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首页> 外文期刊>Transportation research >Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach
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Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach

机译：预测大型自行车共享网络中车站级别的小时需求：一种图形卷积神经网络方法

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摘要

This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between 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, and GCNNrec-DDGF additionally contains a recurrent block from the Long Short-term Memory neural network architecture to capture temporal dependencies in the bike-sharing demand series. Furthermore, four types of 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 types of GCNN models and seven other benchmark models are built and compared on a 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. Through a more detailed graph network analysis based on the learned DDGF, insights are obtained on the “black box” of the GCNN-DDGF model. It is found to capture some information similar to details embedded in the SD, DE and DC matrices. More importantly, it also uncovers hidden heterogeneous pairwise correlations between stations that are not revealed by any of those matrices.
机译：这项研究提出了一种新颖的带有数据驱动图滤波器的图卷积神经网络（GCNN-DDGF）模型，该模型可以学习车站之间隐藏的异构成对相关性，以预测大型自行车共享网络中车站级别的小时需求。探索了GCNN-DDGF模型的两种架构； GCNNreg-DDGF是包含卷积和前馈块的常规GCNN-DDGF模型，而GCNNrec-DDGF还包含来自Long Short-term Memory神经网络架构的循环块，以捕获自行车共享需求序列中的时间依赖性。此外，提出了四种类型的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），平均旅行持续时间矩阵（ATD）和需求相关矩阵（DC） ）。在纽约市的Citi Bike数据集上构建并比较了这六种类型的GCNN模型和其他七个基准模型，该数据集包含2013年至2016年的272个站点和超过2800万笔交易。结果显示，GCNNrec-DDGF的表现最佳均方根误差，平均绝对误差和确定系数（R2），然后是GCNNreg-DDGF。它们优于其他模型。通过基于学习到的DDGF的更详细的图网络分析，可以在GCNN-DDGF模型的“黑匣子”上获得见解。发现它捕获了一些类似于嵌入在SD，DE和DC矩阵中的详细信息的信息。更重要的是，它还揭示了任何这些矩阵都没有揭示的站点之间隐藏的异构成对关联。

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