Measuring the complexity of self-organizing traffic lights

Entropy

Volumn 16, Issue 5, 2014, Pages 2384-2407

  Zubillaga, Darío ^a,b   Cruz, Geovany ^a,c   Aguilar, Luis Daniel ^a,c   Zapotécatl, Jorge ^a,b   Fernández, Nelson ^d,e   Aguilar, José ^e   Rosenblueth, David A ^a,b   Gershenson, Carlos ^a,b  

^a IIMAS UNAM   (Mexico)

^b UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO   (Mexico)

^c Tecno ogico de Estudios Superiores de Jocotit an   (Mexico)

^d UNIVERSIDAD DE PAMPLONA   (Colombia)

^e UNIVERSIDAD DE LOS ANDES   (Venezuela)

Author keywords

Adaptation; Autopoiesis; Cellular automata; Complexity; Emergence; Information; Self organization; Traffic

Indexed keywords

EID: 84901428187     PISSN: None     EISSN: 10994300     Source Type: Journal    
DOI: 10.3390/e16052384     Document Type: Article

References (68)

1. Cohen, J.E. Human Population: The Next Half Century. Science 2003, 302, 1172-1175.
2. Butler, D. Cities: The century of the city. Nature 2010, 467, 900-901.
3. Roberts, L. 9 Billion? Science 2011, 333, 540-543.
4. Glaeser, E. Cities, Productivity, and Quality of Life. Science 2011, 333, 592-594.
5. Bettencourt, L.M.A.; Lobo, J.; Helbing, D.; Kühnert, C.; West, G.B. Growth, innovation, scaling, and the pace of life in cities. Proc. Natl. Acad. Sci. USA 2007, 104, 7301-7306.
6. Bettencourt, L.; West, G. A unified theory of urban living. Nature 2010, 467, 912-913.
7. Gyimesi, K.; Vincent, C.; Lamba, N. Frustration Rising: IBM 2011 Commuter Pain Survey; IBM: Armonk, NY, USA, 2011
8. Gershenson, C. Living in Living Cities. Artif. Life 2013, 19, 401-420.
9. Gershenson, C. The Implications of Interactions for Science and Philosophy. Found. Sci. 2013, 18, 781-790.
10. Gershenson, C. Self-organizing urban transportationsystems. In Complexity Theories of Cities Have Come of Age: An Overview with Implications to Urban Planning and Design; Portugali, J., Meyer, H., Stolk, E., Tan, E., Eds.; Springer: Berlin Heidelberg, Germany, 2012; pp. 269-279.
11. Federal Highway Administration. Traffic Control Systems Handbook; U.S. Department of Transportation: Washington, D.C., USA, 2005.
12. Henry, J.; Farges, J.; Tuffal, J. The PRODYN real time traffic algorithm. In Proceedings of the International Federation of Automatic Control (IFAC) Conference, San Francisco, CA, USA, June 1983.
13. Mauro, V.; di Taranto, D. UTOPIA. In Proceedings of the 6th IFAC/IFIP/IFORS Symposium on Control Computers and Communication in Transportation, Paris, France, 1990.
14. Robertson, D.; Bretherton, R. Optimizing networks of traffic signals in real time-the SCOOT method. Veh. Technol. IEEE Trans. 1991, 40, 11-15.
15. Faieta, B.; Huberman, B.A. Firefly: A Synchronization Strategy for Urban Traffic Control; Technical Report SSL-42; Xerox PARC: Palo Alto, CA, USA, 1993.
16. Gartner, N.H.; Pooran, F.J.; Andrews, C.M. Implementation of the OPAC Adaptive Control Strategy in a Trafffic Signaling Network. In Proceedings of the IEEE Intelligent Transportation Systems Conference, Oakland, CA, USA, 25-29 August 2001; pp. 195-200.
17. Diakaki, C.; Dinopoulou, V.; Aboudolas, K.; Papageorgiou, M.; Ben-Shabat, E.; Seider, E.; Leibov, A. Extensions and New Applications of the Traffic Signal Control Strategy TUC. Transp. Res. Rec. 2003, 1856, 202-211.
18. Fouladvand, M.E.; Sadjadi, Z.; Shaebani, M.R. Optimized Traffic Flow at a Single Intersection: Traffic Responsive Signalization. J. Phys. A: Math. Gen. 2004, 37, 561-576.
19. Mirchandani, P.; Wang, F.Y. RHODES to Intelligent Transportation Systems. IEEE Intell.Syst. 2005, 20, 10-15.
20. Bazzan, A.L.C. A Distributed Approach for Coordination of Traffic Signal Agents. Auton. Agents Multiagent Syst. 2005, 10, 131-164.
21. Helbing, D.; Lämmer, S.; Lebacque, J.P. Self-organized control of irregular or perturbed network traffic. In Optimal Control and Dynamic Games; Deissenberg, C., Hartl, R.F., Eds.; Springer: Dordrecht, The Netherlands, 2005; pp. 239-274.
22. Gershenson, C. Self-Organizing Traffic Lights. Complex Syst. 2005, 16, 29-53.
23. Prokopenko, M. Guided self-organization. HFSP J. 2009, 3, 287-289.
24. Ay, N.; Der, R.; Prokopenko, M. Guided self-organization: Perception-action loops of embodied systems. Theory Biosci. 2012, 131, 125-127.
25. Polani, D.; Prokopenko, M.; Yaeger, L.S. Information and Self-organization of Behavior. Adv. Complex Syst. 2013, 16, 1303001.
26. Prokopenko, M., Ed.; Guided Self-Organization: Inception; Emergence, Complexity and Computation; Springer: Berlin Heidelberg, Germany, 2014; Volume 9.
27. Fernández, N.; Maldonado, C.; Gershenson, C. Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis. In Guided Self-Organization: Inception; Emergence, Complexity and Computation; Prokopenko, M., Ed.; Springer: Berlin Heidelberg, Germany, 2014; Volume 9, pp. 19-51.
28. Wilensky, U. NetLogo, Center for connected learning and computer-based modeling, Northwestern University, 1999.
29. Gershenson, C. TrafficCA Simulation. http://tinyurl.com/trafficCA Last accessed: 2014-04-23
30. Rosenblueth, D.A.; Gershenson, C. A model of city traffic based on elementary cellular automata. Complex Syst. 2011, 19, 305-322.
31. Wuensche, A.; Lesser, M. The Global Dynamics of Cellular Automata; An Atlas of Basin of Attraction Fields of One-Dimensional Cellular Automata; Santa Fe Institute Studies in the Sciences of Complexity, Addison-Wesley: Reading, MA, USA, 1992.
32. Wolfram, S. A New Kind of Science; Wolfram Media: Champaign, IL, USA, 2002.
33. Gershenson, C.; Rosenblueth, D.A. Self-organizing traffic lights at multiple-street intersections. Complexity 2012, 17, 23-39.
34. Kanai, M. Calibration of the Particle Density in Cellular-Automaton Models for Traffic Flow. J. Phys. Soc. Jpn. 2010, 79, 075002.
35. Papadimitriou, C.H.; Tsitsiklis, J.N. The Complexity of Optimal Queuing Network Control. Math. Oper. Res. 1999, 24, 293-305.
36. Gershenson, C. Design and Control of Self-organizing Systems; CopIt Arxives: Mexico City, Mexico, 2007. Available online: http://tinyurl.com/DCSOS2007 (accessed on 2014-04-23).
37. Zapotecatl, J. QtTrafficLights. https://github.com/Zapotecatl/Traffic-Light Last accessed: 2014-04-23
38. Gershenson, C.; Rosenblueth, D.A. Adaptive self-organization vs. static optimization: A qualitative comparison in traffic light coordination. Kybernetes 2012, 41, 386-403.
39. Török, J.; Kertész, J. The green wave model of two-dimensional traffic: Transitions in the flow properties and in the geometry of the traffic jam. Phys. A 1996, 231, 515-533.
40. Cools, S.B.; Gershenson, C.; D'Hooghe, B. Self-organizing traffic lights: A realistic simulation. In Self-Organization: Applied Multi-Agent Systems; Prokopenko, M., Ed.; Springer: London, UK, 2007; Chapter 3, pp. 41-49.
41. Gershenson, C.; Fernández, N. Complexity and Information: Measuring Emergence, Self-organization, and Homeostasis at Multiple Scales. Complexity 2012, 18, 29-44.
42. Bedau, M.A.; Humphreys, P., Eds.; Emergence: Contemporary Readings in Philosophy and Science; MIT Press: Cambridge, MA, USA, 2008.
43. Shannon, C.E. A mathematical theory of communication. Bell Syst. Tech. J. 1948, 27, 379-423 and 623-656.
44. Bedau, M.A. Weak Emergence. In Philosophical Perspectives: Mind, Causation, and World; Tomberlin, J., Ed.; Blackwell: Malden, MA, USA, 1997; Volume 11, pp. 375-399.
45. Mitchell, M. Complexity: A Guided Tour; Oxford University Press: Oxford, UK, 2009.
46. Gershenson, C.; Heylighen, F. When Can We Call a System Self-Organizing? Advances in Artificial Life, 7th European Conference, ECAL 2003 LNAI 2801; Banzhaf, W., Christaller, T., Dittrich, P., Kim, J.T., Ziegler, J., Eds.; Springer: Berlin, Germany, 2003; pp. 606-614.
47. Kauffman, S.A. The Origins of Order; Oxford University Press: Oxford, UK, 1993.
48. Lopez-Ruiz, R.; Mancini, H.L.; Calbet, X. A statistical measure of complexity. Phys. Lett. A 1995, 209, 321-326.
49. Wiener, N. Cybernetics; or, Control and Communication in the Animal and the Machine; Wiley and Sons: New York, NY, USA, 1948.
50. von Bertalanffy, L. General System Theory: Foundations, Development, Applications; George Braziller: New York, NY, USA, 1968.
51. Langton, C. Computation at the Edge of Chaos: Phase Transitions and Emergent Computation. Phys. D 1990, 42, 12-37.
52. Kauffman, S.A. Metabolic Stability and Epigenesis in Randomly Constructed Genetic Nets. J. Theor. Biol. 1969, 22, 437-467.
53. Gershenson, C. Introduction to Random Boolean Networks. Workshop and Tutorial Proceedings, Ninth International Conference on the Simulation and Synthesis of Living Systems (ALife IX); Bedau, M., Husbands, P., Hutton, T., Kumar, S., Suzuki, H., Eds.; 2004; pp. 160-173.
54. Glauber, R.J. TimeDependent Statistics of the Ising Model. J. Math. Phys. 1963, 4, 294-307.
55. Balleza, E.; Alvarez-Buylla, E.R.; Chaos, A.; Kauffman, S.; Shmulevich, I.; Aldana, M. Critical Dynamics in Genetic Regulatory Networks: Examples from Four Kingdoms. PLoS One 2008, 3, e2456.
56. Bedau, M.A.; McCaskill, J.S.; Packard, N.H.; Rasmussen, S. Living Technology: Exploiting Life's Principles in Technology. Artif. Life 2009, 16, 89-97.
57. Bedau, M.A.; McCaskill, J.S.; Packard, N.H.; Parke, E.C.; Rasmussen, S.R. Introduction to Recent Developments in Living Technology. Artif. Life 2013, 19, 291-298.
58. Prokopenko, M.; Lizier, J.T.; Obst, O.; Wang, X.R. Relating Fisher information to order parameters. Phys. Rev. E 2011, 84, 041116.
59. Ashby, W.R. An Introduction to Cybernetics; Chapman & Hall: London, UK, 1956.
60. Gershenson, C. The World as Evolving Information. In Unifying Themes in Complex Systems; Minai, A.; Braha, D.; Bar-Yam, Y., Eds.; Springer: Berlin Heidelberg, Germany, 2012; Volume VII, pp. 100-115.
61. Tononi, G.; Sporns, O.; Edelman, G.M. A complexity measure for selective matching of signals by the brain. Proc. Natl. Acad. Sci. USA 1996, 93, 3422-3427.
62. Lárraga, M.E.; Á lvarez-Icaza, L. Cellular automaton model for traffic flow based on safe driving policies and human reactions. Phys. A: Stat. Mech. Appl. 2010, 389, 5425-5438.
63. Amoretti, M.; Gershenson, C. Measuring the Complexity of Ultra-Large-Scale Evolutionary Systems. Submitted.
64. Fernández, N.; Gershenson, C. Measuring Complexity in an Aquatic Ecosystem. In Advances in Computational Biology; Advances in Intelligent Systems and Computing; Castillo, L.F., Cristancho, M., Isaza, G., Pinzón, A., Corchado Rodríguez, J.M., Eds.; Springer: Cham Heidelberg New York Dordrecht London, 2014; Volume 232, pp. 83-89.
65. Febres, G.; Jaffe, K.; Gershenson, C. Complexity measurement of natural and artificial languages. Complexity 2014, In Press.
66. Krajzewicz, D.; Erdmann, J.; Behrisch, M.; Bieker, L. Recent Development and Applications of SUMO - Simulation of Urban MObility. Int. J. Adv. Syst. Meas. 2012, 5, 128-138.
67. Krauss, S.; Wagner, P.; Gawron, C. Metastable states in a microscopic model of traffic flow. Phys. Rev. E 1997, 55, 5597-5602.
68. Krauss, S. Microscopic Modeling of Traffic Flow: Investigation of Collision Free Vehicle Dynamics. PhD Thesis, Universität zu Köln, Cologne, Germany, 1998.