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A Study for Self-adapting Urban Traffic Control

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Advances in Artificial Intelligence - IBERAMIA 2016 (IBERAMIA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10022))

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Abstract

Nowadays, managing traffic in cities is a complex problem involving considerable physical and economical resources. However, traffic can be simulated by multi-agent systems (MAS), since cars and traffic lights can be modeled as agents that interact to obtain an overall goal: to reduce the average waiting times for the traffic users. In this paper, we present a self-organizing solution to efficiently manage urban traffic. We compare our proposal with other classical and alternative self-organizing approaches, observing that ours provides better results. Then, we present the main contributions of the paper that analyze the effects of different traffic conditions over our cheap and easy-to-implement method for self-organizing urban traffic management. We consider several scenarios where we explore the effects of dynamic traffic density, a reduction in the percentage of sensors needed to support the traffic management system, and the possibility of using communication among cross-points.

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Notes

  1. 1.

    The source code is available at URLs: http://hb-sotl.sourceforge.net and http://ccl.northwestern.edu/netlogo/models/community/HB-SOTL_8_10.

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Acknowledgment

The authors are supported by the Galician Regional Government under project CN 2012/260 (Consolidation of Research Units: AtlantTIC).

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Authors and Affiliations

  1. Department of Telematics Engineering, University of Vigo, 36310, Vigo, Spain

    P. S. Rodríguez-Hernández, J. C. Burguillo, Enrique Costa-Montenegro & Ana Peleteiro

Authors
  1. P. S. Rodríguez-Hernández
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  2. J. C. Burguillo
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  3. Enrique Costa-Montenegro
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  4. Ana Peleteiro
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Corresponding author

Correspondence to J. C. Burguillo .

Editor information

Editors and Affiliations

  1. INAOE , Tonantzintla, Mexico

    Manuel Montes y Gómez

  2. Astrofisica Optica y Electronica, INAOE , Puebla, Mexico

    Hugo Jair Escalante

  3. Universidad Nacional de Costa Rica , Heredia, Costa Rica

    Alberto Segura

  4. Universidad Nacional de Costa Rica , Heredia, Costa Rica

    Juan de Dios Murillo

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Rodríguez-Hernández, P.S., Burguillo, J.C., Costa-Montenegro, E., Peleteiro, A. (2016). A Study for Self-adapting Urban Traffic Control. In: Montes y Gómez, M., Escalante, H., Segura, A., Murillo, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2016. IBERAMIA 2016. Lecture Notes in Computer Science(), vol 10022. Springer, Cham. https://doi.org/10.1007/978-3-319-47955-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-47955-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47954-5

  • Online ISBN: 978-3-319-47955-2

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