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<p>Urban traffic congestion is a major challenge affecting sustainability, energy efficiency, and mobility in smart cities. The emergence of 5G/6G and beyond networks, combined with intelligent wireless communication and sensing, offers unprecedented opportunities for real-time traffic monitoring, AI-driven congestion modeling, and dynamic routing optimization. Despite these advancements, existing 5G/6G-enabled intelligent transportation systems studies remain fragmented with limited integration between wireless communication, sensing, vehicular traffic modeling, which reduces their effectiveness in addressing real-world urban traffic congestion challenges. A systematic literature review was conducted using the PRISMA framework, synthesizing 49 peer-reviewed studies published between 2019 and 2026 to identify key trends, methodologies and applications that leverage 5G/6G-enabled sensing and communication for vehicular congestion reduction. However, current review studies primarily focus on isolated aspects such as communication architectures, AI techniques, or vehicular networking frameworks, and lack a comprehensive and systematic synthesis that examines intelligent wireless communication and sensing for sustainable traffic modeling. Findings indicate that existing studies widely explore the integration of 5G/6G-enabled vehicular communication, IoT sensing, AI-driven traffic prediction, and edge intelligence for real-time congestion management. Nevertheless, full integration across communication, sensing, and traffic modeling remains limited, with privacy, scalability, cost, and interoperability continuing to pose major challenges. Integration of 5G/6G, IoT sensing, and AI traffic modeling enables real-time congestion management and efficient routing, directly supporting sustainability goals such as reduced energy use, lower emissions, and improved urban mobility. To the best of our knowledge, this is the first PRISMA-guided systematic review that provides a unified cross-layer synthesis of intelligent wireless communication, sensing technologies, and AI-driven traffic modeling specifically for sustainable vehicular congestion reduction in 5G/6G-enabled ITS. By examining 5G/6G-enabled ITS, this study complements existing surveys through a unified cross-layer synthesis of communication, sensing, traffic modeling, and sustainability aspects, while providing practical insights for developing efficient and AI-driven urban traffic systems. Furthermore, the findings support policy decisions that prioritize 5G/6G infrastructure investment, edge computing deployment and interoperable ITS standards to enable scalable real-world implementation.</p>

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A systematic review of intelligent wireless communication and sensing for sustainable 5G and 6G enabled intelligent transportation systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月4日

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  • Benjamin Simwinga,
  • Aaron Zimba,
  • Mayumbo Nyirenda

摘要

Urban traffic congestion is a major challenge affecting sustainability, energy efficiency, and mobility in smart cities. The emergence of 5G/6G and beyond networks, combined with intelligent wireless communication and sensing, offers unprecedented opportunities for real-time traffic monitoring, AI-driven congestion modeling, and dynamic routing optimization. Despite these advancements, existing 5G/6G-enabled intelligent transportation systems studies remain fragmented with limited integration between wireless communication, sensing, vehicular traffic modeling, which reduces their effectiveness in addressing real-world urban traffic congestion challenges. A systematic literature review was conducted using the PRISMA framework, synthesizing 49 peer-reviewed studies published between 2019 and 2026 to identify key trends, methodologies and applications that leverage 5G/6G-enabled sensing and communication for vehicular congestion reduction. However, current review studies primarily focus on isolated aspects such as communication architectures, AI techniques, or vehicular networking frameworks, and lack a comprehensive and systematic synthesis that examines intelligent wireless communication and sensing for sustainable traffic modeling. Findings indicate that existing studies widely explore the integration of 5G/6G-enabled vehicular communication, IoT sensing, AI-driven traffic prediction, and edge intelligence for real-time congestion management. Nevertheless, full integration across communication, sensing, and traffic modeling remains limited, with privacy, scalability, cost, and interoperability continuing to pose major challenges. Integration of 5G/6G, IoT sensing, and AI traffic modeling enables real-time congestion management and efficient routing, directly supporting sustainability goals such as reduced energy use, lower emissions, and improved urban mobility. To the best of our knowledge, this is the first PRISMA-guided systematic review that provides a unified cross-layer synthesis of intelligent wireless communication, sensing technologies, and AI-driven traffic modeling specifically for sustainable vehicular congestion reduction in 5G/6G-enabled ITS. By examining 5G/6G-enabled ITS, this study complements existing surveys through a unified cross-layer synthesis of communication, sensing, traffic modeling, and sustainability aspects, while providing practical insights for developing efficient and AI-driven urban traffic systems. Furthermore, the findings support policy decisions that prioritize 5G/6G infrastructure investment, edge computing deployment and interoperable ITS standards to enable scalable real-world implementation.