Traffic Congestion Prediction Employing IoT and Deep Learning

A traffic congestion prediction system that employs IoT and deep learning.

A traffic congestion prediction system that employs IoT and deep learning. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want to connect hardware to software: reading sensors, sending data to a back end, visualising it and acting on it with alerts or control.

The main parts are sensor or device data collection, a messaging layer between devices and the server, a live dashboard, and alerts or control actions. The reference build uses TensorFlow, ESP32, MQTT, Python, and Node-RED, with a clear project structure, setup instructions and sample data so that it can be run and extended.