Abstract

Efficient transportation systems are essential for guaranteeing smooth movement and decreasing congestion in metropolitan areas in today's increasingly urbanizing globe. Urban transportation networks rely heavily on metro systems, which carry millions of passengers every day. Nonetheless, controlling traffic near metro stations is still a difficult task that frequently results in clogged roads, delays, and jeopardized passenger safety. In this regard, utilizing cutting-edge technology like the Internet of Things (IoT) presents viable ways to improve traffic monitoring and management at metro stations. This research also investigates the use of predictive analytics methods and machine learning algorithms to examine the massive volumes of data produced by Internet of Things sensors. Additionally, the incorporation of IoT-capable communication systems facilitates the smooth distribution of information to travellers, offering them the most recent warnings, notifications, and recommended routes to improve their trip experience. This study article clarifies the possible advantages and difficulties related to the use of IoT for enhanced metro station traffic monitoring and management using a mix of theoretical analysis, case studies, and real-world demonstrations. Through promoting data-driven decisionmaking, increasing operational effectiveness.