Realistic Urban Traffic Generator using Decentralized Federated Learning for the SUMO simulator

TL;DR

This paper presents DesRUTGe, a decentralized federated learning framework that uses DRL agents with SUMO to generate realistic, privacy-preserving urban traffic patterns, improving accuracy over existing methods.

Contribution

Introduction of DesRUTGe, a novel decentralized federated learning approach integrating DRL and SUMO for realistic urban traffic simulation.

Findings

Outperforms standard SUMO tools like RouteSampler in accuracy.

Preserves privacy by avoiding centralized data collection.

Effective in large-scale, real-world scenarios like Barcelona.

Abstract

Realistic urban traffic simulation is essential for sustainable urban planning and the development of intelligent transportation systems. However, generating high-fidelity, time-varying traffic profiles that accurately reflect real-world conditions, especially in large-scale scenarios, remains a major challenge. Existing methods often suffer from limitations in accuracy, scalability, or raise privacy concerns due to centralized data processing. This work introduces DesRUTGe (Decentralized Realistic Urban Traffic Generator), a novel framework that integrates Deep Reinforcement Learning (DRL) agents with the SUMO simulator to generate realistic 24-hour traffic patterns. A key innovation of DesRUTGe is its use of Decentralized Federated Learning (DFL), wherein each traffic detector and its corresponding urban zone function as an independent learning node. These nodes train local DRL models using minimal historical data and collaboratively refine their performance by exchanging model parameters with selected peers (e.g., geographically adjacent zones), without requiring a central coordinator. Evaluated using real-world data from the city of Barcelona, DesRUTGe outperforms standard SUMO-based tools such as RouteSampler, as well as other centralized learning approaches, by delivering more accurate and privacy-preserving traffic pattern generation.