Machine Learning and 5G Edge Computing for Intelligent Traffic Management

The integration of fifth-generation (5G) communication technology and Artificial Intelligence (AI) is reshaping urban mobility by enabling intelligent transportation systems and smarter cities. This synergy allows real-time traffic management, predictive maintenance, and enhanced autonomous driving, supported by high-speed, low-latency networks and advanced data analytics. By leveraging 5G’s strong connectivity, AI systems can process massive datasets to address urban challenges such as traffic congestion, environmental sustainability, and public safety. This study presents a framework that combines 5G and AI to optimize traffic management through dynamic congestion prediction and real-time routing, supported by edge computing. It highlights the benefits of improving traffic flow, reducing emissions, and enhancing overall urban mobility efficiency. In addition, it discusses key challenges including data privacy concerns, cybersecurity risks, and the high cost of infrastructure deployment. By analyzing existing technologies and proposing an AI-driven, 5G-enabled system model, this study aims to bridge the gap between theoretical advancements and practical urban implementations. The findings provide insights into scalable, efficient solutions for the future of smart transportation networks and offer directions for further research in this dynamic and evolving field.

@article{Chaymae2025,
  title     = {Machine Learning and 5G Edge Computing for Intelligent Traffic Management},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {6},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Talbi Chaymae and Rahmouni M'hamed and Ziti Soumia},
  doi       = {10.14569/IJACSA.2025.0160644},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160644}
}