Multi-scale Anomaly Decomposition Graph Neural Network for High-Speed Rail Passenger Flow Forecasting

Lipeng Zhao, Weihao Qian, Shengxin Dai, Fei Chen, Lifan Liu, Mingjie Zhao, Kui Ye, Bing Guo, Yan Shen

Abstract: Traffic flow prediction plays a crucial role in the construction of smart cities. Although numerous models already exist for traffic flow prediction, they neither extracted the spatio-temporal features at different scales nor precisely considered the deviation between the traffic signals collected by the sensors and the trend signals at different scales. This leads to their inaccurate prediction results. To address the aforementioned issues, this paper proposes a multi-layer structure to extract spatio-temporal features at different scales and designs an information decomposition module to separate abnormal signals in traffic data. Furthermore, based on the above structure and modules, this paper constructs a new traffic flow prediction model Multi-scale Anomaly Decomposition Graph Neural Network (MADGNN) for feature extraction and information decomposition at different scales. Firstly, the model encodes the input data to fully capture spatio-temporal dependencies. Then, this paper extracts spatio-temporal features at multiple scales based on a multi-layer structure containing multiple GRUs and subtracts the learned abnormal information from the input signal to achieve abnormal signal decomposition. Finally, we use multiple spatio-temporal hidden states for further information extraction and traffic prediction. The final prediction result of the model is obtained by adding up the prediction outputs of each layer. The experimental results show that, compared with DDGCRN, on the Railway datasets, the MAE and RMSE metrics are improved by an average of 3.92% and 2.30% respectively, and on the public dataset PEMSD8, the MAE and MAPE metrics are improved by 2.92% and 2.23% respectively.

External IDs:doi:10.1109/jiot.2026.3677759