@article {Zubillaga2014Measuring-the-C,
	title = {Measuring the Complexity of Self-organizing Traffic Lights},
	journal = {Entropy},
	volume = {16},
	number = {5},
	year = {2014},
	pages = {2384{\textendash}2407},
	abstract = {We apply measures of complexity, emergence, and self-organization to an urban traffic model for comparing a traditional traffic-light coordination method with a self-organizing method in two scenarios: cyclic boundaries and non-orientable boundaries. We show that the measures are useful to identify and characterize different dynamical phases. It becomes clear that different operation regimes are required for different traffic demands. Thus, not only is traffic a non-stationary problem, requiring controllers to adapt constantly; controllers must also change drastically the complexity of their behavior depending on the demand. Based on our measures and extending Ashby{\textquoteright}s law of requisite variety, we can say that the self-organizing method achieves an adaptability level comparable to that of a living system.},
	doi = {10.3390/e16052384},
	url = {http://dx.doi.org/10.3390/e16052384},
	author = {Dar{\'\i}o Zubillaga and Geovany Cruz and Luis Daniel Aguilar and Jorge Zapot{\'e}catl and Nelson Fern{\'a}ndez and Jos{\'e} Aguilar and David A. Rosenblueth and Carlos Gershenson}
}
@article {GershensonRosenblueth:2010,
	title = {Adaptive self-organization vs. static optimization: A qualitative comparison in traffic light coordination},
	journal = {Kybernetes},
	volume = {41},
	number = {3},
	year = {2012},
	pages = {386-403},
	abstract = {Using a recently proposed model of city traffic based on elementary cellular automata, we compare qualitatively two methods for coordinating traffic lights: a \emph{green-wave} method that tries to optimize phases according to expected flows and a \emph{self-organizing} method that adapts to the current traffic conditions. The \emph{self-organizing} method delivers considerable improvements over the \emph{green-wave} method. Seven dynamical regimes and six phase transitions are identified and analyzed for the \emph{self-organizing} method. For low densities, the \emph{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 {\textquoteleft}{\textquoteleft}free-spaces" that flow in the opposite direction of traffic.},
	doi = {10.1108/03684921211229479},
	url = {http://dx.doi.org/10.1108/03684921211229479},
	author = {Carlos Gershenson and David A. Rosenblueth}
}
@article {GershensonRosenblueth:2011,
	title = {Self-organizing traffic lights at multiple-street intersections},
	journal = {Complexity},
	volume = {17},
	number = {4},
	year = {2012},
	pages = {23-39},
	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 {\textquoteleft}{\textquoteleft}green wave{\textquoteright}{\textquoteright} 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.},
	doi = {10.1002/cplx.20395},
	url = {http://dx.doi.org/10.1002/cplx.20395},
	author = {Carlos Gershenson and David A. Rosenblueth}
}
@article {RosenbluethGershenson:2010,
	title = {A model of city traffic based on elementary cellular automata},
	journal = {Complex Systems},
	volume = {19},
	number = {4},
	year = {2011},
	pages = {305-322},
	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.},
	url = {http://www.complex-systems.com/pdf/19-4-1.pdf},
	author = {David A. Rosenblueth and Carlos Gershenson}
}
@unpublished {GershensonRosenblueth2009,
	title = {Modeling self-organizing traffic lights with elementary cellular automata},
	year = {2009},
	note = {Submitted},
	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.},
	url = {http://arxiv.org/abs/0907.1925},
	author = {Carlos Gershenson and David A. Rosenblueth}
}
