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

Measuring the Complexity of Self-Organizing Traffic Lights

1
Departamento de Ciencias de la Computación, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, 04510 México DF, Mexico
2
Posgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México, 04510 México DF, Mexico
3
Tecnológico de Estudios Superiores de Jocotitlán, 50700 Jocotitln, Mexico
4
Laboratorio de Hidroinformática, Facultad de Ciencias Básicas, Univesidad de Pamplona, Pamplona 31009, Colombia
5
Centro de Micro-electrónica y Sistemas Distribuidos, Universidad de los Andes, Mérida 5101, Venezuela
6
Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, 04510 México DF, Mexico
*
Author to whom correspondence should be addressed.
Received: 1 February 2014 / Revised: 15 April 2014 / Accepted: 17 April 2014 / Published: 25 April 2014
(This article belongs to the Special Issue Entropy Methods in Guided Self-Organization)
View Full-Text   |   Download PDF [549 KB, 24 February 2015; original version 24 February 2015]   |  

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. View Full-Text
Keywords: self-organization; complexity; emergence; information; traffic; cellular automata; adaptation; autopoiesis self-organization; complexity; emergence; information; traffic; cellular automata; adaptation; autopoiesis
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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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.

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