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

arXiv: https://arxiv.org/abs/2604.27753 · HTML: https://mlsanity.com/paper/2604.27753/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 3 of 3)

- [Agentic AI Framework for Individuals with Disabilities and Neurodivergence: A Multi-Agent System for Healthy Eating, Daily Routines, and Inclusive Well-Being](https://mlsanity.com/paper/2511.22737/) (2025)
- [A Blockchain-Monitored Agentic AI Architecture for Trusted Perception-Reasoning-Action Pipelines](https://mlsanity.com/paper/2512.20985/) (2025)
- [Agentic AI Framework for Cloudburst Prediction and Coordinated Response](https://mlsanity.com/paper/2511.22767/) (2025)

## Abstract (arXiv)

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.