Abstract: To achieve accurate prediction of highway station traffic flow, this paper proposes a research method that utilizes a combined model to capture the spatio-temporal characteristics of highway toll station traffic. The basic idea is to mine the toll data to obtain a spatio-temporal dataset of traffic flow, analyze its spatio-temporal characteristics, reveal the spatio-temporal evolution rules and correlation mechanisms between the traffic flow of highway toll stations. Subsequently, we combine these insights with deep learning models to predict highway traffic flow. As a case study, we focus on the main toll station in Jiujiang, Jiangxi Province, utilizing the toll data from May 1, 2021, to December 31, 2021. The extracted spatio-temporal traffic data serves as input for our model, yielding analysis and prediction results of outlet flow. The prediction performance of the model is evaluated through three indicators: mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The results show that the proposed model effectively improves the prediction accuracy by utilizing spatio-temporal characteristics, outperforming single models in predictive capability.

Key words: toll station traffic prediction, graph attention networks, long short-term neural network, spatio-temporal characteristics