基于注意力机制和分时图卷积的公交客流预测

实际公交路网通常为复杂的非线性时变系统,难以有效构建线路间的时空间依赖关系.因此,文中提出基于注意力机制和分时图卷积的公交客流预测模型,提升公交客流量预测的准确性.首先通过长短期记忆网络提取历史数据中的时间特征,并利用通道注意力模块加权特征.再使用分时图卷积方法分析不同时段下公交线路间的空间依赖性,根据预测时段选择不同的关系矩阵,通过图卷积的方式完成对非欧关系的建模.最后,融合提取的时空间特征与外部因素(天气、节假日信息等)的特征表示,得到最终的预测结果.在真实公交数据上的实验表明,文中模型可提升预测精度,加快学习速率.

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

1cvarying 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.