Traffic Vision: AI-Powered Traffic Monitoring System and Signal Optimization

Abstract

This paper is focused on Traffic Vision, an integrated AI-powered traffic monitoring and management system built on computer vision, machine learning, and adaptive control strategies to optimize urban traffic flows. The major functions of the system include real-time processing of video feeds for vehicle, pedestrian, emergency vehicle, and accident detection and tracking, as well as traffic density and flow parameters. A new adaptive traffic signal control algorithm employs this information to dynamically adapt traffic light timings according to current conditions. Experimental results show a wait time reduction of up to 30% in intersections and highly improved emergency vehicle response times. This modular architecture allows it to easily fit into any urban framework and match existing infrastructure monitoring systems.

Introduction

Traffic Vision is an advanced, integrated traffic management system designed to address urban traffic congestion, which causes significant economic losses. Traditional traffic control methods rely on fixed timers or simple sensors that lack responsiveness to complex traffic dynamics. Traffic Vision leverages deep learning and computer vision—specifically YOLOv8 and ByteTrack algorithms—to enable real-time detection, tracking, and analysis of vehicles and pedestrians using video feeds without extensive sensor deployment.

Key features include:

• Real-time monitoring of traffic flows.

• Emergency vehicle detection and prioritization.

• Incident detection with automated response.

• Traffic density heatmaps for visual analysis.

• Adaptive traffic signal control adjusting lights based on live traffic.

• Comprehensive data collection for planning and analysis.

The system uses a modular architecture with components like Zone Manager (for detection/tracking), Traffic Light Controller (adaptive signaling), Data Collector, Notification System, and an Analytics Dashboard for visualization.

Methodology highlights:

• Object detection with multiple specialized YOLOv8 models.

• Tracking using ByteTrack for consistent object identification.

• Custom zone definitions for localized traffic counting.

• Speed estimation through frame-by-frame tracking with smoothing.

• Heatmaps generated by accumulating object presence data.

• Adaptive signal timing and emergency protocols activated automatically upon accident detection.

Implementation involves Python-based technologies (OpenCV, PyQt6, SQLite, Telegram API) and provides an intuitive interface for configuration, real-time visualization, and alerts. The system workflow processes video frames to detect, track, estimate speeds, update counts, control signals, store data, and notify operators of critical events.

Conclusion

Traffic Vision exhibits the effectiveness of an integrated approach to traffic monitoring and management. The integration of state-of-the-art computer vision techniques with adaptive control strategies allows the system to offer comprehensive traffic intelligence and management capabilities. Experimental results indicate a tremendous increase in traffic flow efficiency and emergency response times. The modular architecture and extensible design of Traffic Vision allow for its adaptation to a wide array of urban environments and use cases. As smart city initiatives continue to expand, systems such as Traffic Vision are bound to become indispensable in resolving challenges presented by urban mobility and enhancing the quality of life for city occupants.

References

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Copyright

Copyright © 2025 Prabu M, Nandhakumar R, Janani M, Aryan Thakur, Santhosh S, Vasavi A. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.