1 National Centre of Artificial Intelligence and Robotics, Abuja, Nigeria.
2 Department of Electrical Engineering, George Washington University ,DIstrict of Columbia, Washington,USA
3 Department of Electrical Engineering, Yaba College of Technology, Lagos, Nigeria.
4 Department of Computer Engineering, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria.
5 Department of Civil Engineering, Federal University of Technology, Owerri, Imo State, Nigeria
6 Department of Electrical & Electronics Engineering,Federal University of Technology, Owerri, Imo State, Nigeria
7 Department of Civil Engineering, Federal University of Oye Ekiti, Ekiti State, Nigeria.
8 Department of Civil Engineering, Federal University of Technology Akure, Ondo State, Nigeria.
9 Department of Electrical and Electronics Engineering, Michael Okpara University of Agriculture,Umudike ,Abia State Nigeria.
10 Department of Mechanical Engineering, Federal University of Technology, Minna, Niger State, Nigeria.
11 Department of Research and Development, Communication Towers Nigeria Limited, Abuja, Nigeria.
12 Department of Physics, Olusegun Agagu University of Science and Technology, Okitipupa, Ondo State, Nigeria.
13 Prema Consulting, Department of Digital Banking Services and Research, Nigeria.
14 North Carolina A&T State University, Greensboro. U.S.A.
Research Article
World Journal of Advanced Research and Reviews, 2024, 22(03), 1509-1517
Article DOI: 10.30574/wjarr.2024.22.3.1886
Received on 14 May 2024; revised on 18 June 2024; accepted on 21 June 2024
Metropolitan cities worldwide face severe traffic congestion due to a significant increase in vehicles, despite inadequate road infrastructure. Conventional traffic signaling systems, relying on manual or time-based control, are inefficient and lack real-time data, leading to delayed emergency response times, fuel waste, and health issues. To address this, a smart traffic management system is proposed, utilizing real-time data from sensors or Google Maps to optimize traffic light control at junctions. This system aims to efficiently manage signaled intersections, leveraging IoT technology and comparative data analysis to develop an algorithm that adapts to dynamic traffic conditions. This approach offers opportunities for advancements in traffic management, detection technology, and flexible optimization techniques through automated learning.
Internet of things; IoT sensor; Intelligent Transportation System; Real Time Optimization; Smart Traffic Management
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Abdulahi Akintayo Taiwo, Christian Chukwuemeka Nzeanorue, Samuel Ayanwunmi Olanrewaju, Qudus Omotayo Ajiboye, Azeez Adeyinka Idowu, Sanusi Hakeem, Chibuike Godswill Nzeanorue, Joshua Chukwuebuka Agba, Fakoyede Peter Dayo, Fakoyede Peter Dayo, Ewemade Cornelius Enabulele, Victor Ikechukwu Stephen, Ayodele Oyesanya, Matthew Idakwo Ogbe, Raphael Aduramimo Olusola, Olayiwola Blessing Akinnagbe and Abimbola Olamide. Intelligent transportation system leveraging Internet of Things (IoT) Technology for optimized traffic flow and smart urban mobility management. World Journal of Advanced Research and Reviews, 2024, 22(3), 1509-1517. Article DOI: https://doi.org/10.30574/wjarr.2024.22.3.1886