Dynamic Optimization of Traffic Management Using Gen AI

Dynamic optimization of traffic management using cloud GenAi is a sophisticated technique strengthened for the maximum improvementof urban traffic flow coupled with the relief of congestion through the adoption of cutting-edge image processing and deep learning techniques. Basically, the system begins with the process of image taking of vehicles in the dataset. Images of the cars are pre-processed using resizing and grey-scale conversion. This cloud-infrastructure feature provides integration with external data sources like weather and road incidents, thereby sharpening the prediction of traffic flow. The server will compute further based on the Signal Switching Algorithm that has the idea of optimizing flow through switching between multiple alternate stop settings. The process involved in the extraction of key features in an image makes use of statistical measures and LBP. After that, these images are separated into training sets and test sets for the development as well as evaluation of the detection model of a vehicle which would use YOLO. The vehiclecounts per unit length is then calculated in order to obtain the traffic density and then forwarded to the cloud server in order to store it. The accuracy anderror rate quantifies the performance of the system, and the result is reported in the form of comparison graphs and tables. The web application interface also facilitates further accessibility and engagement with the facility of registration, login, uploading images, andretrieving results.