The integration and announcement platform of road traffic incidents can allow traffic control centers to effectively implement real-time traffic management and enhance travelers' trips by rerouting them where necessary. Nevertheless, there are not enough studies that can fully explain how artificial intelligence (AI) can be utilized to make a positive difference in this industry. In order to fill this need, this study has devised AI logics of object detection, along with a deep learning neural network, in order to identify and record traffic misdemeanors. This can then enable the traffic department to efficiently control and monitor how motorists behave while driving. Factors that will be monitored in the proposed AI-based traffic incident logics are illegal parking along a red line, parking violations, intersection overflows, and driving in the wrong direction. In order to test the effectiveness of this AI-based method, a field case was conducted with the following results. The empirical results reveal that the overall accuracy of parking violations is 73.33%, the overall accuracy of illegal parking along a red line is 95.95%, the overall accuracy of intersection overflows is 99.40%, and the overall accuracy of driving in the wrong direction is 39.28%. This study further examines which components of the AI traffic incident detection might be inexact and outlines potential approaches that could enhance the accuracy of these models.