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    Adaptive fuzzy-logic traffic control approach based on volunteer IoT agent mechanism

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    Authors
    Hewei, Guan
    Sadiq, Ali Safaa
    Tahir, Mohammed Adam
    Vhatkar, Sangeeta
    Issue Date
    2021-11-18
    
    Metadata
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    Abstract
    Traffic congestion is an extremely common phenomenal issue, it oc-curs in many cities around the world, especially in those cities with high car own-ership. Traffic congestion not only causes air pollution and fuel wastage, but it also leads to an increased commuting time and reduces the work time availability. Due to these reasons, traffic congestion needs to be controlled and reduced. The traffic light is the most widely adopted method to control traffic, however, most traffic lights in use are designed based on the predefined interval, which cannot cope with traffic volume change very well. Therefore, Internet of Things (IoT) based traffic light or adaptive traffic light systems are developed in the recent years as a complement of the traditional traffic lights. The adaptive traffic light can be built based on monitoring current traffic situation or using Vehicle-to-Vehicle and Vehicle-to-Infrastructure communication. This paper proposes a new design of adaptive traffic light, this traffic light system is based on fuzzy logic, and it introduces for the first time the use of volunteer IoT agent mecha-nism, which introduces more accurate results. To note that the presented work in this paper is the extended version of the work presented in (Hewei, G., Sadiq, A.S. and Tahir, M.A. 2021)
    Citation
    Hewei, G., Sadiq, A.S. & Tahir, M.A. Adaptive Fuzzy-Logic Traffic Control Approach Based on Volunteer IoT Agent Mechanism. SN COMPUT. SCI. 3, 68 (2022). https://doi.org/10.1007/s42979-021-00956-3
    Publisher
    Springer
    Journal
    SN Computer Science
    URI
    http://hdl.handle.net/2436/624388
    DOI
    10.1007/s42979-021-00956-3
    Additional Links
    https://link.springer.com/article/10.1007/s42979-021-00956-3
    Type
    Journal article
    Language
    en
    Description
    This is an accepted manuscript of an article published by Springer in SN Computer Science on 18/11/2021. The accepted version of the publication may differ from the final published version. Reuse is under the terms of the Springer Nature Accepted manuscript terms of use: https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms
    ISSN
    2661-8907
    ae974a485f413a2113503eed53cd6c53
    10.1007/s42979-021-00956-3
    Scopus Count
    Collections
    Faculty of Science and Engineering

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