TY - GEN
T1 - Integration of IoT and Machine Learning Models for Enhancing Efficiency in Smart Public Transportation Systems
AU - Bhalodiya, Devanshi
AU - Sarda, Jigar
AU - Garg, Dweepna
AU - Yang, Tiansheng
AU - Rathore, Rajkumar Singh
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025/5/25
Y1 - 2025/5/25
N2 - 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.
AB - 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.
KW - IoT
KW - Performance prediction
KW - Real-time data analytics
KW - Smart transportation
KW - Smart urbanization
KW - Telematics
UR - http://www.scopus.com/inward/record.url?scp=105006917757&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-3247-3_18
DO - 10.1007/978-981-96-3247-3_18
M3 - Conference contribution
AN - SCOPUS:105006917757
SN - 9789819632466
T3 - Lecture Notes in Networks and Systems
SP - 221
EP - 237
BT - Proceedings of Fourth International Conference on Computing and Communication Networks, ICCCN 2024
A2 - Kumar, Akshi
A2 - Swaroop, Abhishek
A2 - Shukla, Pancham
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Computing and Communication Networks, ICCCN 2024
Y2 - 17 October 2024 through 18 October 2024
ER -
