Open Access

Erik Vogel

• The escalation of traffic volume in urban areas poses multifaceted challenges including increased accident risks, congestion, and prolonged travel times. Traditional approaches of expanding road infrastructure face limitations such as space constraints and the potential exacerbation of traffic issues. Intelligent Transport Systems (ITS) present an alternative strategy to alleviate traffic problems by leveraging data-driven solutions. Central to ITS is traffic prediction, a process vital for applications like Traffic Management and Navigation Systems. Recent advancements in traffic prediction have witnessed a surge of interest, particularly in deep learning methods optimized for graph-based data processing, being considered the most promising avenue presently. These methods typically rely on real-life datasets containing traffic sensor data such as METR-LA and PeMS. However, the finite nature of real-life data prompts exploration into augmenting training and testing datasets with simulated traffic data. This thesis explores the potential of utilizing traffic simulations, employing the microscopic traffic simulator SUMO, to train and test deep learning models for traffic prediction. A framework integrating PyTorch and SUMO is proposed for this purpose, aiming to elucidate the feasibility and effectiveness of using simulated traffic data for enhancing predictive models in traffic management systems.