关键词: 城市交通, 地铁客流预测, 注意力机制, 双向长短时记忆, 时空关联性

Abstract: This paper proposes a Dynamic Spatio-Temporal Neural Network (DSTNN) model based on attention mechanism for urban rail transit station passenger flow forecast. The DSTNN adopts a multi-branch parallel architecture to effectively extract the complex spatio-temporal features of metro passenger flow. In the spatial dimension, the global and local attention mechanisms are combined to capture dynamic spatio-temporal correlation between stations and static topology. In the temporal dimension, the bi-directional long short-term memory and attention mechanisms are used to learn the time-varying patterns of passenger flow data. In the experiments on Hangzhou Metro dataset, the results show that the DSTNN has higher prediction accuracy and training efficiency compared to classical prediction models and deep learning models. The average mean absolute error (MAE) over four different prediction durations is respectively 6.63% and 2.57% lower than that of the Diffusion Convolutional Recurrent Neural Network (DCRNN) and Physical-Virtual Collaboration Graph Network (PVCGN). In addition, the visualization analysis demonstrates the dynamic learning ability of this model for spatio-temporal correlations, and the ablation experiments verified the effectiveness of each branch.

Key words: urban traffic, metro passenger flow prediction, attention mechanism, bidirectional long short-term memory, spatio-temporal correlation