Smart-Traffic-Simulation

AI-based traffic density detection and emergency vehicle prioritization

Smart Traffic Light System with Emergency Vehicle Prioritization

Full Project Title

Modelling and Simulation of Smart Traffic Light System for Emergency Vehicle using Image Processing Techniques

Overview

This project addresses urban congestion and emergency response delays by implementing an intelligent traffic management system. Using the YOLOv3 algorithm, the system detects vehicle density to further time the lanes in real-time and prioritizes emergency vehicles (ambulances, fire trucks) to ensure sustainable and efficient urban mobility.

Key Features

• Object Detection: Real-time vehicle identification using YOLOv3 and OpenCV.
• Dynamic Logic: Signal timing based on lane density rather than fixed intervals.
• Emergency Priority: Immediate signal override upon detection of priority vehicles.

Technical Implementation

• Architecture: Darknet / YOLOv3
• Language: Python
• Environment: macOS (Intel) / Windows

💡Note: To run this simulation, download the yolov3.weights file from Google Drive Google Drive and place it in the root directory."

Credits & References

• YOLOv3: Joseph Redmon's Darknet
• Dataset: COCO (Common Objects in Context)
• Inspired by: Research in Smart City Traffic Optimization for Emergency Response.