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Title	Integration Of Iot And Machine Learning Models For Enhancing Efficiency In Smart Public Transportation Systems
ID_Doc	32177
Authors	Bhalodiya D.; Sarda J.; Garg D.; Yang T.; Rathore R.S.
Year	2025
Published	Lecture Notes in Networks and Systems, 1293
DOI	http://dx.doi.org/10.1007/978-981-96-3247-3_18
Abstract	With the increasing demand for efficient and reliable public transportation systems, the integration of the Internet of Things (IoT) and machine learning models has emerged as a transformative approach. This paper aims to understand the possible improvements of using IoT and machine learning in optimizing the functionality of smart public transport systems. It outlines an extensive architecture that makes use of the real-time data collected through the IoT sensors to apply the machine learning algorithms to different areas of public transport such as the schedules, the routes, and the maintenance. With the ability to study the current flow of passengers and the movement of the vehicles, the proposed system will act to minimize delays, increase service delivery, and control passenger satisfaction levels. This paper outlines the method for deploying the integrated system and analyzes the results based on the simulations carried out in this research study; this paper also addresses the implications of the study on future smart city developments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Author Keywords	IoT; Performance prediction; Real-time data analytics; Smart transportation; Smart urbanization; Telematics

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