Program Implementation of Short-Term Traffic Flow Prediction Using Kalman Method

This article presents a program implementation for short-term traffic flow prediction using the Kalman method. During peak hours such as traffic congestion periods, accurate traffic flow prediction becomes crucial as it enables traffic management departments to better plan road networks and improve urban traffic circulation. The Kalman method, a mathematical algorithm widely applied in prediction systems, helps enhance the accuracy of traffic flow forecasting. In this implementation, we utilize the Kalman filter to predict short-term traffic flow by analyzing both historical datasets and real-time data inputs. The core algorithm involves two main phases: prediction and update. The prediction phase estimates the next state based on the system model, while the update phase corrects these estimates using actual measurements. Key functions include state transition matrix initialization, covariance computation, and Kalman gain optimization. Through this Kalman-based implementation for short-term traffic flow prediction, we can achieve more effective urban traffic planning and create a more convenient and efficient travel environment for citizens.