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Intelligent Traffic Congestion Control System in Smart City

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Cyber Security Impact on Digitalization and Business Intelligence

Part of the book series: Studies in Big Data ((SBD,volume 117))

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Abstract

Congestion in the domain of transportation typically refers to an overload of vehicles on a certain stretch of road at a specific moment, resulting in speeds that are slower, sometimes significantly slower, than normal or “free flow” norms. Congestion-free traffic has been the main goal for a decade, and many methodologies have been embraced to make blockage-free streets. Sadly, street traffic is constrained by antiquated traffic lights (tri-variety signals) no matter the persistent exertion committed to creating and further developing the traffic stream. This study adopts a clever model to control congestion utilizing AI (ML) strategies. These ML procedures show cutting-edge execution to monitor and prevent congestion. The proposed model implements fit in making secure correspondence organizations and safeguarding information spillage to keep away from traffic jams.

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Author information

Authors and Affiliations

  1. Information Systems Department, College of Computing & Informatics, University of Sharjah, Sharjah, United Arab Emirates

    Iman Akour

  2. Department of Business Administration, College of Business, Al Ain University, P.O. Box 112612, Abu Dhabi CampusAbu Dhabi, United Arab Emirates

    Mohammed T. Nuseir

  3. Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa, 13133, Jordan

    Barween Al Kurdi

  4. School of Business, Skyline University College, Sharjah, United Arab Emirates

    Haitham M. Alzoubi

  5. Applied Science Research Center, Applied Science Private University, Amman, 11937, Jordan

    Haitham M. Alzoubi

  6. Department of Marketing, School of Business, The University of Jordan, Amman, 11942, Jordan

    Muhammad Turki Alshurideh

  7. Department of Management, College of Business Administration, University of Sharjah, Sharjah, 27272, United Arab Emirates

    Muhammad Turki Alshurideh & Ahmad Qasim Mohammad AlHamad

Authors
  1. Iman Akour
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  2. Mohammed T. Nuseir
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  3. Barween Al Kurdi
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  4. Haitham M. Alzoubi
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  5. Muhammad Turki Alshurideh
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  6. Ahmad Qasim Mohammad AlHamad
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Corresponding author

Correspondence to Muhammad Turki Alshurideh .

Editor information

Editors and Affiliations

  1. School of Business, Skyline University College, Sharjah, United Arab Emirates

    Haitham M. Alzoubi

  2. Department of Marketing, School of Business, The University of Jordan, Amman, Jordan

    Muhammad Turki Alshurideh

  3. School of Information Technology, Skyline University College, Sharjah, United Arab Emirates

    Taher M. Ghazal

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Akour, I., Nuseir, M.T., Al Kurdi, B., Alzoubi, H.M., Alshurideh, M.T., AlHamad, A.Q.M. (2024). Intelligent Traffic Congestion Control System in Smart City. In: Alzoubi , H.M., Alshurideh, M.T., Ghazal, T.M. (eds) Cyber Security Impact on Digitalization and Business Intelligence. Studies in Big Data, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-031-31801-6_13

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