Automatic Generation of Open Data-Based Traffic Simulation Model

Open transportation data is becoming more accessible, creating new opportunities for data-driven traffic simulation. However, transforming this data into a simulation-ready model is complex and time-consuming. This study introduces an automated framework to generate city-scale traffic simulation models using open data, including road networks, traffic signals, and vehicle volumes. Built on the Simulation of Urban Mobility (SUMO), the pipeline streamlines data processing and integrates real-world inputs into simulation components. The framework combines multiple open datasets through hybrid data synthesis, ensuring consistent road–signal coordination and enhancing traffic-flow realism and spatial resolution. Applied to a real urban center, the resulting models showed strong alignment with observed traffic patterns, supporting validity. As data collection, real-time monitoring, and spatial resolution advance, the framework’s applicability and precision will improve. By enabling efficient and reproducible model generation, this approach contributes to scalable traffic simulation and lays the groundwork for urban digital twin applications.