Traffic prediction based on auto spatiotemporal Multi-graph Adversarial Neural Network

Jun Wang, Wenjun Wang, Xueli Liu, Wei Yu, Xiaoming Li and Peiliang Sun

Physica A: Statistical Mechanics and its Applications, 2022, vol. 590, issue C

Abstract: Traffic prediction plays an essential role in the intelligent transportation systems and has broad applications in transportation management and planning. And the key to this field is to explore the spatiotemporal information of traffic data, synchronously. Recently, various deep learning methods, such as Convolution Neural Network (CNN) and Graph Convolutional Network (GCN), have shown promising performance in traffic prediction. However, these methods cannot automatically model spatial dependencies and dynamic spatiotemporal States, and there are no constraints on the distribution of outputs. To solve the above problems, in this paper, a method of automatically obtaining spatiotemporal dependence in data, which can automatically obtain the spatiotemporal state and spatiotemporal dependency using Multi-graph Adversarial Neural Network (GAN) and is named AST-MAGCN, is proposed. The new method AST-MAGCN combines GAN and GCN, extracts spatiotemporal state of the data in real-time, and outputs the traffic forecast by GAN constraint. Lastly, the proposed method is evaluated on two real-world traffic datasets, and the experimental results show that the proposed method outperforms baseline traffic prediction methods.

Keywords: Traffic prediction; Spatiotemporal state; CNN; GCN (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:590:y:2022:i:c:s0378437121009407

DOI: 10.1016/j.physa.2021.126736

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