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Trail systems self-organize through individual route optimization — arXiv:2611.04346

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04346
License (as archived): CC0-1.0
Archived: 2026-11-04T19:11:48.693Z
SHA-256 of the archived text: 2e011bb96005cd0220bcf7471580db3cecb7c755b47d0bfefbf5a14b0abe1e04
Stable URL of this page: https://metraipolis.com/documento/dc_fef0ef283ac2

How to cite

Trail systems self-organize through individual route optimization — arXiv:2611.04346 — arXiv (en), https://arxiv.org/abs/2611.04346 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fef0ef283ac2 (Metraipolis archive dc_fef0ef283ac2, sha256 2e011bb96005cd0220bcf7471580db3cecb7c755b47d0bfefbf5a14b0abe1e04)

BibTeX

@misc{dcfef0ef283ac2,
  title = {Trail systems self-organize through individual route optimization — arXiv:2611.04346},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04346},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fef0ef283ac2; sha256 2e011bb96005cd0220bcf7471580db3cecb7c755b47d0bfefbf5a14b0abe1e04},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04346. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

As animals, including humans, walk, they compact the vegetation and ground beneath them, reducing the cost of movement for subsequent travelers. From this positive feedback emerge networks of trails which allow for efficient movement and store generational-scale information about the environment. Models simulating this process show how individuals following local cues produce trail systems with many of the features of real-world networks. However, little has been done to investigate walkers with more complete knowledge of their environment and the abilities to plan their behavior intelligently. Here we introduce a simple general model for trail formation, based on minimizing cost of movement by individuals. As in real environments, our simulations produce a striking variety of complex trail networks. We show that these trail systems optimally connect space according to the spatial needs of the walkers, that the geometry of those trail systems matches theoretical predictions and that the systems they generate may fall into multiple attracting states. As technological advances allow us to quantify trail systems across diverse habitats and species, our model provides a theoretical foundation for empirical research on animal trails and for understanding the emergence of human transportation networks, with implications for urban design.

Tema degli esploratori: scienze della vita e salute (elenco arXiv q-bio.QM, nuovi lavori del giorno).
Categorie arXiv: Adaptation and Self-Organizing Systems (nlin.AO) ; Quantitative Methods (q-bio.QM).
Pagina del lavoro: https://arxiv.org/abs/2611.04346 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T18:46:22.559Z dalla pagina https://arxiv.org/list/q-bio.QM/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).