A GATs-GAN framework for road traffic states forecasting

Abstract

Short-term traffic states forecasting of road networks based on real-time data is an important component of intelligent transportation systems, especially advanced traffic management systems and traveller information systems. By considering the influence of both space and time dimensions, we proposed a novel GATs-GAN framework for the forecasting of traffic states. First, to capture spatial traffic relationships, the traffic topological graph network is set up based on the connection of traffic sections. Then, the first-order neighbours and high-order neighbours of traffic networks can be structured. Graph attention networks (GATs) are used to obtain the hidden features of input traffic data by training the attention between nodes in high-order neighbours. Based on two traffic networks in California and Seattle in the United States, we find that the GATs-GAN with high-order neighbours can satisfactorily estimate the traffic data and performs better than the baseline methods and comparative experiments.

Keywords:

• Traffic data forecasting
• traffic graph network
• generative adversarial network
• graph attention network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://github.com/zhiyongc/Seattle-Loop-Data

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant (61903334), in part by the Zhejiang Provincial Natural Science Foundation under Grant (LY21F030016, LQ16E080011), in part by the China Postdoctoral Science Foundation under Grant (2018M632501), and in part by the Transportation Engineering of Beijing University of Technology (2020BJUT2T10).