Enhancing Expressway Traffic State Perception: A Novel BAS-Optimized PSO-BP Fusion Model with Tensor Completion

With the continuous expansion of the expressway network and the rapid growth of traffic demand, traditional single-source traffic detection data is limited in spatial-temporal coverage and accuracy, which can hardly support the refined operation and management of intelligent expressways. Existing data preprocessing methods often fail to fully capture global spatiotemporal features, and traditional PSO-BP neural networks are prone to local optima. To address these issues, this study investigates multi-source traffic data fusion using ETC-DSRC and RTMS microwave data from the Jiangsu section of the G50 Shanghai-Chongqing Expressway. The HaLRTC tensor completion algorithm is adopted to repair missing and abnormal data, fully mining the spatial-temporal correlation characteristics of traffic flow. The beetle antennae search (BAS) mechanism is introduced into the particle swarm optimization (PSO) process to improve particle search behavior and population diversity. On this basis, a BAS-optimized PSO-BP neural network, referred to as BSO-BP in this study, is constructed for multi-source traffic data fusion. In this model, the improved PSO algorithm is used to optimize the initial weights and thresholds of the backpropagation (BP) neural network, thereby improving the global search capability and convergence stability of the fusion model. Taking the average road speed as the fusion target, MAE, RMSE and MAPE are used for accuracy verification. The results show that the proposed model has significantly higher accuracy than single-source data methods and BP, PSO-BP, and GA-PSO-BP models, and can reflect the real traffic state of road sections more accurately.