Urban road traffic flow prediction: A graph convolutional network embedded with wavelet decomposition and attention mechanism

Yan Zheng, Shengyou Wang, Chunjiao Dong, Wenquan Li, Wen Zheng and Jingcai Yu

Physica A: Statistical Mechanics and its Applications, 2022, vol. 608, issue P1

Abstract: Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in the region. In this study, a graph convolutional network model framework embedded with wavelet decomposition and attention mechanism (WDA-GCN) is proposed to predict the traffic flow of each traffic monitor at the regional level by exploring the spatio-temporal correlation among traffic monitors. Specifically, the spatial correlation between different monitors is encoded into two graphs by Graph Convolutional Network (GCN): geographical neighbor graph and functional similarity graph. The Gated Recurrent Unit (GRU) is used to learn the spatial features extracted by GCN, and the attention mechanism is added to improve the prediction accuracy. Finally, the time series data and spatio-temporal correlation of traffic flow are input into the encoder–decoder based on GRU to realize regional traffic flow prediction. The model is validated and compared with the real traffic monitor data in Daxing District of Beijing, China, and the results show that the prediction accuracy of WDA-GCN model can reach 81.03% after embedding wavelet decomposition and attention mechanism, which is better than the traditional time series prediction methods and deep learning methods.

Keywords: Traffic flow prediction; Graph convolutional network; Gated recurrent unit; Deep learning (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437122008329
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:608:y:2022:i:p1:s0378437122008329

DOI: 10.1016/j.physa.2022.128274

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().