# IoT-Simulated Digital Twin with AI Traffic Signal Control for Real-Time Traffic Optimization in SUMO

## TL;DR

This paper introduces an IoT-based digital twin system combined with AI to optimize real-time traffic control in a simulated urban environment.

## Contribution

A novel IoT-driven digital twin framework integrated with AI for adaptive traffic signal control in SUMO simulations.

## Key findings

- The AI agent reduced vehicle waiting times and emissions compared to traditional control methods.
- The framework enables safe and scalable testing of intelligent traffic systems before real-world deployment.
- Simulation results show effectiveness under varying traffic demand scenarios.

## Abstract

Urban traffic congestion leads to longer travel times, economic losses, and increased pollution. Recent advances in the Internet of Things (IoT) provide detailed real-time traffic data, yet testing adaptive control strategies directly on live networks remains costly and risky. To address this challenge, we propose an IoT-driven digital twin framework for the design and evaluation of AI-based traffic management systems. The framework is implemented in the Simulation of Urban MObility (SUMO) and uses its Python 3.14.2 API to emulate a dense network of IoT sensors that stream real-time information on vehicle density, queue lengths, and waiting times. This simulated IoT data feeds an AI agent that adapts traffic signal control in real time. The agent is trained with a composite reward function to jointly minimise vehicle waiting times and emissions. Its performance is compared with fixed-time and vehicle-actuated control under varying traffic demand scenarios. Results demonstrate the effectiveness of combining IoT-based simulation with AI control, providing a safe and scalable pathway towards the real-world deployment of intelligent traffic management systems.