Hybrid Model for Method for Short-Term Traffic Flow Prediction Based on Secondary Decomposition Technique and ELM

Abstract

Strong nonstationary and nonlinearity are the main characteristics in the short-term traffic flow data, which frustrates traditional methods (e.g., autoregressive integrated moving average and deep belief network) to provide a satisfactory prediction. To address the above problem, a novel forecasting method, which is composed of a secondary decomposition technique and extreme learning machine, is proposed in this study. This developed technique is a hybrid of time-varying filtering-empirical mode decomposition (TVF-EMD) and local mean decomposition (LMD), which not only can effectively handle the above complex data features by decomposing them into several regular subsets but also produce the smoother subseries that is beneficial to prediction. To verify the effectiveness of the proposed method, a case study based on two groups of actual traffic flow data with different characteristics is performed. Meanwhile, several single models and hybrid models based on the other decomposition methods (e.g., EMD and variational mode decomposition) are considered benchmark models. The experimental results reveal that the proposed model presents the best performance. For example, compared with the TVF-EMD-based method, the improvement by the proposed approach reaches 33.3% in terms of the evaluation criterion of mean absolute percentage error.

Short-Term Traffic Flow Forecasting Method Based on Secondary Decomposition and Conventional Neural Network–Transformer

“…Zhang [23] combined an adaptive secondary decomposition algorithm and a robust temporal convolutional network for short-term wind speed prediction. Zhao et al [24] applied a secondary decomposition technique and an ELM model for short-term traffic flow prediction. Hu [25] combined denoising schemes and an echo state network for short-term traffic flow forecasting.…”