Entropy 2014, 16(5), 2384-2407; doi:10.3390/e16052384
Article

Measuring the Complexity of Self-Organizing Traffic Lights

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Received: 1 February 2014; in revised form: 15 April 2014 / Accepted: 17 April 2014 / Published: 25 April 2014
(This article belongs to the Special Issue Entropy Methods in Guided Self-Organization)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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’s law of requisite variety, we can say that the self-organizing method achieves an adaptability level comparable to that of a living system.
Keywords: self-organization; complexity; emergence; information; traffic; cellular automata; adaptation; autopoiesis
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MDPI and ACS Style

Zubillaga, D.; Cruz, G.; Aguilar, L.D.; Zapotécatl, J.; Fernández, N.; Aguilar, J.; Rosenblueth, D.A.; Gershenson, C. Measuring the Complexity of Self-Organizing Traffic Lights. Entropy 2014, 16, 2384-2407.

AMA Style

Zubillaga D, Cruz G, Aguilar LD, Zapotécatl J, Fernández N, Aguilar J, Rosenblueth DA, Gershenson C. Measuring the Complexity of Self-Organizing Traffic Lights. Entropy. 2014; 16(5):2384-2407.

Chicago/Turabian Style

Zubillaga, Darío; Cruz, Geovany; Aguilar, Luis D.; Zapotécatl, Jorge; Fernández, Nelson; Aguilar, José; Rosenblueth, David A.; Gershenson, Carlos. 2014. "Measuring the Complexity of Self-Organizing Traffic Lights." Entropy 16, no. 5: 2384-2407.


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