Autonomous Traffic Signal Optimization Using Digital Twin and Agentic AI for Real-Time Decision-Making

TL;DR

This paper presents a digital twin and agentic AI framework for autonomous, real-time traffic signal optimization that outperforms traditional methods.

Contribution

It introduces a novel three-layer system integrating perception, conceptualization, and action for adaptive traffic light control using digital twins and AI.

Findings

Minimizes waiting time at traffic lights

Improves overall traffic flow efficiency

Outperforms fixed-time and reinforcement learning baselines

Abstract

This article outlines a new framework of traffic light optimization through a digital twin of the transport infrastructure, managed by agentic AI to ensure real-time autonomous decisions. The framework relies on physical sensors and edge computing to measure real-time traffic information and simulate traffic flow in a constantly updated digital twin. The traffic light is automatically controlled through the digital twin according to traffic congestion, travel delay and traffic patterns. This approach is implemented as a three-layer system: perception, conceptualization and action. The perception layer receives data on physical systems; the conceptualization layer uses LangChain to process the data; and the action layer links to the Model Context Protocol (MCP) and traffic management APIs to implement optimised traffic signal control algorithms. The results show that the framework minimizes waiting time at traffic lights and positively affects the effectiveness of the entire traffic flow, which is better than the fixed-time and reinforcement learning-based baselines.