Spatio-temporal multi-graph convolutional network based on wavelet analysis for vehicle speed prediction

Abstract

The research on theoretical techniques and practical applications of vehicle speed prediction holds significant importance in mitigating the increasingly pressing traffic issues and enhancing the operational efficiency of urban transportation systems. This paper aims to improve the accuracy of traffic speed prediction. To achieve this, we have devised a multi-graph convolutional neural network (GCN) framework that utilizes data from three different patterns: recent, daily, and weekly, enabling the extraction of distinct time-related features. Through the integration of multiple GCNs and 3D convolutional neural networks (3DCNN), we have obtained richer spatiotemporal feature representations. Additionally, we have applied wavelet analysis to process the traffic speed data from each node in the road network, unearthing hidden information and eliminating noise interference. Our experiments have demonstrated that the model employing wavelet analysis outperforms the baseline model without wavelet analysis, thus enhancing the prediction accuracy. In conclusion, we propose a wavelet-based spatiotemporal multi-graph convolutional neural network for traffic speed prediction. Our experimental results validate the superiority of this model across different time intervals and real-world datasets, showcasing its advantages in terms of prediction accuracy and robustness. As a result, our approach offers an effective and viable solution for traffic speed prediction tasks.