文献类型：期刊文献

英文题名：Parallel multi-graph convolution network for metro passenger volume prediction

作者：Gao, Fuchen[1]; Wang, Zhanquan[1]; Liu, Zhenguang[2]

机构：[1] College of Information Science and Engineering, East China University of Science and Technology, Shanghai, China; [2] College of Computer Science and Technology, Zhejiang University, Zhejiang, China

年份：2021

外文期刊名：arXiv

收录：EI(收录号：20210318501)

语种：英文

外文关键词：Complex networks - Convolutional neural networks - Deep learning - Flow graphs - Flow patterns - Forecasting - Intelligent systems - Subway stations

摘要：Accurate prediction of metro passenger volume (number of passengers) is valuable to realize real-time metro system management, which is a pivotal yet challenging task in intelligent transportation. Due to the complex spatial correlation and temporal variation of urban subway ridership behavior, deep learning has been widely used to capture non-linear spatial-temporal dependencies. Unfortunately, the current deep learning methods only adopt graph convolutional network as a component to model spatial relationship, without making full use of the different spatial correlation patterns between stations. In order to further improve the accuracy of metro passenger volume prediction, a deep learning model composed of Parallel multi-graph convolution and stacked Bidirectional unidirectional Gated Recurrent Unit (PB-GRU) was proposed in this paper. The parallel multi-graph convolution captures the origin-destination (OD) distribution and similar flow pattern between the metro stations, while bidirectional gated recurrent unit considers the passenger volume sequence in forward and backward directions and learns complex temporal features. Extensive experiments on two real-world datasets of subway passenger flow show the efficacy of the model. Surprisingly, compared with the existing methods, PB-GRU achieves much lower prediction error. Copyright ? 2021, The Authors. All rights reserved.