2011/04/14 by Carlos Gershenson, Gershenson, Carlos, David A. Rosenblueth +1
Computer Science · Engineering · Physics and Astronomy · #Adaptation and Self-Organizing Systems (nlin.AO) #Artificial Intelligence (cs.AI) #Cellular Automata and Applications #Cellular Automata and Lattice Gases (nlin.CG) #F.1.1 #FOS: Computer and information sciences #FOS: Physical sciences #I.2.11 #I.6.3 #J.2 #Slime Mold and Myxomycetes Research #Traffic control and management #cs.AI #nlin.AO #nlin.CG
paper · pdf · doi:10.48550/arxiv.1104.2829
32 pages, 12 figures
arxiv created 2011/04/14 · openalex publication_date 2011/04/14 · arxiv updated 2011/04/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.