期刊
INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
卷 29, 期 11-12, 页码 1727-1740出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218194019400187
关键词
Car-sharing systems; multistepflow prediction; graph convolution network
类别
资金
- National Key R&D Program of China [2018YFB0505000]
- Shanghai Committee of Science and Technology [17511107303, 17511110202]
Multistep flow prediction is an essential task for the car-sharing systems. An accurate flow prediction model can help system operators to pre-allocate the cars to meet the demand of users. However, this task is challenging due to the complex spatial and temporal relations among stations. Existing works only considered temporal relations (e.g. using LSTM) or spatial relations (e.g. using CNN) independently. In this paper, we propose an attention to multi-graph convolutional sequence-to-sequence model (AMGC-Seq2Seq), which is a novel deep learning model for multistep flow prediction. The proposed model uses the encoder-decoder architecture, wherein the encoder part, spatial and temporal relations are encoded simultaneously. Then the encoded information is passed to the decoder to generate multistep outputs. In this work, specific multiple graphs are constructed to reflect spatial relations from different aspects, and we model them by using the proposed multi-graph convolution. Attention mechanism is also used to capture the important relations from previous information. Experiments on a large-scale real-world car-sharing dataset demonstrate the effectiveness of our approach over state-of-the-art methods.
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