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Title	Rutge: Realistic Urban Traffic Generator For Urban Environments Using Deep Reinforcement Learning And Sumo Simulator
ID_Doc	47163
Authors	Guillén A.B.; Barbecho Bautista P.A.; Igartua M.A.
Year	2025
Published	International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS - Proceedings
DOI	http://dx.doi.org/10.5220/0013375000003941
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. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.
Author Keywords	Deep Reinforcement Learning; Realistic Simulator; Smart Cities; SUMO Traffic Generation

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