用图像化方法融合不同时段客流特征，提升地铁站短时预测精度。
Leveraging Intra-Period and Inter-Period Features for Enhanced Passenger Flow Prediction of Subway Stations
- 将时间序列转为二维矩阵，用CNN提取多时段特征
- 结合GNN处理站点间空间关系，误差低于现有方法
- 适合城市交通规划与智能调度系统参考
地铁站短时客流预测对人员调度至关重要。现有研究缺乏对不同时段特征（如时段内与时段间）的有效融合。本文提出多时段时空网络MPSTN，将一维时间序列按周期折叠为二维矩阵，使其具备图像结构，从而应用卷积神经网络（CNN）提取多时段时序特征，并结合图神经网络（GNN）建模站点间空间关联。在公开数据集上，该模型在多种先进方法中表现最优，预测误差最小。代码已开源：https://github.com/xiannanhuang/MPSTN。
原文摘要 · Abstract (English)
Accurate short-term passenger flow prediction of subway stations plays a vital role in enabling subway station personnel to proactively address changes in passenger volume. Despite existing literature in this field, there is a lack of research on effectively integrating features from different periods, particularly intra-period and inter-period features, for subway station passenger flow prediction. In this paper, we propose a novel model called \textbf{M}uti \textbf{P}eriod \textbf{S}patial \textbf{T}emporal \textbf{N}etwork \textbf{MPSTN}) that leverages features from different periods by transforming one-dimensional time series data into two-dimensional matrices based on periods. The folded matrices exhibit structural characteristics similar to images, enabling the utilization of image processing techniques, specifically convolutional neural networks (CNNs), to integrate features from different periods. Therefore, our MPSTN model incorporates a CNN module to extract temporal information from different periods and a graph neural network (GNN) module to integrate spatial information from different stations. We compared our approach with various state-of-the-art methods for spatiotemporal data prediction using a publicly available dataset and achieved minimal prediction errors. The code for our model is publicly available in the following repository: https://github.com/xiannanhuang/MPSTN
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