Google Scholar
  14. 14.
    Heylighen, F.P.: The science of self-organization and adaptivity. In: Kiel, L.D. (ed.) Knowledge Management, Organizational Intelligence and Learning, and Complexity. The Encyclopedia of Life Support Systems. EOLSS, Oxford, UK (2003)Google Scholar
  15. 15.
    Cover, T.M., Thomas, J.A.: Elements of information theory, 2nd edn. Wiley, New York (2006)Google Scholar
  16. 16.
    Fellendorf, M., Vortisch, P.: Microscopic Traffic Flow Simulator VISSIM. In: Fundamentals of Traffic Simulation. International Series in Operations Research and Management Science, pp. 63–93. Springer, New York (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Richard Holzer
    • 1
  • Matthew Fullerton
    • 2
  • Nihan Celikkaya
    • 2
  • Cristina Beltran Ruiz
    • 3
  • Hermann de Meer
    • 4
  1. 1.University of PassauPassauGermany
  2. 2.Technische UniversitätMünchenGermany
  3. 3.SICESpain
  4. 4.University of PassauPassauGermany

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