Integrating Bayesian Inference with BP-Transformer for Accurate Traffic Flow Prediction

Abstract

Nonlinear fluctuations and abrupt events in traffic flow can lead to significant point prediction errors, thereby limiting the effectiveness of traffic control strategies in Intelligent Transportation Systems (ITS). To address this issue, we propose a novel hybrid deep learning model, Bayes-BP-Transformer. This model combines Bayesian inference with backpropagation and a Transformer architecture, improving prediction accuracy while maintaining the model's generalization ability. Specifically, the backpropagation component captures local temporal patterns, while the Transformer encoder models long-range spatiotemporal dependencies. By introducing Bayesian inference, the model optimizes the probability distribution of network weights, improving robustness to noise and incomplete data. Experiments on two real-world traffic datasets demonstrate the superior performance of our model. Compared to advanced models, Bayes-BP-Transformer reduces the root mean square error (RMSE) by 18.6-38.0% and the mean absolute percentage error (MAPE) by 5.7-32