Argentine ants regulate traffic flow with stopped individuals

TL;DR

This study reveals how Argentine ants self-organize traffic flow by stopping individuals acting as dynamic obstacles, with implications for understanding biological traffic regulation and potential human traffic management.

Contribution

The paper introduces an agent-based model incorporating stopped ants as obstacles, explaining experimental traffic patterns and revealing a novel self-regulation mechanism.

Findings

Stopped ants act as dynamic obstacles affecting flow.

Wider bridges increase the fraction of stopped ants.

Model including stopped agents matches experimental data.

Abstract

We investigated the emerging traffic patterns of Argentine ants (Linepithema humile) as they navigated a narrow bridge between their nest and a food source. By tracking ant movements in experiments with varying bridge widths and colony sizes and analyzing the resulting trajectories, we discovered that a small subset of ants stopped for long periods of time, acting as obstacles and affecting traffic flow. Interestingly, the fraction of these stopped ants increased with wider bridges, suggesting a mechanism to reduce traffic flow to a narrower section of the bridge. To quantify transport efficiency, we measured the average speed of the ants on the bridge as a function of the pressure of ants arriving at the bridge, finding this relationship to be an increasing but saturating function of the pressure. We developed an agent-based model for ant movement and interactions to better understand these dynamics. Including stopped agents in the model was crucial to explaining the experimental observations. We further validated our hypothesis by introducing artificial obstacles on the bridges and found that our simulations accurately mirrored the experimental data when these obstacles were included. These findings provide new insights into how Argentine ants self-organize to manage traffic, highlighting a unique form of dynamic obstruction that enhances traffic flow in high-density conditions. This study advances our understanding of self-regulation in biological traffic systems and suggests potential applications for managing human traffic in congested environments.