Real-time Traffic Analysis and Prediction Using Yolo Algorithm
Year: 2025
Type: article
Abstract: Currently the traffic control system in our country is non-flexible to the ever growing number of vehicles on the road. Traffic light is the basic element in traffic flow control through specified waiting and going time, fixed traffic light time systems is a bad control way. Intelligent traffic system includes smart way to control traffic light time based on number of vehicles in each lane. Improving traffic signal control system will increase safety, reliability, and traffic flow speed and reduce average travelling and waiting time for passengers. The objective is to design an efficient automatic Traffic Time Saver system. The system is implemented on the traffic control. In this proposed application system first captures the vehicle image. Vehicle image is extracted using the image segmentation finally converting the images from RGB to gray scale. Next, the segmentation is applied on the prepared image and then for each segment the neural networks will predict a vehicle or not. A counter will count the positive segments. Finally, the suitable periods for each light color will display in GUI. Keywords: Traffic Signal Control, Image Processing, Vehicle Detection, Real-time Traffic Management, Image Segmentation, Adaptive Signal Timing, Traffic Congestion Reduction.
DOI: 10.55041/ijsrem42726
Authors: Mrs. A. JEEVARATHINAM, KANISHKA. S
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408766264