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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Spatiotemporal Graph Convolution Multifusion Network for Urban Vehicle Emission Prediction
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Spatiotemporal Graph Convolution Multifusion Network for Urban Vehicle Emission Prediction

机译：城市车辆排放预测的时空图卷积多化网络

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摘要

Urban vehicle emission prediction can help the regulation of vehicle pollution and traffic control. However, it is hard to predict the spatiotemporal variation of vehicle emission because of the spatial interactions and temporal correlations between different road segments as well as the high nonlinearity and complexity of vehicle emission variation. The existing methods solve the problem by splitting the region into standard segments or grids based on conventional deep learning methods, without considering that urban vehicle emission varies by graph-structured traffic road network and depends on many complex external environment factors. To address these issues, a spatiotemporal graph convolution multifusion network (ST-MFGCN) is proposed to leverage the graph structural properties as the inherent connectivity of road network for urban vehicle emission prediction, which can capture the vehicle emission spatiotemporal variation patterns and learn the effects of complex environmental factors. The proposed model consists of three parts: 1) a spatiotemporal graph convolution module to capture spatiotemporal dependencies by merging closeness, period, and trend sequences with temporal convolution as well as graph convolution is introduced to model the spatial dependencies; 2) an external factor component to divide multisource external factors into global and individual external features; and 3) a general fusion component to merge the spatiotemporal patterns and the external features as well as fit the mutation of emission measurement data by multifusion strategy. Finally, the proposed model is evaluated on the practical monitoring data of vehicle emission data in Hefei, and the results demonstrate that our proposed model can predict regional vehicle emissions effectively.
机译：城市车辆排放预测有助于对车辆污染和交通管制的调节。然而，由于不同的道路段之间的空间相互作用和时间相关性以及车辆发射变化的高非线性和复杂性，难以预测车辆发射的时空变化。现有方法通过基于传统的深度学习方法将区域分成标准段或网格来解决问题，而不考虑城市车辆发射因图形结构的交通路网络而变化，并且取决于许多复杂的外部环境因素。为了解决这些问题，提出了一种时空图卷积多化网络（ST-MFGCN），以利用图形结构特性作为城市车辆排放预测的道路网络的固有连接，这可以捕获车辆发射时空变化模式并学习效果复杂的环境因素。所提出的模型由三部分组成：1）时空图卷积模块通过合并了与时间卷积的闭合，时段和趋势序列来捕获时空依赖性以及图形卷积以模拟空间依赖性; 2）将Multisource外部因素分为全局和单个外部特征的外部因子组件; 3）通用融合组分以合并时空图案和外部特征，以及通过多化策略拟合发射测量数据的突变。最后，在合肥中的车辆发射数据的实际监测数据中评估了所提出的模型，结果表明我们所提出的模型可以有效地预测区域车辆排放。

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