A Spatio-Temporal Graph Neural Network Approach for Traffic Flow Prediction

Yanbing Li, Wei Zhao and Huilong Fan
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Yanbing Li: School of Cyber Science and Engineering, College of Information Science and Engineering, XinJiang University, Urmuqi 830046, China
Wei Zhao: School of Computer Science and Engineering, Central South University, Changsha 410075, China
Huilong Fan: School of Computer Science and Engineering, Central South University, Changsha 410075, China

Mathematics, 2022, vol. 10, issue 10, 1-14

Abstract: The accuracy of short-term traffic flow prediction is one of the important issues in the construction of smart cities, and it is an effective way to solve the problem of traffic congestion. Most previous studies could not effectively mine the potential relationship between the temporal and spatial dimensions of traffic data flow. Due to the large variability in the traffic flow data of road conditions, we analyzed it with “dynamic”, using a dynamic-aware graph neural network model for the hidden relationships between space-time in the deep learning segment. In this paper, we propose a dynamic perceptual graph neural network model for the temporal and spatial hidden relationships of deep learning segments. This model mixes temporal features and spatial features with graphs and expresses them. The temporal features and spatial features are connected to each other to learn potential relationships, so as to more accurately predict the traffic speed in the future time period, we performed experiments on real data sets and compared with some baseline models. The experiments show that the method proposed in this paper has certain advantages.

Keywords: traffic flow; deep learning; graph neural network; forecasting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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