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Modelling the evolution of human trail systems

Author

Listed:
  • Dirk Helbing

    (Institute of Theoretical Physics, University of Stuttgart)

  • Joachim Keltsch

    (Science–Computing)

  • Péter Molnár

    (Center for Theoretical Studies of Physical Systems, Clark Atlanta University)

Abstract

Many human social phenomena, such as cooperation1,2,3, the growth of settlements4, traffic dynamics5,6,7 and pedestrian movement7,8,9,10, appear to be accessible to mathematical descriptions that invoke self-organization11,12. Here we develop a model of pedestrian motion to explore the evolution of trails in urban green spaces such as parks. Our aim is to address such questions as what the topological structures of these trail systems are13, and whether optimal path systems can be predicted for urban planning. We use an ‘active walker’ model14,15,16,17,18,19 that takes into account pedestrian motion and orientation and the concomitant feedbacks with the surrounding environment. Such models have previously been applied to the study of complex structure formation in physical14,15,16, chemical17 and biological18,19 systems. We find that our model is able to reproduce many of the observed large-scale spatial features of trail systems.

Suggested Citation

  • Dirk Helbing & Joachim Keltsch & Péter Molnár, 1997. "Modelling the evolution of human trail systems," Nature, Nature, vol. 388(6637), pages 47-50, July.
    • Handle: RePEc:nat:nature:v:388:y:1997:i:6637:d:10.1038_40353
    DOI: 10.1038/40353
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    Citations

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    Cited by:

    1. Jiang, Rui & Wu, Qing-Song, 2006. "Interaction between vehicle and pedestrians in a narrow channel," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 368(1), pages 239-246.
    2. Huang, Jiechen & Wang, Juan & Xia, Chengyi, 2020. "Role of vaccine efficacy in the vaccination behavior under myopic update rule on complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    3. Dirk Helbing & Lubos Buzna & Anders Johansson & Torsten Werner, 2005. "Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions," Transportation Science, INFORMS, vol. 39(1), pages 1-24, February.
    4. Czirók, András & Vicsek, Tamás, 2000. "Collective behavior of interacting self-propelled particles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 281(1), pages 17-29.
    5. Gašper Jaklič & Tadej Kanduč & Selena Praprotnik & Emil Žagar, 2012. "Energy Minimizing Mountain Ascent," Journal of Optimization Theory and Applications, Springer, vol. 155(2), pages 680-693, November.
    6. Sam K. Hui & Peter S. Fader & Eric T. Bradlow, 2009. "Path Data in Marketing: An Integrative Framework and Prospectus for Model Building," Marketing Science, INFORMS, vol. 28(2), pages 320-335, 03-04.
    7. Mehdi Moussaïd & Elsa G Guillot & Mathieu Moreau & Jérôme Fehrenbach & Olivier Chabiron & Samuel Lemercier & Julien Pettré & Cécile Appert-Rolland & Pierre Degond & Guy Theraulaz, 2012. "Traffic Instabilities in Self-Organized Pedestrian Crowds," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-10, March.
    8. Yuan Tang & Yu Xue & Muyang Huang & Qiyun Wen & Bingling Cen & Dong Chen, 2023. "A Lattice Hydrodynamic Model for Four-Way Pedestrian Traffic with Turning Capacity," Sustainability, MDPI, vol. 15(3), pages 1-17, January.
    9. Le-le Cao & Xiao-xue Li & Fen-ni Kang & Chang Liu & Fu-chun Sun & Ramamohanarao Kotagiri, 2015. "The Quantitative and Qualitative Evaluation of a Multi-Agent Microsimulation Model for Subway Carriage Design," International Journal of Microsimulation, International Microsimulation Association, vol. 8(3), pages 6-40.
    10. Haghani, Milad, 2021. "The knowledge domain of crowd dynamics: Anatomy of the field, pioneering studies, temporal trends, influential entities and outside-domain impact," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    11. Seitz, Michael J. & Dietrich, Felix & Köster, Gerta, 2015. "The effect of stepping on pedestrian trajectories," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 594-604.
    12. Goldsztein, Guillermo H., 2017. "Crowd of individuals walking in opposite directions. A toy model to study the segregation of the group into lanes of individuals moving in the same direction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 162-173.
    13. Jiang, Rui & Wu, Qing-Song, 2006. "The moving behavior of a large object in the crowds in a narrow channel," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 457-463.
    14. Niizato, Takayuki & Gunji, Yukio-Pegio, 2011. "Metric–topological interaction model of collective behavior," Ecological Modelling, Elsevier, vol. 222(17), pages 3041-3049.
    15. Manuel Stein & Halldór Janetzko & Daniel Seebacher & Alexander Jäger & Manuel Nagel & Jürgen Hölsch & Sven Kosub & Tobias Schreck & Daniel A. Keim & Michael Grossniklaus, 2017. "How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects," Data, MDPI, vol. 2(1), pages 1-23, January.
    16. Ohnishi, Teruaki & Okada, Osami & Shirakata, Hirofumi, 2013. "Morphological similarity of road networks and cracks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4127-4133.

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