Traffic flow prediction over muti-sensor data correlation with graph convolution network

Accurate and real-time traffic flow prediction plays an important role in improving the traffic planning capability of intelligent traffic systems. However, traffic flow prediction is a very challenging problem because the spatial-temporal correlation among roads is complex and changeable. Most of the existing methods do not reasonably analyze the dynamic spatial-temporal correlation caused by the changing relationship of traffic patterns among roads, thus cannot get satisfactory results in the medium and long-term traffic prediction. To address these issues, a novel Multisensor Data Correlation Graph Convolution Network model, named MDCGCN, is proposed in this paper. The MDCGCN model consists of three parts: recent, daily period and weekly period components, and each of which consists of two parts: 1) benchmark adaptive mechanism and 2) multisensor data correlation convolution block. The first part can eliminate the differences among the periodic data and effectively improve the quality of data input. The second part can effectively capture the dynamic temporal and spatial correlation caused by the changing relationship of traffic patterns among roads. Through substantial experiments conducted on two real data sets, results indicate that the proposed MDCGCN model can significantly improve the medium and long-term prediction accuracy for traffic networks of different sizes, and is superior to existing prediction methods. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a novel Multisensor Data Correlation Graph Convolution Network model, named MDCGCN, to address the spatial-temporal correlation issues in traffic flow prediction. Experimental results show that the proposed model can significantly improve the medium and long-term prediction accuracy for traffic networks of different sizes, and outperforms existing prediction methods.