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Smart Pedestrian Crossing Management at Traffic Light Junctions through a Fuzzy-Based Approach

Author

Listed:
  • Giovanni Pau

    (Faculty of Engineering and Architecture, Kore University of Enna, Cittadella Universitaria, 94100 Enna, Italy)

  • Tiziana Campisi

    (Faculty of Engineering and Architecture, Kore University of Enna, Cittadella Universitaria, 94100 Enna, Italy)

  • Antonino Canale

    (Faculty of Engineering and Architecture, Kore University of Enna, Cittadella Universitaria, 94100 Enna, Italy)

  • Alessandro Severino

    (Faculty of Engineering and Architecture, Kore University of Enna, Cittadella Universitaria, 94100 Enna, Italy)

  • Mario Collotta

    (Faculty of Engineering and Architecture, Kore University of Enna, Cittadella Universitaria, 94100 Enna, Italy)

  • Giovanni Tesoriere

    (Faculty of Engineering and Architecture, Kore University of Enna, Cittadella Universitaria, 94100 Enna, Italy)

Abstract

In the last few years, numerous research efforts have been conducted to merge the Internet of Things (IoT) with smart city environments. The goal to make a city “smart” is arising as a possible solution to lessen the issues caused by the urban population growth and fast urbanization. Attention also has focused on the pedestrian crossings because they are one of the most dangerous places in the transport field. Information and Communications Technologies (ICT) can undoubtedly be an excellent support in developing infrastructures that can best manage pedestrian crossing. For this reason, this paper introduces a fuzzy logic-based solution able to manage dynamically the traffic lights’ phases in signalized pedestrian crossings. The proposed approach provides the possibility to change the phases of the traffic light taking into account the time of the day and the number of pedestrians about to cross the road. The paper presents a thorough description of the fuzzy logic controller configuration, an in-depth analysis of the application scenario and simulative assessments obtained through Vissim simulations.

Suggested Citation

  • Giovanni Pau & Tiziana Campisi & Antonino Canale & Alessandro Severino & Mario Collotta & Giovanni Tesoriere, 2018. "Smart Pedestrian Crossing Management at Traffic Light Junctions through a Fuzzy-Based Approach," Future Internet, MDPI, vol. 10(2), pages 1-19, February.
    • Handle: RePEc:gam:jftint:v:10:y:2018:i:2:p:15-:d:129731
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    References listed on IDEAS

    as
    1. Francesco Carrino & Elena Mugellini & Omar Abou Khaled & Nabil Ouerhani & Juergen Ehrensberger, 2016. "iNUIT: Internet of Things for Urban Innovation," Future Internet, MDPI, vol. 8(2), pages 1-21, May.
    2. Kayvan Aghabayk & Majid Sarvi & William Young, 2015. "A State-of-the-Art Review of Car-Following Models with Particular Considerations of Heavy Vehicles," Transport Reviews, Taylor & Francis Journals, vol. 35(1), pages 82-105, January.
    3. Pablo E. Branchi & Carlos Fernández-Valdivielso & Ignacio R. Matias, 2014. "Analysis Matrix for Smart Cities," Future Internet, MDPI, vol. 6(1), pages 1-15, January.
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    Cited by:

    1. Fabio Arena & Giovanni Pau, 2019. "An Overview of Vehicular Communications," Future Internet, MDPI, vol. 11(2), pages 1-12, January.
    2. Auwal Alhassan Musa & Salim Idris Malami & Fayez Alanazi & Wassef Ounaies & Mohammed Alshammari & Sadi Ibrahim Haruna, 2023. "Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations," Sustainability, MDPI, vol. 15(13), pages 1-15, June.

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