Smart Traffic Flow Prediction with IoT and ML

Objectives to cover:

• Introduction
  Introduces the concept of using IoT sensor data and machine learning for real-time traffic prediction.

• Background and Motivation
  Explains rising traffic congestion and the need for intelligent transportation systems.

• IoT in Intelligent Transportation
  Describes how IoT devices collect live traffic data for analysis.

• Machine Learning Approaches
  Discusses ML models that identify patterns and predict traffic flow.

• System Architecture & Data Flow
  Outlines the integration of IoT sensors, data pipelines, and ML prediction modules.

• Data Preprocessing & Feature Engineering
  Covers cleaning sensor data and extracting key features like speed, vehicle count, and time.

• Model Training & Real-Time Prediction
  Explains ML algorithm selection, training, and deployment for live forecasting.

• Evaluation & Challenges
  Highlights performance metrics (accuracy, RMSE, latency) and challenges like noisy data or scalability.

• Conclusion & Future Work
  Summarizes contributions and suggests future improvements with advanced ML and IoT integration.

Reference:  IEEE Style