Adaptive IoT Empowered Smart Road Traffic Congestion Control System Using Supervised Machine Learning Algorithm

Abstract The concept of smart systems blessed with different technologies can enable many algorithms used in Machine Learning (ML) and the world of the Internet of Things (IoT). In a modern city many different sensors can be used for information collection. Algorithms that are cast-off in Machine Le...

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Bibliographic Details
Published inComputer journal Vol. 64; no. 11; pp. 1672 - 1679
Main Authors Ata, Ayesha, Khan, Muhammad Adnan, Abbas, Sagheer, Khan, Muhammad Saleem, Ahmad, Gulzar
Format Journal Article
LanguageEnglish
Published Oxford University Press 01.11.2021
Subjects
prediction
smart systems
Internet of things
Machine Learning
neural networks
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Summary:Abstract The concept of smart systems blessed with different technologies can enable many algorithms used in Machine Learning (ML) and the world of the Internet of Things (IoT). In a modern city many different sensors can be used for information collection. Algorithms that are cast-off in Machine Learning improves the capabilities and intelligence of a system when the amount of data collectedincreases. In this research, we propose a TCC-SVM system model to analyse traffic congestion in the environment of a smart city. The proposed model comprises an ML-enabled IoT-based road traffic congestion control system whereby the occurrence of congestion at a specific point is notified.
ISSN:0010-4620
1460-2067
DOI:10.1093/comjnl/bxz129