Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm

In this paper, the application of transportation systems in real-time traffic conditions is evaluated with data handling representations. The proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the system. Compared to the existing approach, the design model in the proposed method is made by dividing the computing areas into several cluster regions, thereby reducing the complex monitoring system where control errors are minimized. Furthermore, a route management technique is combined with Artificial Intelligence (AI) algorithm to transmit the data to appropriate central servers. Therefore, the combined objective case studies are examined as minimization and maximization criteria, thus increasing the efficiency of the proposed method. Finally, four scenarios are chosen to investigate the projected design's effectiveness. In all simulated metrics, the proposed approach provides better operational outcomes for an average percentage of 97, thereby reducing the amount of traffic in real-time conditions.