Self-organizing traffic lights at multiple-street intersections

TL;DR

This paper extends a cellular automaton traffic model to complex intersections, demonstrating that self-organizing traffic lights outperform traditional synchronized systems in scalability and efficiency, approaching theoretical optimal performance.

Contribution

It introduces a scalable self-organizing traffic light method for complex intersections and proposes a benchmark for performance comparison against theoretical optima.

Findings

Self-organizing traffic lights adapt better to complex scenarios.

Traditional synchronized traffic lights struggle with high-capacity scenarios.

The proposed benchmark measures controller performance against an optimal standard.

Abstract

Summary: Traffic light coordination is a complex problem. In this paper, we extend previous work on an abstract model of city traffic to allow for multiple street intersections. We test a self-organizing method in our model, showing that it is close to theoretical optima and superior to a traditional method of traffic light coordination. Abstract: The elementary cellular automaton following rule 184 can mimic particles flowing in one direction at a constant speed. This automaton can therefore model highway traffic. In a recent paper, we have incorporated intersections regulated by traffic lights to this model using exclusively elementary cellular automata. In such a paper, however, we only explored a rectangular grid. We now extend our model to more complex scenarios employing an hexagonal grid. This extension shows first that our model can readily incorporate multiple-way intersections and hence simulate complex scenarios. In addition, the current extension allows us to study and evaluate the behavior of two different kinds of traffic light controller for a grid of six-way streets allowing for either two or three street intersections: a traffic light that tries to adapt to the amount of traffic (which results in self-organizing traffic lights) and a system of synchronized traffic lights with coordinated rigid periods (sometimes called the "green wave" method). We observe a tradeoff between system capacity and topological complexity. The green wave method is unable to cope with the complexity of a higher-capacity scenario, while the self-organizing method is scalable, adapting to the complexity of a scenario and exploiting its maximum capacity. Additionally, in this paper we propose a benchmark, independent of methods and models, to measure the performance of a traffic light controller comparing it against a theoretical optimum.