AI-Driven Intelligent Transportation IoT Platform for Congestion Reduction
DOI:
https://doi.org/10.46610/IJMCSE.2026.v02i01.004Abstract
The continuous expansion of urban populations, coupled with the growing need for efficient mobility solutions, has significantly worsened traffic congestion in contemporary cities. Conventional traffic management approaches, largely based on fixed, non-adaptive control mechanisms, are increasingly unable to cope with the complexity and variability of modern urban traffic systems. In response to these limitations, this study introduces an intelligent transportation platform driven by Artificial Intelligence (AI) and the Internet of Things (IoT), aimed at mitigating congestion and enhancing traffic flow through real-time data processing and adaptive decision-making. The proposed framework leverages the combined capabilities of AI, IoT, and Big Data technologies to facilitate continuous monitoring, anticipate congestion patterns, and enable dynamic traffic regulation. The research methodology is grounded in a comprehensive documentary review of recent scientific literature (2020–2025), which supports the identification of key technological components, system architectures, and analytical models relevant to intelligent transportation systems. The findings indicate that integrating AI with IoT technologies substantially improves transportation system performance. This integration enables real-time observation of traffic conditions, early detection of congestion hotspots, and more efficient route optimization. Furthermore, the platform enhances adaptive traffic signal control and supports data-driven decision-making processes, resulting in smoother traffic flow, reduced delays, and improved overall system efficiency. This study contributes to the advancement of intelligent transportation systems by proposing a scalable and integrated framework aligned with smart city development goals. It underscores the transformative potential of AI–IoT platforms in reshaping urban mobility and provides a solid foundation for future research and practical implementation.
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