Affiliation:
1. Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Faculty of Information Technology Beijing University of Technology Beijing China
2. Discipline of Business Analytics The University of Sydney Business School The University of Sydney New South Wales Australia
3. Faulty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China
Abstract
AbstractTraffic prediction is an important part of intelligent transportation system. Recently, graph convolution network (GCN) is introduced for traffic flow forecasting and achieves good performance due to its superiority of representing the graph traffic road structure network. Moreover, the dynamic GCN is put forward to model the temporal property of the traffic flow. Although great progress has been made, most GCN based traffic flow forecasting methods utilize a single graph for convolution, which is considered not enough to reveal the inherent property of traffic graph as it is influenced by many factors, for example weather, season and traffic accidents etc. In this paper, an exotic graph transformer based dynamic multiple graph convolution networks (GTDMGCN) is conceived for traffic flow forecasting. Instead of the single graph, multiple graphs are constructed to modulate the complex traffic network by the proposed graph transformer network. Additionally, a temporal gate convolution is proposed to get the temporal property of traffic flow. The proposed GTDMGCN model is evaluated on four real traffic datasets of PEMS03, PEMS04, PEMS07, PEMS08, and there are average increments of 9.78%, 7.80%, 5.96% under MAE, RMSE, and MAPE metrics compared with the current results.
Funder
Natural Science Foundation of Beijing Municipality
National Natural Science Foundation of China
Publisher
Institution of Engineering and Technology (IET)
Subject
Law,Mechanical Engineering,General Environmental Science,Transportation
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