García-González, JorgeFernández-Andrés, JavierAliane, NourdineSánchez-Soriano, Javier2026-01-272026-01-272025García-González, J, Fernández-Andrés, J, Aliane, N & Sánchez-Soriano, J 2025, 'Big Data and I2X Communication Infrastructure for Traffic Optimization and Accident Prevention on Automated Roads', IEEE Access, vol. 13, pp. 133497-133509. https://doi.org/10.1109/ACCESS.2025.35923102169-3536dblp: journals/access/GarciaGonzalezAAS25unpaywall: 10.1109/access.2025.3592310https://hdl.handle.net/10641/7560Publisher Copyright: © IEEE. 2013 IEEE.This paper presents a scalable big data infrastructure designed to support traffic optimization and accident prevention in automated and connected road environments. The proposed system integrates real-time data acquisition from heterogeneous sources, including multichannel roadside camera gantries and IoT-enabled vehicle telemetry. The architecture is built upon technologies such as Apache Kafka, Apache Beam, and MongoDB, enabling high-throughput data ingestion, processing, and storage. To validate its performance, two experimental use-cases were developed: one for large-scale image ingestion and another for vehicle telemetry data streaming. The system successfully handled over 48 GB of image data and more than 3.4 million telemetry messages under real-time constraints. Results show that applying data compression techniques - such as resolution reduction and transmission throttling - reduced image upload durations by up to 77%, improving ingestion efficiency without compromising system robustness. These findings demonstrate the feasibility of deploying the proposed infrastructure as a foundational layer for future intelligent traffic management systems.137043484enghttp://creativecommons.org/licenses/by-nc-nd/4.0/Big dataV2X communicationinfrastructureintelligent transportation systems (ITS)real-timestreamingGeneral Computer ScienceGeneral Materials ScienceGeneral EngineeringYesyesjournal articleopen access10.1109/ACCESS.2025.3592310https://www.scopus.com/pages/publications/105011694368https://www.scopus.com/pages/publications/105011694368#tab=citedBy