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Fusion: Practice and Applications

Volume 11 , Issue 1 , PP: 114-128, 2023 | Cite this article as | XML | Html | PDF | Full Length Article

Using a Fuzzy Logic Integrated Machine Learning Algorithm for Information Fusion in Smart Parking

Mohammed Abdul J. Maktoof 1 * , Anwar Ja’afar M. Jawad 2 , Hasan M. Abd 3 , Ahmed Husain 4 , Ali Majdi 5

  • 1 Department of Computer Techniques Engineering, Al-Turath University College, Baghdad, 10021, Iraq - (mohammed.maktof@turath.edu.iq)
  • 2 Department of Medical device technology Engineering, Al-Rafidain University College, Baghdad 10064, Iraq - (anwar.jawad@ruc.edu.iq)
  • 3 Computer Techniques Engineering Department, Mazaya University College, Thi Qar, Iraq - (eng.hassan@mpu.edu.iq)
  • 4 Department of Medical device technology Engineering, Alfarahidi University, Baghdad, Iraq - (Ahmed.hussein@alfarahidiuc.edu.iq)
  • 5 Department of Buildings and Construction Techniques Engineering, Al-Mustaqbal University College, 51001 Hillah, Babylon , Iraq - (alimajdi@uomus.edu.iq)

    • Doi: https://doi.org/10.54216/FPA.110109

    Received: December 11, 2022 Accepted: April 01, 2023
    Abstract

    The free flow of people and products within metropolitan areas depends on well-managed transportation systems. However, public parking places in smart cities are often limited by traffic, causing cars and residents to waste time, money, and fuel. To counteract this issue, today's automobile systems combine information fusion with intelligent parking solutions. In this research, we present a Fuzzy Logic Integrated Machine Learning Algorithm (FL-MLA) for use in smart parking and traffic management in a metropolis. The FL-MLA use fuzzy induction to distinguish between parked and moving vehicles while calculating traffic flow. The suggested technique efficiently resolves the problem of locating suitable parking places by avoiding incorrect configurations that govern traffic management difficulties. Therefore, the FL-MLA is used in traffic management systems to boost performance metrics like efficiency ratio (98.1%) and accident detection (98.1%) based on simulation results like reduced energy consumption (95.3%), more accurate traffic estimation (97.9%), higher average daily park occupancy (97.2%), and higher efficiency ratio (98.1%).

    Keywords :

    Traffic management , artificial neural network , information fusion , smart parking , smart transport system , information fusion , fuzzy controller.

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    Cite This Article As :
    Abdul, Mohammed. , Ja’afar, Anwar. , M., Hasan. , Husain, Ahmed. , Majdi, Ali. Using a Fuzzy Logic Integrated Machine Learning Algorithm for Information Fusion in Smart Parking. Fusion: Practice and Applications, vol. , no. , 2023, pp. 114-128. DOI: https://doi.org/10.54216/FPA.110109
    Abdul, M. Ja’afar, A. M., H. Husain, A. Majdi, A. (2023). Using a Fuzzy Logic Integrated Machine Learning Algorithm for Information Fusion in Smart Parking. Fusion: Practice and Applications, (), 114-128. DOI: https://doi.org/10.54216/FPA.110109
    Abdul, Mohammed. Ja’afar, Anwar. M., Hasan. Husain, Ahmed. Majdi, Ali. Using a Fuzzy Logic Integrated Machine Learning Algorithm for Information Fusion in Smart Parking. Fusion: Practice and Applications , no. (2023): 114-128. DOI: https://doi.org/10.54216/FPA.110109
    Abdul, M. , Ja’afar, A. , M., H. , Husain, A. , Majdi, A. (2023) . Using a Fuzzy Logic Integrated Machine Learning Algorithm for Information Fusion in Smart Parking. Fusion: Practice and Applications , () , 114-128 . DOI: https://doi.org/10.54216/FPA.110109
    Abdul M. , Ja’afar A. , M. H. , Husain A. , Majdi A. [2023]. Using a Fuzzy Logic Integrated Machine Learning Algorithm for Information Fusion in Smart Parking. Fusion: Practice and Applications. (): 114-128. DOI: https://doi.org/10.54216/FPA.110109
    Abdul, M. Ja’afar, A. M., H. Husain, A. Majdi, A. "Using a Fuzzy Logic Integrated Machine Learning Algorithm for Information Fusion in Smart Parking," Fusion: Practice and Applications, vol. , no. , pp. 114-128, 2023. DOI: https://doi.org/10.54216/FPA.110109

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