TFGAN: Traffic forecasting using generative adversarial network with multi-graph convolutional network

doi:10.1016/j.knosys.2022.108990

2022-08-01

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DOI：10.1016/j.knosys.2022.108990

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Traffic forecasting constitutes a task of great importance in intelligent transport systems. Owing to the non-Euclidean structure of traffic data, the complicated spatial correlations, and the dynamic temporal dependencies, it is challenging to predict traffic accurately. Despite the fact that few prior studies have considered the interconnections between multiple traffic nodes at the same timestep, the majority of studies fail to capture the dependencies among multiple nodes at different timesteps. Furthermore, most existing work generates shallow graphs based solely on the distance between traffic nodes, which limits their representation competence and declines their power in capturing complex correlations. In particular, inspired by the recent breakthroughs in the generative adversarial network (GAN) and the power of the graph convolution network (GCN) in handling non-Euclidean data, this paper puts forward an adversarial multi-graph convolutional neural network model, named TFGAN, to address the abovementioned problems. We integrate the unsupervised model elasticity with the supervision provided by supervised training to help the GAN generator model generates accurate traffic predictions. To improve the representation and model the implicit correlations effectively, multiple GCNs are constructed within the generator based on various perspectives, such as similarity, correlation, and spatial distance. Meanwhile, GRU and self-attention are applied after each graph to capture the dynamic temporal dependencies across nodes. The comprehensive experiments on three different traffic variables (traffic flow, speed, and travel time) using six real-world traffic datasets demonstrate that TFGAN outperforms the related state-of-the-art models and achieves significant results. (C) 2022 Elsevier B.V. All rights reserved.

Keyword:

Traffic forecasting

Multivariate time series

Generative adversarial network

Graph convolution network