Modeling self-organizing traffic lights with elementary cellular automata

TL;DR

This paper extends elementary cellular automata to model city traffic, comparing a self-organizing traffic light method with a green-wave approach, showing the former's superior adaptability and efficiency across various traffic densities.

Contribution

It introduces a simple cellular automata model for city traffic with intersection coupling and evaluates a novel self-organizing traffic light control method.

Findings

Self-organizing method improves traffic flow and reduces stops.

Method prevents gridlocks at high densities.

Efficient platoon formation at low densities.

Abstract

There have been several highway traffic models proposed based on cellular automata. The simplest one is elementary cellular automaton rule 184. We extend this model to city traffic with cellular automata coupled at intersections using only rules 184, 252, and 136. The simplicity of the model offers a clear understanding of the main properties of city traffic and its phase transitions. We use the proposed model to compare two methods for coordinating traffic lights: a green-wave method that tries to optimize phases according to expected flows and a self-organizing method that adapts to the current traffic conditions. The self-organizing method delivers considerable improvements over the green-wave method. For low densities, the self-organizing method promotes the formation and coordination of platoons that flow freely in four directions, i.e. with a maximum velocity and no stops. For medium densities, the method allows a constant usage of the intersections, exploiting their maximum flux capacity. For high densities, the method prevents gridlocks and promotes the formation and coordination of "free-spaces" that flow in the opposite direction of traffic.