Graph neural networks (GNNs) have been widely applied in traffic demand
prediction, and transportation modes can be divided into station-based mode and
free-floating traffic mode. Existing research in traffic graph construction
primarily relies on map matching to construct graphs based on the road network.
However, the complexity and inhomogeneity of data distribution in free-floating
traffic demand forecasting make road network matching inflexible. To tackle
these challenges, this paper introduces a novel graph construction method
tailored to free-floating traffic mode. We propose a novel density-based
clustering algorithm (HDPC-L) to determine the flexible positioning of nodes in
the graph, overcoming the computational bottlenecks of traditional clustering
algorithms and enabling effective handling of large-scale datasets.
Furthermore, we extract valuable information from ridership data to initialize
the edge weights of GNNs. Comprehensive experiments on two real-world datasets,
the Shenzhen bike-sharing dataset and the Haikou ride-hailing dataset, show
that the method significantly improves the performance of the model. On
average, our models show an improvement in accuracy of around 25\% and 19.5\%
on the two datasets. Additionally, it significantly enhances computational
efficiency, reducing training time by approximately 12% and 32.5% on the two
datasets. We make our code available at
this https URL

介绍了一种针对自由流动交通模式的新型图构建方法，利用密度聚类算法确定图中节点的灵活定位，并从乘车数据中提取有价值的信息来初始化图神经网络的边权重，实验证实了该方法显著提高了模型的性能和计算效率。

基于灵活节点的交通需求预测图构建

Graph Construction with Flexible Nodes for Traffic Demand Prediction

Graph clustering algorithms with autoencoder structures have recently gained
popularity due to their efficient performance and low training cost. However,
for existing graph autoencoder clustering algorithms based on GCN or GAT, not
only do they lack good generalization ability, but also the number of clusters
clustered by such autoencoder models is difficult to determine automatically.
To solve this problem, we propose a new framework called Graph Clustering with
Masked Autoencoders (GCMA). It employs our designed fusion autoencoder based on
the graph masking method for the fusion coding of graph. It introduces our
improved density-based clustering algorithm as a second decoder while decoding
with multi-target reconstruction. By decoding the mask embedding, our model can
capture more generalized and comprehensive knowledge. The number of clusters
and clustering results can be output end-to-end while improving the
generalization ability. As a nonparametric class method, extensive experiments
demonstrate the superiority of \textit{GCMA} over state-of-the-art baselines.

图聚类算法中的自编码器结构因其高效性能和低训练成本而近来受到关注。然而，对于基于 GCN 或 GAT 的现有图自编码器聚类算法来说，它们不仅缺乏良好的泛化能力，而且很难自动确定由这些自编码器模型聚类的群集数目。为解决这个问题，我们提出了一个称为 Graph Clustering with Masked Autoencoders (GCMA) 的新框架。它利用我们设计的基于图遮蔽方法的融合自编码器来进行图的融合编码。在解码过程中，它引入了我们改进的基于密度的聚类算法作为第二个解码器，并通过多目标重构进行解码。通过解码遮蔽嵌入，我们的模型能够捕捉到更广义和全面的知识。在提高泛化能力的同时，可以输出端到端的群集数目和聚类结果。作为一种非参数类方法，广泛的实验证明了 GCMA 相对于最先进的基准算法的优越性。