What question did this study set out to answer?

The aim is to develop an intelligent system for monitoring and predicting traffic congestion using machine learning and IoT.

June 14, 2026

Intelligent Real-Time Traffic Congestion Monitoring and Prediction System Using Machine Learning

Key Points

The aim is to develop an intelligent system for monitoring and predicting traffic congestion using machine learning and IoT.
Design and implement a traffic system integrating IoT sensors and cameras for live data gathering.
Process data through a cloud-based analytical pipeline.
Utilize machine learning algorithms to predict congestion levels.
Real-time dashboard displays traffic conditions and predicted congestion levels.
System enables proactive actions such as signal optimization, reducing travel times.
Framework promotes sustainable urban mobility and enhances road efficiency.

Abstract

The rapid urbanization and growing number of vehicles in cities have made traffic congestion a critical issue affecting productivity, energy consumption, and the environment. This paper presents the design and implementation of an Intelligent Real-Time Traffic Congestion Monitoring and Prediction System that integrates Machine Learning (ML) and the Internet of Things (IoT). The system gathers live traffic data using IoT sensors and cameras, processes it through a cloud-based analytical pipeline, and predicts congestion levels using ML algorithms. Results are visualized on a real-time dashboard to help authorities take proactive control actions such as signal optimization and route diversion. The framework aims to enhance road efficiency, reduce travel time, and promote sustainable urban mobility.

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