TY  - EJOU
AU  - Hassan, Mahbub 
AU  - Islam, Md Kamrul 
AU  - Alam, Md Shafiul 
AU  - Amin, Mohammad Bin 
AU  - Rahman, M. M. Hafizur 
AU  - Haque, Md Ehtesamul 
AU  - Nagy, Zoltán 

TI  - Applications of IoT in Intelligent Transportation Systems: Research Landscape, Technological Innovations, Challenges, and Future Opportunities
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 89
IS  - 2
SN  - 1546-2226

AB  - The integration of the Internet of Things (IoT) into Intelligent Transportation Systems (ITS) is transforming urban mobility through widespread sensing, real-time data exchange, and Artificial Intelligence (AI)-driven adaptive control. Although research in this domain has expanded rapidly, bibliometric analyses combined with critical thematic synthesis remain limited. This study addresses this gap through a two-stage analysis of 574 peer-reviewed articles indexed in Scopus from 2011 to 2024. Using performance analysis, keyword co-occurrence mapping, and co-authorship network visualization, the study maps global publication trends, institutional productivity, and collaboration patterns. The results show an annual growth rate of 27.06%, with China, India, and the United States emerging as the leading contributors. Thematic evolution analysis identifies three research phases: foundational IoT connectivity from 2011 to 2019, distributed AI and fog computing from 2020 to 2022, and blockchain-enabled secure automation from 2023 to 2024. The synthesis covers eight application domains, including cybersecurity, blockchain, edge computing, traffic prediction, V2X communication, and signal optimization. It also proposes a functional taxonomy of IoT integration across sensing, communication, processing, control, security, and human-system interaction layers. Key deployment challenges include interoperability constraints, legacy system integration, data governance, and sociotechnical barriers to equitable adoption. Emerging research priorities include digital twins, federated learning, vehicular edge AI, post-quantum cryptography, trustworthy and explainable AI for safety-critical decision-making, and large language models as semantic reasoning layers within IoT-ITS workflows. This review provides a critically synthesized reference for researchers and policymakers and identifies practical directions for developing scalable, secure, and socially inclusive intelligent transportation systems.
KW  - Artificial intelligence; large language models; cybersecurity; digital twins; vehicular communication; Internet of Things; intelligent transportation systems

DO  - 10.32604/cmc.2026.085434