A novel road traffic flow prediction model using hybrid Particle Swarm Optimization (PSO) and Radial Basis Function Neural Network (RBFNN)

Traffic congestion is a major problem in urban areas, leading to increased travel time, air pollution, and fuel consumption. Road impedance function, which describes the relationship between traffic status and travel time, plays an important role in predicting travel time and managing traffic flow. Traditional methods for estimating road impedance function rely on manual calibration and may have limitations in reflecting the complexity of traffic patterns. To address these challenges, researchers have proposed various machine learning models for predicting travel time and road impedance function. In this paper, a hybrid particle swarm optimization—radial basis function neural network model is proposed for improving the accuracy of the road impedance function. The model takes into consideration various vehicle types and is validated using travel time data collected from a road section in Huai’an City, China. The effectiveness of the proposed model is compared with the traditional road impedance function calibrated by nonlinear regression. The experimental results indicate that the Mean Relative Error (MRE) of PSORBFNN is increased by 3.89% and 6.28% respectively when compared with DPNR training samples and validation samples. When compared with DPPSO training and validation samples, the MRE of PSORBFNN is increased by 2.87% and 3.3% respectively. These findings suggest that the proposed model could guide and assist traffic engineers and practitioners in predicting travel time on road sections with improved accuracy.