论文标题
用于城市铁路运输中短期乘客流量预测的多画卷积网络
Multi-graph convolutional network for short-term passenger flow forecasting in urban rail transit
论文作者
论文摘要
短期乘客流量预测是城市铁路运输行动的关键任务。新兴的深度学习技术已成为克服该问题的有效方法。在这项研究中，作者提出了一个名为GCN的深度学习结构，该结构结合了图形卷积网络（GCN）和三维（3D）卷积神经网络（3D CNN）。首先，他们介绍了一个多段GCN，以分别处理三种流入和流出模式（近期，每日和每周）。多毛牌GCN网络可以捕获整个网络中的时空相关性和拓扑信息。然后将3D CNN应用于深度整合流入和流出信息。不同的流入和流出模式之间以及附近和遥远的站点之间的高级时空特征可以用3D CNN提取。最后，完全连接的层用于输出结果。在10、15和30分钟的时间间隔内，对北京地铁的智能卡数据进行了Cons-GCN模型。结果表明，与其他七个模型相比，该模型可以产生最佳性能。就根平方错误而言，在三个时间间隔下的性能分别提高了9.402％，7.756和9.256％。这项研究可以为地铁运营商优化城市铁路运输运营提供关键见解。
Short-term passenger flow forecasting is a crucial task for urban rail transit operations. Emerging deep-learning technologies have become effective methods used to overcome this problem. In this study, the authors propose a deep-learning architecture called Conv-GCN that combines a graph convolutional network (GCN) and a three-dimensional (3D) convolutional neural network (3D CNN). First, they introduce a multi-graph GCN to deal with three inflow and outflow patterns (recent, daily, and weekly) separately. Multi-graph GCN networks can capture spatiotemporal correlations and topological information within the entire network. A 3D CNN is then applied to deeply integrate the inflow and outflow information. High-level spatiotemporal features between different inflow and outflow patterns and between stations that are nearby and far away can be extracted by 3D CNN. Finally, a fully connected layer is used to output results. The Conv-GCN model is evaluated on smart card data of the Beijing subway under the time interval of 10, 15, and 30 min. Results show that this model yields the best performance compared with seven other models. In terms of the root-mean-square errors, the performances under three time intervals have been improved by 9.402, 7.756, and 9.256%, respectively. This study can provide critical insights for subway operators to optimise urban rail transit operations.
