Autonomous Traffic Control System Using Agent Based Technology

TL;DR

This paper proposes an autonomous, agent-based traffic control system utilizing intelligent learning algorithms for real-time traffic management in large cities, addressing challenges posed by rapid urban growth.

Contribution

It introduces a novel agent-based architecture for traffic management that incorporates video analysis and learning algorithms to optimize traffic flow in urban environments.

Findings

Effective real-time traffic flow estimation using video analysis.

Improved traffic management through intelligent agent coordination.

Addressed challenges of urban traffic growth with autonomous systems.

Abstract

The way of analyzing, designing and building of real-time projects has been changed due to the rapid growth of internet, mobile technologies and intelligent applications. Most of these applications are intelligent, tiny and distributed components called as agent. Agent works like it takes the input from numerous real-time sources and gives back the real-time response. In this paper how these agents can be implemented in vehicle traffic management especially in large cities and identifying various challenges when there is a rapid growth of population and vehicles. In this paper our proposal gives a solution for using autonomous or agent based technology. These autonomous or intelligent agents have the capability to observe, act and learn from their past experience. This system uses the knowledge flow of precedent signal or data to identify the incoming flow of forthcoming signal. Our architecture involves the video analysis and exploration using some Intelligence learning algorithm to estimate and identify the flow of traffic.