Encroachments in Traffic Management and Detection Systems through Machine Learning: A Review

Abstract

The invention proposes an adaptive TSC (Traffic Signal Control) system that uses real-time machine learning techniques to optimize traffic flow at intersections. The system uses continuous data streams from traffic sensors & cameras to dynamically adjusted signal timings depending upon the present traffic patterns and predicted future flows. This system reduces congestion, improves fuel efficiency, and enhances traffic management efficiency. The paper explores the amalgamation of ML techniques in management of traffic, highlighting its potential benefits and challenges. It suggests that developers can create intelligent transport systems that manage traffic data and provide supportive assessments for modern traffic management systems. This technology promises a safe, more effective, and also supportable urban-transportation networks by strongly altering the traffic-signal timings.