Adaptive IoT Empowered Smart Road Traffic Congestion Control System Using Supervised Machine Learning Algorithm. (19th May 2020)

• Record Type: Journal Article
• Authors: Ata, Ayesha; Khan, Muhammad Adnan; Abbas, Sagheer; Khan, Muhammad Saleem; Ahmad, Gulzar
• Is Part Of: Computer journal. Volume 64:Number 11(2021)
• Page Start: 1672
• Page End: 1679
• Publication Date: 2020-05-19
• Subjects: neural networks -- prediction -- Machine Learning -- Internet of things -- smart systems; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxz129
• Languages: English
• ISSNs: 0010-4620

Abstract

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.