  Steering traffic in cities is a very complex task, since improving efficiency
involves the coordination of many actors. Traditional approaches attempt to
optimize traffic lights for a particular density and configuration of traffic.
The disadvantage of this lies in the fact that traffic densities and
configurations change constantly. Traffic seems to be an adaptation problem
rather than an optimization problem. We propose a simple and feasible
alternative, in which traffic lights self-organize to improve traffic flow. We
use a multi-agent simulation to study three self-organizing methods, which are
able to outperform traditional rigid and adaptive methods. Using simple rules
and no direct communication, traffic lights are able to self-organize and adapt
to changing traffic conditions, reducing waiting times, number of stopped cars,
and increasing average speeds.
