Smart Traffic Management System Using Computer Vision and IoT

Urban traffic congestion causes economic loss, pollution, and safety risks. Traditional traffic systems operate on static timers, ignoring real-time conditions. This research project proposes an intelligent traffic management system using computer vision and IoT sensors.

Problem Statement

Static traffic signals fail to adapt to dynamic vehicle density. Emergency vehicles often face delays due to lack of intelligent prioritization.

Research Objectives

The objective is to design a system that detects traffic density in real time and dynamically adjusts signal timing. The project also explores emergency vehicle detection.

Methodology

Cameras capture live video feeds which are processed using object detection algorithms to count vehicles. IoT controllers communicate signal timing changes based on traffic load.

Technologies Used

Computer vision frameworks, microcontrollers, cloud dashboards, and real-time data processing pipelines are utilized.

Expected Outcomes

The system improves traffic flow, reduces waiting time, and supports smart city initiatives.

Conclusion

This project demonstrates the integration of AI and IoT to solve large-scale urban challenges.