论文标题
HYPERGST：预测Metro乘客流程，包括图形，超图，社交范围的边缘重量和时间剥削
Hyper-GST: Predict Metro Passenger Flow Incorporating GraphSAGE, Hypergraph, Social-meaningful Edge Weights and Temporal Exploitation
论文作者
论文摘要
准确预测地铁乘客流程对于动态交通计划至关重要。深度学习算法由于其在非线性系统建模中的稳健性能而被广泛应用。但是，传统的深度学习算法完全丢弃了地铁系统中固有的图形结构。基于图的深度学习算法可以利用图形结构，但提出了一些挑战，例如如何确定边缘的权重和由过度平滑的问题引起的浅层接受场。为了进一步改善这些挑战，本研究提出了一个基于Edge Weights学习者的图形模型。边缘权重学习者利用社会意义的特征来产生边缘权重。超图和时间剥削模块也被构造为附加组件，以提高性能。对拟议的算法和其他最先进的图形神经网络进行了比较研究，该算法可以改善性能。
Predicting metro passenger flow precisely is of great importance for dynamic traffic planning. Deep learning algorithms have been widely applied due to their robust performance in modelling non-linear systems. However, traditional deep learning algorithms completely discard the inherent graph structure within the metro system. Graph-based deep learning algorithms could utilise the graph structure but raise a few challenges, such as how to determine the weights of the edges and the shallow receptive field caused by the over-smoothing issue. To further improve these challenges, this study proposes a model based on GraphSAGE with an edge weights learner applied. The edge weights learner utilises socially meaningful features to generate edge weights. Hypergraph and temporal exploitation modules are also constructed as add-ons for better performance. A comparison study is conducted on the proposed algorithm and other state-of-art graph neural networks, where the proposed algorithm could improve the performance.
