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A Study of Wireless Sensor Networks Based Adaptive Traffic Lights Control

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Artificial Intelligence and Its Applications (AIAP 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 413))

Abstract

With the rising impact of congestion in cities, implementing an adaptive traffic light system as part of an Intelligent transportation system (ITS) is more than necessary to control traffic lights at intersections. These adaptive systems have the ability to sense traffic in real time and adjust the lights accordingly, contrary to the existing preprogrammed control systems which they fail to adapt to traffic variation causing more traffic jams. The existing industrial adaptive control systems use expensive equipment impeding their spread worldwide, but thanks to the advances in embedded systems, Wireless Sensor Networks (WSN) are emerging as a potential solution for the expensiveness of the existing adaptive schemes. In this paper, relevant works of designing an adaptive traffic lights control system using WSN are reviewed with a focus on the different sensing technologies, architectures and the algorithms used.

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Author information

Authors and Affiliations

  1. University of Constantine 1, Constantine, Algeria

    Sofiane Benzid & Ahmed Belhani

Authors
  1. Sofiane Benzid
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  2. Ahmed Belhani
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Corresponding author

Correspondence to Sofiane Benzid .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Echahid Hamma Lakhdar, El-Oued, Algeria

    Brahim Lejdel

  2. Department of Industrial and Information Engineering and Economics, University of L'Aquila, L'Aquila, Italy

    Eliseo Clementini

  3. Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia

    Louai Alarabi

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Benzid, S., Belhani, A. (2022). A Study of Wireless Sensor Networks Based Adaptive Traffic Lights Control. In: Lejdel, B., Clementini, E., Alarabi, L. (eds) Artificial Intelligence and Its Applications. AIAP 2021. Lecture Notes in Networks and Systems, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-030-96311-8_37

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