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How Do People Search: A Modelling Perspective

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Parallel Processing and Applied Mathematics

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9574))

Abstract

The simulation of pedestrian movement is an important tool to ensure safety whenever many people have to be evacuated or pass through an environment. Although there are many simulation models for pedestrian dynamics, crucial aspects of human behaviour are still being neglected. One of those behaviours is the search strategy humans use to find someone or something within a building. We present three possible search strategies for pedestrian simulation. Two are often used as default implementations: random search and the optimal solution. The third more plausibly agrees with findings from psychology, neuroscience and related fields: a nearest room heuristic. We compare and evaluate the strategies, present simulation results for two concrete scenarios, and give a recommendation for computer models of human search behaviour.

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References

  1. Arikan, O., Chenney, S., Forsyth, D.: Efficient multi-agent path planning. In: Magnenat-Thalmann, N., Thalmann, D. (eds.) Computer Animation and Simulation 2001. Eurographics, pp. 151–162. Springer, Vienna (2001)

    Chapter  Google Scholar 

  2. Barnett-Cowan, M., Bülthoff, H.H.: Human path navigation in a three-dimensional world. Behav. Brain Sci. 36, 544–545 (2013)

    Article  Google Scholar 

  3. Blaser, R., Wilber, J.: A comparison of human performance in figural and navigational versions of the traveling salesman problem. Psychol. Res. 77(6), 761–772 (2013)

    Article  Google Scholar 

  4. Burstedde, C., Klauck, K., Schadschneider, A., Zittartz, J.: Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Phys. A: Stat. Mech. Appl. 295, 507–525 (2001)

    Article  MATH  Google Scholar 

  5. Chu, M., Law, K.: Computational framework incorporating human behaviors for egress simulations. J. Comput. Civ. Eng. 27(6), 699–707 (2013)

    Article  Google Scholar 

  6. Collett, T.S., Graham, P.: Animal navigation: path integration, visual landmarks and cognitive maps. Curr. Biol. 14(12), R475–R477 (2004)

    Article  Google Scholar 

  7. Daamen, W., Duives, D.C., Hoogendoorn, S.P. (eds.): The Conference in Pedestrian and Evacuation Dynamics (PED 2014), Transportation Research Procedia, vol. 2, pp. 1–818. Elsevier, Delft, The Netherlands (2014). www.sciencedirect.com/science/journal/23521465/2/

  8. Dietrich, F., Köster, G.: Gradient navigation model for pedestrian dynamics. Phys. Rev. E 89(6), 062801 (2014)

    Article  Google Scholar 

  9. Duives, D.C., Daamen, W., Hoogendoorn, S.P.: State-of-the-art crowd motion simulation models. Transp. Res. Part C: Emerg. Technol. 37, 193–209 (2013)

    Article  Google Scholar 

  10. Etienne, A.S., Maurer, R., Berlie, J., Reverdin, B., Rowe, T., Georgakopoulos, J., Seguinot, V.: Navigation through vector addition. Nat. 396(6707), 161–164 (1998)

    Article  Google Scholar 

  11. Franz, G., Mallot, H., Wiener, J.: Graph-based models of space in architecture and cognitive science - a comparative analysis. In: Proceedings of the 17th International Conference on Systems Research, Informatics and Cybernetics, vol. 3038 (2005)

    Google Scholar 

  12. Gigerenzer, G.: Why heuristics work. Perspect. Psychol. Sci. 3(1), 20–29 (2008)

    Article  Google Scholar 

  13. Gigerenzer, G., Todd, P.M., A.B.C. Research Group: Simple Heuristics That Make Us Smart. Oxford University Press, Oxford (1999)

    Google Scholar 

  14. Hafting, T., Fyhn, M., Molden, S., Moser, M.B., Moser, E.I.: Microstructure of a spatial map in the entorhinal cortex. Nat. 436(7052), 801–806 (2005)

    Article  Google Scholar 

  15. Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282–4286 (1995)

    Article  Google Scholar 

  16. Hutchinson, J.M.C., Gigerenzer, G.: Simple heuristics and rules of thumb: where psychologists and behavioural biologists might meet. Behav. Process. 69(2), 97–124 (2005). The Proceedings of the Meeting of the Society for the Quantitative Analyses of Behavior (SQAB 2004)

    Article  Google Scholar 

  17. Karapetyan, D., Gutin, G.: Efficient local search algorithms for known and new neighborhoods for the generalized traveling salesman problem. Eur. J. Oper. Res. 219(2), 234–251 (2012). http://www.sciencedirect.com/science/article/pii/S0377221712000288

    Article  MathSciNet  MATH  Google Scholar 

  18. Kneidl, A., Borrmann, A., Hartmann, D.: Generation and use of sparse navigation graphs for microscopic pedestrian simulation models. Adv. Eng. Inform. 26(4), 669–680 (2012)

    Article  Google Scholar 

  19. Laporte, G., Nobert, Y.: Generalized travelling salesman problem through n sets of nodes: an integer programming approach. INFOR J. 21(1), 61–75 (1983)

    MATH  Google Scholar 

  20. Leach, J.: Why people freeze in an emergency: temporal and cognitive constraints on survival responses. Aviat. Space Environ. Med. 75(6), 539–542 (2004). http://www.ingentaconnect.com/content/asma/asem/2004/00000075/00000006/art00011

    Google Scholar 

  21. Lenstra, J.K., Kan, A.: On general routing problems. Netw. 6(3), 273–280 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  22. Lozano-Pérez, T., Wesley, M.A.: An algorithm for planning collision-free paths among polyhedral obstacles. Commun. ACM 22(10), 560–570 (1979)

    Article  Google Scholar 

  23. Moser, E.I., Kropff, E., Moser, M.B.: Place cells, grid cells, and the brain’s spatial representation system. Annu. Rev. Neurosci. 31(1), 69–89 (2008)

    Article  Google Scholar 

  24. Näher, S.: The travelling salesman problem. In: Vöcking, B., Alt, H., Dietzfelbinger, M., Reischuk, R., Scheideler, C., Vollmer, H., Wagner, D. (eds.) Algorithms Unplugged, pp. 383–391. Springer, Berlin Heidelberg (2011)

    Chapter  Google Scholar 

  25. Pop, P.C., Matei, O., Sabo, C.: A new approach for solving the generalized traveling salesman problem. In: Blesa, M.J., Blum, C., Raidl, G., Roli, A., Sampels, M. (eds.) HM 2010. LNCS, vol. 6373, pp. 62–72. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  26. Proulx, G.: Evacuation from a single family house. In: Proceedings of the 4th International Symposium on Human Behaviour in Fire. Robinson College, Cambridge, UK, pp. 255–266 (2009)

    Google Scholar 

  27. Seitz, M.J., Köster, G.: Natural discretization of pedestrian movement in continuous space. Phys. Rev. E 86(4), 046108 (2012)

    Article  Google Scholar 

  28. Seitz, M.J., Köster, G.: How update schemes influence crowd simulations. J. Stat. Mech.: Theor. Exp. 7, P07002 (2014)

    Article  MathSciNet  Google Scholar 

  29. Sime, J.D.: Affiliative behaviour during escape to building exits. J. Environ. Psychol. 3(1), 21–41 (1983)

    Article  Google Scholar 

  30. von Sivers, I., Köster, G.: Dynamic stride length adaptation according to utility and personal space. Transp. Res. Part B: Methodol. 74, 104–117 (2015)

    Article  Google Scholar 

  31. Srivastava, S., Kumar, S., Garg, R., Sen, P.: Generalized traveling salesman problem through n sets of nodes. CORS J. 7, 97–101 (1969)

    MathSciNet  MATH  Google Scholar 

  32. Tversky, A., Kahneman, D.: Judgment under uncertainty: Heuristics and biases. Sci. 185, 1124–1131 (1974)

    Article  Google Scholar 

  33. Wiener, J., Ehbauer, N., Mallot, H.: Planning paths to multiple targets: Memory involvement and planning heuristics in spatial problem solving. Psychol. Res. 73(5), 644–658 (2009)

    Article  Google Scholar 

  34. Zheng, X., Zhong, T., Liu, M.: Modeling crowd evacuation of a building based on seven methodological approaches. Build. Environ. 44(3), 437–445 (2009)

    Article  MathSciNet  Google Scholar 

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Acknowledgement

This work was funded by the German Federal Ministry of Education and Research through the projects MEPKA on mathematical characteristics of pedestrian stream models (grant number 17PNT028) and MultikOSi on assistance systems for urban events – multi criteria integration for openness and safety (grant number 13N12824). The authors also acknowledge the support by the Faculty Graduate Center CeDoSIA of TUM Graduate School at Technische Universität München, Germany.

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Authors and Affiliations

  1. Munich University of Applied Sciences, Lothstr. 64, 80335, München, Germany

    Isabella von Sivers, Michael J. Seitz & Gerta Köster

Authors
  1. Isabella von Sivers
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  2. Michael J. Seitz
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  3. Gerta Köster
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Corresponding author

Correspondence to Isabella von Sivers .

Editor information

Editors and Affiliations

  1. Czestochowa University of Technolog, Czestochowa, Poland

    Roman Wyrzykowski

  2. Department of Computer Science, University of Southern California, Marina Del Rey, California, USA

    Ewa Deelman

  3. Electrical Engineering & Comput. Science, University of Tennessee, Knoxville, Tennessee, USA

    Jack Dongarra

  4. Czestochowa University of Technology, Institute of Computer & Information Sci., Czestochowa, Poland

    Konrad Karczewski

  5. Department of Computer Science, AGH University of Science and Technology, Krakow, Poland

    Jacek Kitowski

  6. Systèmes d’informations, Big Data et Rec, AGH University of Science and Technology, Krakow, Poland

    Kazimierz Wiatr

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von Sivers, I., Seitz, M.J., Köster, G. (2016). How Do People Search: A Modelling Perspective. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K., Kitowski, J., Wiatr, K. (eds) Parallel Processing and Applied Mathematics. Lecture Notes in Computer Science(), vol 9574. Springer, Cham. https://doi.org/10.1007/978-3-319-32152-3_45

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  • DOI: https://doi.org/10.1007/978-3-319-32152-3_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32151-6

  • Online ISBN: 978-3-319-32152-3

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