Traffic Flow Prediction Using Kalman Filter
Kalman filter-based traffic flow prediction implementation with detailed MATLAB code examples demonstrating state-space modeling and recursive estimation algorithms
Traffic Flow Prediction Using Genetic Algorithm-Optimized Wavelet Neural Networks (GA-WNN)
Urban traffic flow exhibits high complexity, time-varying characteristics, and randomness. Real-time accurate traffic flow prediction is crucial for intelligent transportation systems, particularly in advanced traffic management and traveler information systems. This paper presents a GA-WNN prediction model that combines genetic algorithms with wavelet neural networks. The genetic algorithm performs preliminary optimization of connection weights and scaling/translation parameters, overcoming limitations of gradient descent methods like local minima and oscillation effects. Simulation experiments validate GA-WNN's effectiveness for short-term traffic flow prediction.
Traffic Flow Prediction Using ARIMA Model
Traffic flow prediction based on ARIMA model demonstrating excellent forecasting performance with code implementation insights