01436nas a2200205   4500008004100000245006300041210006200104300001600166490000700182520082000189100002201009700001801031700002601049700002301075700002301098700001901121700002701140700002301167856004001190       2014                        eng d00aMeasuring the Complexity of Self-organizing Traffic Lights0 aMeasuring the Complexity of Selforganizing Traffic Lights  a2384–24070 v163 aWe 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's law of requisite variety, we can say that the self-organizing method achieves an adaptability level comparable to that of a living system.1 aZubillaga, Darío1 aCruz, Geovany1 aAguilar, Luis, Daniel1 aZapotécatl, Jorge1 aFernández, Nelson1 aAguilar, José1 aRosenblueth, David, A.1 aGershenson, Carlos  uhttp://dx.doi.org/10.3390/e1605238401487nas a2200133   4500008004100000245011100041210006900152300001200221490000700233520101500240100002301255700002701278856004801305       2012                        eng d00aAdaptive self-organization vs. static optimization: A qualitative comparison in traffic light coordination0 aAdaptive selforganization vs static optimization A qualitative c  a386-4030 v413 aUsing 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 ``free-spaces" that flow in the opposite direction of traffic.1 aGershenson, Carlos1 aRosenblueth, David, A.  uhttp://dx.doi.org/10.1108/0368492121122947901934nas a2200133   4500008004100000245006800041210006600109300001000175490000700185520151700192100002301709700002701732856004101759       2012                        eng d00aSelf-organizing traffic lights at multiple-street intersections0 aSelforganizing traffic lights at multiplestreet intersections  a23-390 v173 aThe 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.1 aGershenson, Carlos1 aRosenblueth, David, A.  uhttp://dx.doi.org/10.1002/cplx.2039500693nas a2200133   4500008004100000245006600041210006400107300001200171490000700183520026900190100002700459700002300486856005000509       2011                        eng d00aA model of city traffic based on elementary cellular automata0 amodel of city traffic based on elementary cellular automata  a305-3220 v193 aThere 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 136.1 aRosenblueth, David, A.1 aGershenson, Carlos  uhttp://www.complex-systems.com/pdf/19-4-1.pdf01557nas a2200109   4500008004100000245007800041210006900119520117400188100002301362700002701385856003501412       2009                        eng d00aModeling self-organizing traffic lights with elementary cellular automata0 aModeling selforganizing traffic lights with elementary cellular 3 aThere 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.1 aGershenson, Carlos1 aRosenblueth, David, A.  uhttp://arxiv.org/abs/0907.1925