The Effect of Integrating Travel Time

Conference paper

Abstract

This contribution demonstrates the potential gain for the quality of results in a simulation of pedestrians when estimated remaining travel time is considered as a determining factor for the movement of simulated pedestrians. This is done twice: once for a force-based model and once for a cellular automata-based model. The results show that for the (degree of realism of) simulation results it is more relevant if estimated remaining travel time is considered or not than which modeling technique is chosen – here force-based vs. cellular automata – which normally is considered to be the most basic choice of modeling approach.

Keywords

Wayfinding Navigation Quickest path Earliest arrival Dynamic potential Dynamic assignment 

References

  1. 1.
    Rogsch, C., Klingsch, W.: Basics of Software-Tools for Pedestrian Movement – Identification and Results. Fire Technology (2010) 1–21Google Scholar
  2. 2.
    Various YouTube Contributors: Various Titles. published via YouTube (2007–2010) http://youtu.be/49HIZbFLPhg, http://youtu.be/jtKkHJXUVQY, http://youtu.be/LodYbDco0jY, and http://youtu.be/1WqnQjwAAac (accessed March 2012).
  3. 3.
    Schadschneider, A., Klingsch, W., Klüpfel, H., Kretz, T., Rogsch, C., Seyfried, A.: Evacuation Dynamics: Empirical Results, Modeling and Applications. [37] 3142 ISBN:978-0-387-75888-6.Google Scholar
  4. 4.
    Schadschneider, A., Klüpfel, H., Kretz, T., Rogsch, C., Seyfried, A.: Fundamentals of Pedestrian and Evacuation Dynamics. In Bazzan, A., Klügl, F., eds.: Multi-Agent Systems for Traffic and Transportation Engineering. Information Science Reference, Hershey, PA, USA (2009) 124–154 ISBN:978-1-60566-226-8.CrossRefGoogle Scholar
  5. 5.
    Kretz, T., Schreckenberg, M.: The F.A.S.T.-Model. In El Yacoubi, S., Chopard, B., Bandini, S., eds.: Cellular Automata – 7th International Conference on Cellular Automata for Research and Industry, ACRI 2006, Perpignan, France, Springer-Verlag Berlin Heidelberg (September 2006) 712–715 ISBN:3-540-40929-7.Google Scholar
  6. 6.
    Kretz, T.: Pedestrian Traffic – Simulation and Experiments. PhD thesis, Universität Duisburg-Essen (2007)Google Scholar
  7. 7.
    Kretz, T., Schreckenberg, M.: F.A.S.T. – Floor field- and Agent-based Simulation Tool. In Chung, E., Dumont, A., eds.: Transport simulation: Beyond traditional approaches. EPFL press, Lausanne, CH (April 2009) 125–135 ISBN:978-1420095098.CrossRefGoogle Scholar
  8. 8.
    Kretz, T., Schreckenberg, M.: Moore and more and symmetry. In Waldau, N., Gattermann, P., Knoflacher, H., Schreckenberg, M., eds.: Pedestrian and Evacuation Dynamics 2005, Springer Berlin Heidelberg (2007) 297–308 ISBN:978-3-540-47062-5.Google Scholar
  9. 9.
    Kretz, T.: CA and MAS – with the NaSch as Example. [38] 589–592 ISBN:978-3-642- 15978-7.Google Scholar
  10. 10.
    Kretz, T.: Computation Speed of the F.A.S.T. Model. In S. Dai et al., ed.: Traffic and Granular Flow ’09. (2010) (accepted for publication).Google Scholar
  11. 11.
    Kretz, T.: Pedestrian Traffic: on the Quickest Path. Journal of Statistical Mechanics: Theory and Experiment P03012 (2009)Google Scholar
  12. 12.
    Kretz, T., Bönisch, C., Vortisch, P.: Comparison of Various Methods for the Calculation of the Distance Potential Field. In Klingsch, W., Rogsch, C., Schadschneider, A., Schreckenberg, M., eds.: Pedestrian and Evacuation Dynamics 2008, Berlin Heidelberg, Springer-Verlag (2010) 335–346 ISBN: 978-3-642-04503-5.CrossRefGoogle Scholar
  13. 13.
    Johansson, A., Helbing, D., Shukla, P.: Specification of the Social Force Pedestrian Model by Evolutionary Adjustment to Video Tracking Data. Advances in Complex Systems 10(4) (2007) 271–288CrossRefMATHMathSciNetGoogle Scholar
  14. 14.
    Helbing, D., Johansson, A.: Pedestrian, Crowd and Evacuation Dynamics. [37] 6476 ISBN:978-0-387-75888-6.Google Scholar
  15. 15.
    PTV: VISSIM 5.40 User Manual, PTV Planung Transport Verkehr AG, Stumpfstraße 1, D-76131 Karlsruhe. (2010)Google Scholar
  16. 16.
    Kretz, T., Hengst, S., Vortisch, P.: Pedestrian Flow at Bottlenecks – Validation and Calibration of VISSIM’s Social Force Model of Pedestrian Traffic and its Empirical Foundations. In Sarvi, M., ed.: International Symposium of Transport Simulation 2008 (ISTS08), Gold Coast, Australia, Monash University (2008) electronic publicationGoogle Scholar
  17. 17.
    Kretz, T., Große, A., Hengst, S., Kautzsch, L., Pohlmann, A., Vortisch, P.: Quickest Paths in Simulations of Pedestrians. Advances in Complex Systems 14 (2011) 733CrossRefGoogle Scholar
  18. 18.
    Kimmel, R., Sethian, J.: Computing geodesic paths on manifolds. In: Proc. Natl. Acad. Sci. USA. (1998) 8431–8435Google Scholar
  19. 19.
    Jeong, W.K., Whitaker, R.: A fast eikonal equation solver for parallel systems. In: SIAM conference on Computational Science and Engineering. (February 2007)Google Scholar
  20. 20.
    Jeong, W.K., Whitaker, R.: A Fast Iterative Method for a Class of Hamilton-Jacobi Equations on Parallel Systems. Technical Report UUCS-07-010, University of Utah, School of Computing (April 2007)Google Scholar
  21. 21.
    Jeong, W.K., Whitaker, R.: A Fast Iterative Method for Eikonal Equations. SIAM Journal on Scientific Computing 30(5) (July 2008) 2512–2534CrossRefMATHMathSciNetGoogle Scholar
  22. 22.
    Kretz, T.: The use of dynamic distance potential fields for pedestrian flow around corners. In: First International Conference on Evacuation Modeling and Management, TU Delft (2009)Google Scholar
  23. 23.
    Kretz, T.: Applications of the Dynamic Distance Potential Field Method. In S. Dai et al., ed.: Traffic and Granular Flow ’09. (2010) (accepted for publication).Google Scholar
  24. 24.
    Kretz, T.: The Dynamic Distance Potential Field in a Situation with Asymmetric Bottleneck Capacities. In Bandini, S., Manzoni, S., Umeo, H., Vizzari, G., eds.: Cellular Automata – 9th International Conference on Cellular Automata for Research and Industry, ACRI 2010. Volume 6350 of Lecture Notes in Computer Science., Heidelberg, Springer (2010) 480–488Google Scholar
  25. 25.
    Kretz, T., Hengst, S., Roca, V., Pérez Arias, A., Friedberger, S., Hanebeck, U.: Calibrating Dynamic Pedestrian Route Choice with an Extended Range Telepresence System. In: 2011 IEEE International Conference on Computer Vision Workshops. (2011) 166–172 First IEEE Workshop on Modeling, Simulation and Visual Analysis of Large Crowds, 6–13 November 2011, Barcelona, Spain.Google Scholar
  26. 26.
    Kretz, T., Hengst, S., Pérez Arias, A., Friedberger, S., Hanebeck, U.: Using Extended-Range Telepresence to Collect Data on Pedestrian Dynamics. In: Annual Meeting of the Transportation Research Board 2012. (2012) (on CD).Google Scholar
  27. 27.
    PTV Vision: Quickest vs. Shortest Path in a Simulation of Pedestrians. published via YouTube (2011) http://youtu.be/8SmRBTJ-jeU (accessed March 2012).
  28. 28.
    Moussaïd, M., Helbing, D., Garnier, S., Johansson, A., Combe, M., Theraulaz, G.: Experimental study of the behavioural mechanisms underlying self-organization in human crowds. Proceedings of the Royal Society B: Biological Sciences 276(1668) (2009) 2755–2762CrossRefGoogle Scholar
  29. 29.
    Chraibi, M., Seyfried, A., Schadschneider, A.: Generalized centrifugal-force model for pedestrian dynamics. Physical Review E 82(4) (2010) 046111Google Scholar
  30. 30.
    Steffen, B., Seyfried, A.: The repulsive force in continous space models of pedestrian movement. Arxiv preprint arXiv:0803.1319 (2008)Google Scholar
  31. 31.
    Sherif, M.: The psychology of social norms. Harper & Brothers, New York (1936)Google Scholar
  32. 32.
    Rascle, M., Nkonga, B., Decoupigny, F., Maignant, G.: Geodesics and Shortest Paths in Pedestrian Motions: A Mathematical Approach. In V.V. Kozlov, A.S. Bugaev, A.S. et al., ed.: Traffic and Granular Flow ’11, Moscow, Springer-Verlag Berlin Heidelberg (2012) in print.Google Scholar
  33. 33.
    Brunner, U., Kirchberger, H., Lebeda, C., Oswald, M., Könnecke, R., Kraft, M., Thoss, A., Mülli, L., Seyfried, A., Hartnack, C., Wader, S., Spennes, G., Kretz, T.: RiMEA – Richtlinie für Mikroskopische Entfluchtungs-Analysen. 2.2.1 edn. Initiatoren des RiMEA-Projekts: M. Schwendimann, N. Waldau, P. Gattermann, C. Moroge, T. Meyer-König, and M. Schreckenberg (2009) (eprint from http://www.rimea.de/). (in German).
  34. 34.
    Weidmann, U.: Transporttechnik der Fußgänger – Transporttechnische Eigenschaften des Fußgängerverkehrs. Schriftenreihe des IVT 90, ETH Zürich (3 1993) Zweite, ergänzte Auflage. (in German).Google Scholar
  35. 35.
    Lakoba, T., Kaup, D., Finkelstein, N.: Modifications of the helbing-molnar-farkas-vicsek social force model for pedestrian evolution. Simulation 81(5) (2005) 339–352CrossRefGoogle Scholar
  36. 36.
    Yu, W., Chen, R., Dong, L., Dai, S.: Centrifugal force model for pedestrian dynamics. Physical Review E 72(2) (2005) 026112Google Scholar
  37. 37.
    Meyers, R., ed.: Encyclopedia of Complexity and Systems Science. SpringerScience+BuisinessMedia, New York (2009) ISBN:978-0-387-75888-6.MATHGoogle Scholar
  38. 38.
    Bandini, S., Manzoni, S., Umeo, H., Vizzari, G., eds.: Cellular Automata – 9th International Conference on Cellular Automata for Research and Industry, ACRI 2010. Volume 6350 of Lecture Notes in Computer Science., Heidelberg, Springer (2010) ISBN:978-3-642-15978-7.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.PTV GroupHaid-und-Neu-Straße 15KarlsruheGermany

Personalised recommendations