Improving flocking behaviors in street networks with vision

TL;DR

This paper enhances a flocking model on street networks by expanding walkers' vision, leading to more realistic group behaviors with improved gathering times and robustness, relevant for understanding collective events.

Contribution

It introduces an expanded vision model for flocking on street networks, improving group cohesion and stability compared to previous models.

Findings

Groups gather faster with expanded vision.

Robustness to group breakups is improved.

Walkers avoid divergent splitting at intersections.

Abstract

We improve a flocking model on street networks introduced in a previous paper. We expand the field of vision of walkers, making the model more realistic. Under such conditions, we obtain groups of walkers whose gathering times and robustness to break ups are better than previous results. We explain such improvements because the alignment rule with vision guaranties walkers do not split into divergent directions at intersections anymore, and because the attraction rule with vision gathers distant groups. This paves the way to a better understanding of events where walkers have collective decentralized goals, like protests.