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Optimal traffic organization in ants under crowded conditions

Author

Listed:
  • Audrey Dussutour

    (Université Paul Sabatier
    Université Libre de Bruxelles)

  • Vincent Fourcassié

    (Université Paul Sabatier)

  • Dirk Helbing

    (Dresden University of Technology)

  • Jean-Louis Deneubourg

    (Université Libre de Bruxelles)

Abstract

Efficient transportation, a hot topic in nonlinear science1, is essential for modern societies and the survival of biological species. Biological evolution has generated a rich variety of successful solutions2, which have inspired engineers to design optimized artificial systems3,4. Foraging ants, for example, form attractive trails that support the exploitation of initially unknown food sources in almost the minimum possible time5,6. However, can this strategy cope with bottleneck situations, when interactions cause delays that reduce the overall flow? Here, we present an experimental study of ants confronted with two alternative routes. We find that pheromone-based attraction generates one trail at low densities, whereas at a high level of crowding, another trail is established before traffic volume is affected, which guarantees that an optimal rate of food return is maintained. This bifurcation phenomenon is explained by a nonlinear modelling approach. Surprisingly, the underlying mechanism is based on inhibitory interactions. It points to capacity reserves, a limitation of the density-induced speed reduction, and a sufficient pheromone concentration for reliable trail perception. The balancing mechanism between cohesive and dispersive forces appears to be generic in natural, urban and transportation systems.

Suggested Citation

  • Audrey Dussutour & Vincent Fourcassié & Dirk Helbing & Jean-Louis Deneubourg, 2004. "Optimal traffic organization in ants under crowded conditions," Nature, Nature, vol. 428(6978), pages 70-73, March.
    • Handle: RePEc:nat:nature:v:428:y:2004:i:6978:d:10.1038_nature02345
    DOI: 10.1038/nature02345
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    Cited by:

    1. Viana, Matheus P. & Fourcassié, Vincent & Perna, Andrea & Costa, Luciano da F. & Jost, Christian, 2013. "Accessibility in networks: A useful measure for understanding social insect nest architecture," Chaos, Solitons & Fractals, Elsevier, vol. 46(C), pages 38-45.
    2. Christoph Grüter & Roger Schürch & Tomer J Czaczkes & Keeley Taylor & Thomas Durance & Sam M Jones & Francis L W Ratnieks, 2012. "Negative Feedback Enables Fast and Flexible Collective Decision-Making in Ants," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-11, September.
    3. Jinxiao Duan & Guanwen Zeng & Nimrod Serok & Daqing Li & Efrat Blumenfeld Lieberthal & Hai-Jun Huang & Shlomo Havlin, 2023. "Spatiotemporal dynamics of traffic bottlenecks yields an early signal of heavy congestions," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    4. Shiwakoti, Nirajan & Sarvi, Majid, 2013. "Understanding pedestrian crowd panic: a review on model organisms approach," Journal of Transport Geography, Elsevier, vol. 26(C), pages 12-17.
    5. Natalia Zabzina & Audrey Dussutour & Richard P Mann & David J T Sumpter & Stamatios C Nicolis, 2014. "Symmetry Restoring Bifurcation in Collective Decision-Making," PLOS Computational Biology, Public Library of Science, vol. 10(12), pages 1-11, December.
    6. Sakiyama, Tomoko & Gunji, Yukio-Pegio, 2016. "Directional ambiguity in trail-laying algorithms," Ecological Modelling, Elsevier, vol. 340(C), pages 37-44.
    7. 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).
    8. 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.
    9. Wang, Shujie & Cao, Shuchao & Wang, Qiao & Lian, Liping & Song, Weiguo, 2016. "Effect of exit locations on ants escaping a two-exit room stressed with repellent," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 239-254.
    10. Arjun Chandrasekhar & James A R Marshall & Cortnea Austin & Saket Navlakha & Deborah M Gordon, 2021. "Better tired than lost: Turtle ant trail networks favor coherence over short edges," PLOS Computational Biology, Public Library of Science, vol. 17(10), pages 1-24, October.
    11. Mark W. Moffett & Simon Garnier & Kathleen M. Eisenhardt & Nathan R. Furr & Massimo Warglien & Costanza Sartoris & William Ocasio & Thorbjørn Knudsen & Lars A. Bach & Joachim Offenberg, 2021. "Ant colonies: building complex organizations with minuscule brains and no leaders," Journal of Organization Design, Springer;Organizational Design Community, vol. 10(1), pages 55-74, March.
    12. Armbruster, D. & de Beer, C. & Freitag, M. & Jagalski, T. & Ringhofer, C., 2006. "Autonomous control of production networks using a pheromone approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(1), pages 104-114.

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