Bus Passenger Flow Forecast Based on Attention and Time-Sharing Graph Convolutional Network

被引：0
|
作者：
Zhang W. [1 ,2 ]
Zhu F. [2 ,3 ]
Chen Y. [2 ]
Lü Y. [2 ]
机构：
[1] School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing
[2] State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing
[3] Cloud Computing Center, Chinese Academy of Sciences, Dongguan
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2021年 / 34卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Bus Passenger Flow Prediction; Channel-Wise Attention; Intelligent Transportation; Recurrent Neural Network; Time-Sharing Graph Convolution;
D O I：
10.16451/j.cnki.issn1003-6059.202102008
中图分类号：
学科分类号：
摘要：
Real bus network tends to be a complicated nonlinear time-varying system. Therefore, the spatiotemporal correlation between different bus lines can hardly be built effectively. To solve this problem, an attention and time-sharing graph convolution based long short-term memory network for bus passenger flow forecast is proposed. Firstly, temporal features of historical data are extracted by long short-term memory network(LSTM), and then they are weighted by a channel-wise attention module. A time-sharing graph convolution approach is utilized to analyze the spatial dependencies among bus lines. Different adjacent matrices are selected according to time intervals, and non-Euclidean pair-wise correlations are modeled via graph convolution. Finally, the final prediction result is obtained by integrating the extracted spatiotemporal features and vector representations of external factors, like weather and holiday information. Experiments on real bus passenger flow datasets indicate that the proposed model improves the prediction accuracy and learning speed evidently. © 2021, Science Press. All right reserved.
引用
收藏
页码：167 / 175
页数：8
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