GSAA: A Novel Graph Spatiotemporal Attention Algorithm for Smart City Traffic Prediction

Abstract

With the development of 5G and Internet of Things technologies, the application process of smart transportation in smart cities continues to advance. Sensors are a key source of information for smart transportation, and their data commonly includes complicated traffic scene information. Urban traffic scheduling and efficiency can be significantly increased by deploying data from smart sensors to forecast traffic flows. Despite the fact that some related works have focused on the prediction task of traffic flows, they have not completely mined the traffic spatiotemporal information present in smart sensor data. We offer a novel graph spatio-temporal attention algorithm (GSAA) for traffic prediction in this paper. To completely exploit the geographical and temporal correlations among complicated roadways for traffic forecast, the algorithm combines a spatiotemporal attention mechanism with a graph neural network.To take full advantage of how much effect various hyperparameters provide, deep reinforcement learning is used to obtain the optimal hyperparameters while the predictive model is trained. Experimental results on real-world public datasets show that the algorithm proposed in this paper achieves performance improvements of about 5.47% and 13.10% over the MAE (mean absolute error) than the best baseline strategies for short-term and long-term traffic forecasting, respectively.