Traffic flow diversion control system on scheduling algorithm

The phenomenon of traffic congestion is now widespread in the traffic of major and medium-sized cities. This study creates a traffic flow diversion system with a backend command, control system management, and a data monitoring visualization large screen surveillance side to enhance operation management, incident response effectiveness, and traffic system. The system leverages relevant expertise in machine learning and visual analytics for target tracking, violation detection, and other tasks. Jetson NX receives deep learning models from Deepstream [2] to enable secure two-way communication between the edge and the cloud. The system also uses signal lights to implement intelligent traffic guidance. In this study, we illustrate the simulation using Pygame, and the simulation outcomes show the efficacy of scheduling.