Research on Spatio-temporal Traffic Data Prediction Method Based on Generative Adversarial Network
Traffic flow prediction represents a crucial research avenue within the domain of spatio-temporal data mining.How-ever,prevailing predictions fall short of expectations due to the highly nonlinear and intricate nature of traffic patterns.Exist-ing methodologies often overlook the significance of capturing spatial-temporal dynamics and integrating external environmental variables.A novel approach for traffic flow prediction termed as the spatial-temporal graph traffic flow prediction method,le-veraging generative adversarial network(GAN).This approach entails unsupervised learning of spatial-temporal graph feature representations to facilitate more precise spatial-temporal sequence graph prediction.Utilizing the GAN framework,the method engages in game-based learning to model data distributions and employs variational inference.Extensive experimentation on Chinese Taiwan's high-speed traffic dataset validates the efficacy of the proposed method,demonstrating its superior capability in addressing the issue of prediction result smoothing compared to baseline approaches,obtaining more efficient and accurate forecasting results.
GANtraffic flowspatial-temporal graphgeneration of graph structure
万方数据
维普
