Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3601 2019 2019 Computer Engineering Department, University of Technology- Iraq, Baghdad, Iraq Noor Noor Biomedical Engineering Department, University of Technology- Iraq, Baghdad, Iraq Rand A. Atta Biomedical Engineering Department, University of Technology- Iraq, Baghdad, Iraq Hussein M. Hasan Biomedical Engineering Department, University of Technology- Iraq, Baghdad, Iraq Asmaa S. Zamil Biomedical Engineering Department, University of Technology- Iraq, Baghdad, Iraq Saja B. Attallah Car crowd management refers to the process of efficiently and safely managing the movement and flow of cars in crowded areas, such as parking lots, traffic intersections, event venues, and busy streets. Effective car crowd management is essential to ensure smooth traffic flow, prevent accidents, reduce congestion, and optimize the utilization of available parking spaces. It is a critical aspect of urban planning and traffic management to enhance the overall transportation experience and safety for both drivers and pedestrians. Deep learning methods are used to create an artificial system that is shown in this study. Proposed in detecting cars in streets and traffic intersections, in addition to determining the quantity of cars based on the YOLOv8 algorithm. Where the proposed system was trained on three types of datasets for the purpose of testing the algorithm used to determine the number of cars in each direction of the traffic intersection and then give priority to the most crowded direction with cars and then less and less. Where the system reached a high accuracy in detecting cars, reaching 98%, and through it conclude that the YOLOv8 algorithm used was suitable to be employed in solving the problem of determining the priority of traffic by identifying places of congestion with high accuracy. 2025 2025 132 141 10.54216/JISIoT.160111 https://www.americaspg.com/articleinfo/18/show/3601