Highway Load Forecasting Method Based on BP Neural Network and Monte Carlo Simulation

This study presents a novel approach to expressway load forecasting by integrating the BP neural network and Monte Carlo simulation techniques. Firstly, a comprehensive analysis is conducted to identify and classify different types of loads on highways into periodic and non-periodic categories based on their distinct characteristics. For periodic loads, the proposed algorithm utilizes a BP neural network-based model for accurate prediction. On the other hand, for non-periodic loads, a Monte Carlo simulation-based algorithm is employed for prediction purposes. To validate the effectiveness of the developed methodology, field investigations and data collection are carried out along the stretch from Yongdeng Service Area to Huining Service Area of G30 Lianhuo Expressway. The simulation results demonstrate the high accuracy of the proposed model in load prediction. In comparison with existing literature’s prediction methods, our approach proves to be more comprehensive and yields results that closely align with real-world observations.