Large-scale data missing is a challenging problem in Intelligent Transportation Systems (ITS). Many studies have been carried out to impute large-scale traffic data by considering their spatiotemporal correlations at a network level. In existing traffic data imputations, however, rich semantic information of a road network has been largely ignored when capturing network-wide spatiotemporal correlations. This study proposes a Graph Transformer for Traffic Data Imputation (GT-TDI) model to impute large-scale traffic data with spatiotemporal semantic understanding of a road network. Specifically, the proposed model introduces semantic descriptions consisting of network-wide spatial and temporal information of traffic data to help the GT-TDI model capture spatiotemporal correlations at a network level. The proposed model takes incomplete data, the social connectivity of sensors, and semantic descriptions as input to perform imputation tasks with the help of Graph Neural Networks (GNN) and Transformer. On the PeMS freeway dataset, extensive experiments are conducted to compare the proposed GT-TDI model with conventional methods, tensor factorization methods, and deep learning-based methods. The results show that the proposed GT-TDI outperforms existing methods in complex missing patterns and diverse missing rates. The code of the GT-TDI model will be available at https://github.com/KP-Zhang/GT-TDI.
翻译:缺少大规模数据是智能运输系统(ITS)中一个具有挑战性的问题。许多研究都通过考虑网络一级的网络空间和时间相关性,对大规模交通数据进行估算。然而,在现有的交通数据估算中,在获取全网络范围的网络空间和时间相关性时,道路网络丰富的语义信息基本上被忽视。本研究提议了一种用于计算交通数据输入的图形变异器(GT-TDI)模型,用对公路网络的简易时间定义解析来对大型交通数据进行估算。具体地说,拟议的模型引入了包含全网络空间和时间通信数据信息的语义描述,以帮助GT-TDI模型在网络一级捕捉到网络上的广语义相关性。拟议的模型采用不完整的数据、传感器的社会连通性和语义描述,作为在图形神经网络(GNN)和变异体(GNN)的帮助下完成信号传输任务的投入。在PEMS Freeway数据集方面,将进行广泛的实验,将拟议的GT-TD模型与传统的空间空间和时间信息数据信息信息信息信息信息交换系统(TII)的空泛泛化方法,在网络中将显示现有的复杂数据化方法中显示缺失的学习方法,以缺失数据交换。