论文标题
使用图形卷积神经网络预测电台级别的自行车共享需求
Predicting Station-Level Bike-Sharing Demands Using Graph Convolutional Neural Network
论文作者
论文摘要
这项研究提出了一个具有数据驱动图滤波器（GCNN-DDGF）模型的新型图形卷积神经网络，该模型可以学习站点之间隐藏的异质成对相关性，以预测大型自行车共享网络中站级的小时需求。探索了GCNN-DDGF模型的两个体系结构：GCNNREG-DDGF是一个常规的GCNN-DDGF模型，其中包含卷积和前馈块； GCNNREC-DDGF还包含从长期短期内存神经网络中的复发块，以捕获自行车共享需求序列中的时间依赖性。此外，提出了四个GCNN模型，其邻接矩阵基于各种自行车共享系统数据，包括空间距离矩阵（SD），需求矩阵（DE），平均行程持续时间矩阵（ATD）和需求相关矩阵（DC）。这六个GCNN模型以及其他七个基准模型还使用来自纽约市的花旗自行车数据集建立和比较，其中包括272个站点和超过2800万台从2013年到2016年的交易。结果表明，GCNNREC-DDGF在根平方误差，平均绝对误差和确定系数（r2）中，在根平方误差方面表现出最佳状态。他们的表现胜过其他模型。
This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations among stations to predict station-level hourly demand in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model are explored: GCNNreg-DDGF is a regular GCNN-DDGF model which contains the convolution and feedforward blocks; GCNNrec-DDGF additionally contains a recurrent block from the Long Short-term Memory neural network to capture temporal dependencies in bike-sharing demand series. Furthermore, four GCNN models are proposed whose adjacency matrices are based on various bike-sharing system data, including Spatial Distance matrix (SD), Demand matrix (DE), Average Trip Duration matrix (ATD), and Demand Correlation matrix (DC). These six GCNN models along with seven other benchmark models are built and compared using the Citi Bike dataset from New York City, which includes 272 stations and over 28 million transactions from 2013 to 2016. Results show that the GCNNrec-DDGF performs the best in terms of the Root Mean Square Error, the Mean Absolute Error, and the coefficient of determination (R2), followed by the GCNNreg-DDGF. They outperform the other models.
