IoT-Based Traffic Prediction and Management System for Smart Cities Using RASPBERRY Pi

Abstract

Urbanization and rapid vehicle growth have intensified congestion challenges in modern cities, making traditional fixed-schedule traffic control systems inefficient for dynamic traffic conditions. This project presents an IoT-based traffic prediction and management framework that integrates real- time sensing, cloud analytics, and deep learning for adaptive signal control in smart city environments.The proposed system architecture comprises four layers — Perception, Network, Processing, and Application — enabling seamless data flow from IoT sensors to decision-making systems. IoT devices such as cameras, GPS modules, and RF sensor nodes collect traffic and environmental data, transmitted via MQTT protocols to the cloud for processing. A Long Short- Term Memory (LSTM) deep learning model predicts congestion levels by learning temporal traffic dependencies, achieving a prediction accuracy of 96.4%, RMSE of 0.298, and MAE of 0.213. Key hardware components include the Raspberry Pi as the central processing unit, USB webcam for realtime video capture and vehicle detection via OpenCV, RF communication modules with HT12E encoder for wireless data transmission, LEDs for status indication, LCD display for local output, and a regulated power supply. The software stack includes Python, OpenCV for image processing, and TensorFlow/Keras for the LSTM model. Based on LSTM predictions, an adaptive traffic signal algorithm dynamically adjusts signal durations, reducing vehicle waiting time by 32% and fuel consumption by 18%. The system demonstrates scalability, energy efficiency, and real-time adaptability, offering a robust and cost- effective solution for intelligent urban traffic management in smart cities.