Simulation of Handicapped People Finding Their Way Through Transport Infrastructures

  • Helmut Schrom-Feiertag
  • Thomas Matyus
  • Martin Brunnhuber
Conference paper


This paper presents a research effort put into enhancing existing simulation models by including models for the motion and orientation behavior of handicapped people being unfamiliar with a transport infrastructure. On the tactical level the perception of guidance systems is modeled and makes it possible to simulate agent navigation through an unknown infrastructure using the present signage. The guidance information is determined against relevant influencing factors in a simulated virtual 3D environment. For the proof of concept the applicability of the wayfinding algorithm is demonstrated in three different scenarios. Results show that the proposed simulation model facilitates an agent to find its way autonomously through a transport infrastructure based on signage information only. This makes it possible to evaluate the visibility of the guidance system and can reveal areas lacking guidance information for people unfamiliar with the infrastructure especially for elderly and handicapped people with reduced reception capabilities.


Pedestrian simulation Wayfinding Handicapped people Sensory impairment Visibility Virtual 3D environment 



This work is part of the project MASIMO and has been funded by the research program line ways2go within the framework of the Austrian strategic initiative IV2Splus “Intelligent Transport Systems and Services plus” under the project number 819192. We thank our partner the Austrian Federal Railway Company (ÖBB) for the access to the railway station “Praterstern” in Vienna for our field experiments and to the plans of the building.


  1. 1.
    Bauer, D.: Comparing Pedestrian Movement Simulation Models for a Crossing Area Based on Real World Data. In: Peacock, R.D., Kuligowski, E.D., and Averill, J.D. (eds.) Pedestrian and Evacuation Dynamics. pp. 547–556. Springer US, Boston, MA (2011).CrossRefGoogle Scholar
  2. 2.
    Helbing, D., Molnar, P.: Social Force Model for Pedestrian Dynamics. Physical Review E. 51, 4282–4286 (1995).CrossRefGoogle Scholar
  3. 3.
    Johansson, A., Helbing, D.: Analysis of Empirical Trajectory Data of Pedestrians. In: Klingsch, W.W.F., Rogsch, C., Schadschneider, A., and Schreckenberg, M. (eds.) Pedestrian and Evacuation Dynamics 2008. pp. 203–214. Springer Berlin Heidelberg, Berlin, Heidelberg (2010).CrossRefGoogle Scholar
  4. 4.
    Braun, A., Musse, S.R., Oliveira, L.P.L. de, Bodmann, B.E.J.: Modeling Individual Behaviors in Crowd Simulation. 16th International Conference on Computer Animation and Social Agents (CASA 2003). p. 143. IEEE Computer Society, Los Alamitos, CA, USA (2003).Google Scholar
  5. 5.
    Pelechano, N., Allbeck, J.M., Badler, N.I.: Controlling individual agents in high-density crowd simulation. Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation. pp. 99–108. Eurographics Association, San Diego, California (2007).Google Scholar
  6. 6.
    Shao, W., Terzopoulos, D.: Autonomous pedestrians. Graph. Models. 69, 246–274 (2007).CrossRefGoogle Scholar
  7. 7.
    Xie, H., Filippidis, L., Gwynne, S., Galea, E.R., Blackshields, D., Lawrence, P.J.: Signage Legibility Distances as a Function of Observation Angle. Journal of Fire Protection Engineering. 17, 41–64 (2007).CrossRefGoogle Scholar
  8. 8.
    Brunnhuber, M., Schrom-Feiertag, H., Hesina, G., Bauer, D., Purgathofer, W.: Simulation and Visualization of the Behavior of Handicapped People in Virtually Reconstructed Public Buildings. 15th International Conference on Urban Planning and Regional Development in the Information Society, Vienna (2010).Google Scholar
  9. 9.
    Hoogendoorn, S.P., Bovy, P.H..: Pedestrian Route-Choice and Activity Scheduling Theory and Models. Transportation Research, Part B: Methodological. 38, 169–190 (2004).Google Scholar
  10. 10.
    Bovy, P.H.L., Stern, E.: Route choice: wayfinding in transport networks. Kluwer Academic Publishers (1990).Google Scholar
  11. 11.
    Golledge, R.G., Stimson, R.J. (Robert J.: Spatial behavior : a geographic perspective / Reginald G. Golledge, Robert J. Stimson. Guilford Press, New York : (New York : Guilford Press, c1997.).Google Scholar
  12. 12.
    Li, Y., Brimicombe, A.J., Li, C.: Agent-based services for the validation and calibration of multi-agent models. Computers, Environment and Urban Systems. 32, 464–473 (2008).Google Scholar
  13. 13.
    Egger, V., Schrom-Feiertag, H., Ehrenstrasser, L., Telepak, G.: Creating a richer data source for 3D pedestrian flow simulations in public transport. Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research. pp. 20:1–20:4. ACM, New York, NY, USA (2010).Google Scholar
  14. 14.
    Bauer, D., Brändle, N., Seer, S., Ray, M., Kitazawa, K.: Measurement of pedestrian movements - a comparative study on various existing systems. Pedestrian behaviour: Models, data collection and applications, ed. H. Timmermans. pp. 301–319. Emerald Group Publishing (2009).Google Scholar
  15. 15.
    Overmars, M.H., Welzl, E.: New methods for computing visibility graphs. Proceedings of the fourth annual symposium on Computational geometry. pp. 164–171. ACM, New York, NY, USA (1988).Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Helmut Schrom-Feiertag
    • 1
  • Thomas Matyus
    • 1
  • Martin Brunnhuber
    • 2
  1. 1.AIT Austrian Institute of TechnologyViennaAustria
  2. 2.VRVis Zentrum für Virtual reality und Visualisierung Forschungs-GmbHViennaAustria

Personalised recommendations