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.

中文翻译：

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基于监督机器学习算法的自适应物联网赋能智能道路交通拥堵控制系统

拥有不同技术的智能系统的概念可以实现机器学习（ML）和物联网（IoT）世界中使用的许多算法。在现代城市中，许多不同的传感器可用于信息收集。当收集的数据量增加时，机器学习中抛弃的算法将提高系统的功能和智能。在这项研究中，我们提出了一种TCC-SVM系统模型来分析智能城市环境中的交通拥堵。提出的模型包括基于ML的，基于IoT的道路交通拥堵控制系统，从而通知特定点的拥塞情况。