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.

中文翻译：

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共享汽车系统的多步流预测：具有注意机制的多图卷积神经网络

多步流量预测是汽车共享系统的一项基本任务。准确的流量预测模型可以帮助系统运营商预先分配汽车以满足用户的需求。然而，由于车站之间复杂的时空关系，这项任务具有挑战性。现有作品仅独立考虑时间关系（例如使用 LSTM）或空间关系（例如使用 CNN）。在本文中，我们提出了对多图卷积序列到序列模型（AMGC-Seq2Seq）的关注，这是一种用于多步流预测的新型深度学习模型。所提出的模型使用编码器-解码器架构，其中编码器部分、空间和时间关系被同时编码。然后将编码信息传递给解码器以生成多步输出。在这项工作中，构建特定的多图以从不同方面反映空间关系，并使用提出的多图卷积对其进行建模。注意力机制也被用来从先前的信息中捕捉重要的关系。在大规模现实世界汽车共享数据集上的实验证明了我们的方法优于最先进的方法。