论文标题
AST-GIN：属性增强的时空图形信息网络网络，用于电动汽车充电站的可用性预测
AST-GIN: Attribute-Augmented Spatial-Temporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting
论文作者
论文摘要
电动汽车（EV）充电需求和充电站的可用性预测是智能运输系统中的挑战之一。通过准确的EV站情况预测，可以提前安排合适的充电行为以减轻范围焦虑。但是，由于复杂的道路网络结构和全面的外部因素，例如兴趣点（POI）和天气效应，因此提出了许多现有的深度学习方法来解决这个问题，因此，许多常用的算法只能提取历史用法信息而不考虑外部因素的全面影响。为了提高预测准确性和解释性，在本研究中提出了属性增强的时空图信息器（AST-GIN）结构，通过组合图形卷积网络（GCN）层和告密者层来提取相关运输数据的外部和内部空间 - 周期依赖性。并且外部因素通过属性调制的编码器将其建模为动态属性。测试了邓迪市收集的数据的AST-GIN模型，实验结果表明，与其他基层相比，考虑到外部因素对各种地平线环境的影响，我们的模型的有效性。
Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With the accurate EV station situation prediction, suitable charging behaviors could be scheduled in advance to relieve range anxiety. Many existing deep learning methods are proposed to address this issue, however, due to the complex road network structure and comprehensive external factors, such as point of interests (POIs) and weather effects, many commonly used algorithms could just extract the historical usage information without considering comprehensive influence of external factors. To enhance the prediction accuracy and interpretability, the Attribute-Augmented Spatial-Temporal Graph Informer (AST-GIN) structure is proposed in this study by combining the Graph Convolutional Network (GCN) layer and the Informer layer to extract both external and internal spatial-temporal dependence of relevant transportation data. And the external factors are modeled as dynamic attributes by the attribute-augmented encoder for training. AST-GIN model is tested on the data collected in Dundee City and experimental results show the effectiveness of our model considering external factors influence over various horizon settings compared with other baselines.
