RUTGe: Realistic Urban Traffic Generator for Urban Environments Using Deep Reinforcement Learning and SUMO Simulator

Alberto Bazán Guillén, Pablo A. Barbecho Bautista, Mónica Aguilar Igartua

2025

Abstract

We are witnessing a profound shift in societal and political attitudes, driven by the visible consequences of climate change in urban environments. Urban planners, public transport providers, and traffic managers are urgently reimagining cities to promote sustainable mobility and expand green spaces for pedestrians, bicycles, and scooters. To design more sustainable cities, urban planners require realistic simulation tools to optimize mobility, identify location for car chargers, convert streets to pedestrian zones, and evaluate the impact of alternative configurations. However, realistic traffic profiles are essential to produce meaningful simulation results. Addressing this need, we propose a traffic generator based on deep reinforcement learning integrated with the SUMO simulator. This tool learns to generate an instantaneous number of vehicles throughout the day, aligning closely with the target profiles observed at the traffic monitoring stations. Our approach generates accurate 24-hour traffic patterns for any city using minimal statistical data, achieving higher accuracy compared to existing alternatives. In particular, our proposal demonstrates a highly accurate 24-hour traffic adjustment, with the generated traffic deviating only by about 5% from the real target traffic. This performance significantly exceeds that of current SUMO tools like RouteSampler, which struggle to accurately follow the total daily traffic curve, especially during peak hours when severe traffic congestion occurs.

Paper Citation

in Harvard Style

Bazán Guillén A., Barbecho Bautista P. and Aguilar Igartua M. (2025). RUTGe: Realistic Urban Traffic Generator for Urban Environments Using Deep Reinforcement Learning and SUMO Simulator. In Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS; ISBN 978-989-758-745-0, SciTePress, pages 557-564. DOI: 10.5220/0013375000003941

in Bibtex Style

@conference{vehits25,
author={Alberto Bazán Guillén and Pablo Barbecho Bautista and Mónica Aguilar Igartua},
title={RUTGe: Realistic Urban Traffic Generator for Urban Environments Using Deep Reinforcement Learning and SUMO Simulator},
booktitle={Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS},
year={2025},
pages={557-564},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013375000003941},
isbn={978-989-758-745-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS
TI - RUTGe: Realistic Urban Traffic Generator for Urban Environments Using Deep Reinforcement Learning and SUMO Simulator
SN - 978-989-758-745-0
AU - Bazán Guillén A.
AU - Barbecho Bautista P.
AU - Aguilar Igartua M.
PY - 2025
SP - 557
EP - 564
DO - 10.5220/0013375000003941
PB - SciTePress