Abstract
Vehicle-to-Everything (V2X) communication systems rely on datasets that contain not only vehicle trajectory data but also wireless network parameters at a realistic fidelity level, enabling the creation of predictive and optimization models. There is a critical research infrastructure gap today, with publicly available datasets often limited to one of the two: mobility or network parameters, rarely offering an integrated view that combines both. This paper introduces MINT-V2X, a comprehensive dataset generated by coupling SUMO traffic dynamics with OMNeT++/Simu5G network simulation.
Dataset Composition
The validation framework consists of 14 standardized tests based on 3GPP Release 14 (C-V2X), ETSI standards, and Shannon capacity theory. The resulting dataset contains 9.87 million synchronized data points from 1,386 vehicles and 15 roadside units (RSUs) over 3 hours of urban traffic simulation.
We demonstrate strict algorithmic consistency through network metric correlations (CQI-SINR: 0.993; SINR-PDR: 0.946). Finally, we showcase the dataset's value by conducting an RSU load prediction case study, indicating that using trajectory data yields better predictive performance than network-history-only baselines.
The dataset, experiments, and complete SUMO configuration files are available in the GitHub repository to facilitate reproduction on alternative simulation stacks.
Blogger's Review: The MINT-V2X dataset provides a crucial foundation for V2X communication research, especially in analyzing the interplay between traffic and network parameters. With high-quality data and validation mechanisms, researchers can more effectively develop and optimize relevant models, advancing the development of intelligent transportation systems. The open-source nature of this dataset also fosters collaboration and innovation between academia and industry.