Urban Traffic Prediction Based on Deep Spatio-temporal Hybrid Graph Convolution
Due to the complex spatio-temporal correlations and nonlinear patterns in the traffic network,traffic prediction presents sub-stantial challenges.Existing methods primarily focus on the spatio-temporal features of road networks,separately modelingtemporal and spatial correlations to simulate spatio-temporal dependencies.As urban road networks continue to expand,existing models may lack the ability to fully exploit the spatial characteristics of road networks.Furthermore,the traffic operational state is influenced by external en-vironmental factors,leading to significant fluctuations in traffic flow due to segment transmission effects.To address these issues,a deep spatio-temporal hybrid graph convolution model is proposed.The residual connected graph neural network and graph attention network are used to aggregate the global and local information of the road network,respectively,thereby extending the receptive field of graph convolutions to enhance the extraction capability of spatial features.Inspired by the Transformer's success in long sequence prediction and to reduce computational complexity,the Informer model is introduced to handle the potential temporal dependencies in road network data.This achieves long-term prediction capabilities for traffic flow parameters and encodes external factors such as city weather and points of interest to enhance road network information attributes.To validate the performance of the proposed model,accu-racy and feasibility analyses are conducted on real-world datasets.Experimental results demonstrate that the deep spatio-temporal hy-brid graph convolution model achieves a peak accuracy of up to 75.1％,outperforming Transformer and Informer models by 2.5％and 2.3％respectively.It surpasses other baseline models across different prediction ranges,showcasing its long-term traffic prediction capabilities.
NETL
NSTL
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