Traffic Flow Prediction Using Kalman Filter

This document provides comprehensive Kalman filter-based traffic flow prediction code, designed to assist transportation planners in forecasting future traffic patterns for improved road infrastructure planning and traffic management. The Kalman filter algorithm, implemented through state-space modeling, processes historical traffic data to generate accurate predictions by combining system dynamics equations with measurement updates. The MATLAB implementation includes key components such as: - State transition matrix formulation for traffic flow dynamics - Measurement update functions incorporating sensor data - Covariance matrix propagation for uncertainty quantification - Recursive prediction-correction cycle implementation The provided code examples demonstrate practical applications including: - Time-series traffic data preprocessing and normalization - Parameter tuning for process and measurement noise matrices - Real-time prediction updates using filter gain calculations - Performance evaluation through mean squared error analysis This resource enables users to understand and apply this prediction methodology effectively, enhancing the accuracy and reliability of traffic forecasting systems. For researchers and practitioners interested in intelligent transportation systems, this documentation offers valuable insights into algorithmic implementation and practical deployment considerations.