Internet of Things Traffic Prediction with ARIMA and LSTM

📊 Project Overview

This project focuses on predicting traffic patterns using IoT data and advanced machine learning techniques, ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short-Term Memory). The goal is to forecast traffic conditions in real-time, providing valuable insights for urban planning, traffic management, and optimization.

🚗 Problem Statement

With the rapid advancement of IoT technology, real-time traffic monitoring can be achieved using sensors connected to IoT devices. In this project, we utilize 3 ESP32 devices and a Raspberry Pi as a gateway to collect and transmit traffic sensor data to a central system.

However, a significant challenge in IoT systems lies in predicting key metrics that affect network performance, such as latency, packet loss, throughput, and power consumption. These metrics are crucial for ensuring the efficiency and sustainability of IoT systems, especially in applications that require real-time communication.

By leveraging two predictive modeling approaches, ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short-Term Memory), this project aims to forecast the performance of the IoT network based on historical data. Accurate predictions of these metrics will provide valuable insights into connection quality, enabling optimized resource usage within the IoT system, specifically for traffic surveillance applications.

🧠 Model Approach

• ARIMA: A statistical model for time series forecasting, used to capture and predict traffic trends based on historical data.
• LSTM: A type of recurrent neural network (RNN) designed to handle time-series data, offering enhanced performance for long-term forecasting of traffic patterns.

⚙️ Key Features

• Real-time IoT data collection using sensors placed at strategic locations.
• Traffic prediction using ARIMA for short-term forecasting.
• Long-term traffic prediction using LSTM for more complex data patterns.
• A dynamic dashboard to visualize predicted traffic conditions.

🔧 Technologies Used

• IoT: Sensors, Raspberry Pi, ESP32
• Data Science: Python, ARIMA, LSTM
• Libraries: NumPy, Pandas, TensorFlow, Keras, Matplotlib and Statstools
• Hardware: Raspberry Pi 4B, DHT22 Sensor, PIR Sensor

🚀 Getting Started

• Clone the repository to your local machine.
• Install the required dependencies using pip install -r requirements.txt.
• Set up the IoT devices and collect real-time traffic data.
• Train the ARIMA and LSTM models with the collected data.
• Run the prediction scripts and monitor the traffic forecasts in real-time.

📈 Future Enhancements

• Integration with traffic control systems for automated responses.
• Real-time alert system for traffic jams or accidents.
• Expansion to predict other transportation-related metrics like speed or vehicle count.