Kalman Filter-Based Traffic Flow Analysis System

This system implements a Kalman filter-based approach for traffic flow analysis, capable of processing traffic data provided through .txt files to generate comprehensive reports and analytical results. The implementation involves reading traffic sensor data from text files, applying Kalman filtering algorithms for noise reduction and state estimation, and generating traffic pattern analysis. Users can select different analysis modes through a configurable interface, enabling detailed examination of various traffic aspects including road bottlenecks, vehicle density metrics, and congestion patterns. The system architecture incorporates real-time monitoring capabilities through continuous data stream processing, allowing users to track traffic conditions dynamically and implement timely interventions. The core algorithm utilizes state-space modeling where the Kalman filter recursively predicts and corrects traffic flow states, minimizing estimation errors through optimal weighting of predictions and measurements. Key functions include data preprocessing, Kalman gain calculation, and covariance matrix updates. This makes the system a valuable traffic analysis tool that assists transportation authorities in urban traffic management and planning optimization.