Trail systems self-organize through individual route optimization
Abstract
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.
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BibTeX
@misc{wallace2026trail,
title = {{Trail systems self-organize through individual route optimization}},
author = {Brendan Wallace and Andrew Berdahl},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.04346},
eprint = {2611.04346},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.04346},
}
RIS
TY - UNPB
TI - Trail systems self-organize through individual route optimization
AU - Brendan Wallace
AU - Andrew Berdahl
PY - 2026
DA - 2026/11/03
PB - arXiv
DO - 10.48550/arxiv.2611.04346
UR - https://arxiv.org/abs/2611.04346
AB - 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.
ER -